Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of ChatGPT for NLP-based Mental Health Applications

Large language models (LLM) have been successful in several natural language understanding tasks and could be relevant for natural language processing (NLP)-based mental health application research. In this work, we report the performance of LLM-based ChatGPT (with gpt-3.5-turbo backend) in three text-based mental health classification tasks: stress detection (2-class classification), depression detection (2-class classification), and suicidality detection (5-class classification). We obtained annotated social media posts for the three classification tasks from public datasets. Then ChatGPT API classified the social media posts with an input prompt for classification. We obtained F1 scores of 0.73, 0.86, and 0.37 for stress detection, depression detection, and suicidality detection, respectively. A baseline model that always predicted the dominant class resulted in F1 scores of 0.35, 0.60, and 0.19. The zero-shot classification accuracy obtained with ChatGPT indicates a potential use of language models for mental health classification tasks.
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing

The field of mobile and wearable computing is undergoing a revolutionary integration of machine learning. Devices can now diagnose diseases, predict heart irregularities, and unlock the full potential of human cognition. However, the underlying algorithms powering these predictions are not immune to biases with respect to sensitive attributes (e.g., gender, race), leading to discriminatory outcomes. The goal of this work is to explore the extent to which the mobile and wearable computing community has adopted ways of reporting information about datasets and models to surface and, eventually, counter biases. Our systematic review of papers published in the Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) journal from 2018-2022 indicates that, while there has been progress made on algorithmic fairness, there is still ample room for growth. Our findings show that only a small portion (5%) of published papers adheres to modern fairness reporting, while the overwhelming majority thereof focuses on accuracy or error metrics. To generalize these results across venues of similar scope, we analyzed recent proceedings of ACM MobiCom, MobiSys, and SenSys, IEEE Pervasive, and IEEE Transactions on Mobile Computing Computing, and found no deviation from our primary result. In light of these findings, our work provides practical guidelines for the design and development of mobile and wearable technologies that not only strive for accuracy but also fairness.
Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised Learning

In this work, we adopt Wyner common information framework for unsupervised multi-view representation learning. Within this framework, we propose two novel formulations that enable the development of computational efficient solvers based on the alternating minimization principle. The first formulation, referred to as the variational form, enjoys a linearly growing complexity with the number of views and is based on a variational-inference tight surrogate bound coupled with a Lagrangian optimization objective function. The second formulation, i.e., the representational form, is shown to include known results as special cases. Here, we develop a tailored version from the alternating direction method of multipliers (ADMM) algorithm for solving the resulting non-convex optimization problem. In the two cases, the convergence of the proposed solvers is established in certain relevant regimes. Furthermore, our empirical results demonstrate the effectiveness of the proposed methods as compared with the state-of-the-art solvers. In a nutshell, the proposed solvers offer computational efficiency, theoretical convergence guarantees (local minima), scalable complexity with the number of views, and exceptional accuracy as compared with the state-of-the-art techniques. Our focus here is devoted to the discrete case and our results for continuous distributions are reported elsewhere.
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization

Most offline reinforcement learning (RL) methods suffer from the trade-off between improving the policy to surpass the behavior policy and constraining the policy to limit the deviation from the behavior policy as computing $Q$-values using out-of-distribution (OOD) actions will suffer from errors due to distributional shift. The recently proposed \textit{In-sample Learning} paradigm (i.e., IQL), which improves the policy by quantile regression using only data samples, shows great promise because it learns an optimal policy without querying the value function of any unseen actions. However, it remains unclear how this type of method handles the distributional shift in learning the value function. In this work, we make a key finding that the in-sample learning paradigm arises under the \textit{Implicit Value Regularization} (IVR) framework. This gives a deeper understanding of why the in-sample learning paradigm works, i.e., it applies implicit value regularization to the policy. Based on the IVR framework, we further propose two practical algorithms, Sparse $Q$-learning (SQL) and Exponential $Q$-learning (EQL), which adopt the same value regularization used in existing works, but in a complete in-sample manner. Compared with IQL, we find that our algorithms introduce sparsity in learning the value function, making them more robust in noisy data regimes. We also verify the effectiveness of SQL and EQL on D4RL benchmark datasets and show the benefits of in-sample learning by comparing them with CQL in small data regimes.
Proper orthogonal decomposition in Squire's coordinate system for dynamical models of channel turbulence

The convergence rate and the structures of the proper orthogonal decomposition (POD) reconstruction are re-examined. The relatively slow convergence rate of the wall-normal velocity and the over-prediction of the Reynolds shear stress from POD-based representations of near-wall turbulence suggest that coherent structures are too well correlated. This is a consequence of the directional preference of the eigenfunctions toward the most energetic data (the streamwise motion at $x^+_2 \,{=}\, 13$) which may be relaxed by extracting the POD eigenfunctions from the streak modes and the streamwise vortex modes separately. The rate of convergence and representation of structures may be improved by performing the POD in Squire's coordinate system (PODS). The statistical reconstruction of the localized ($x^+_2 \,{\leq}\, 84$) PODS eigen-functions from the large-eddy simulation (LES) database suggests that the convergence rate of component energy, Reynolds shear stress and the correlation coefficient of streamwise and wall-normal velocity improve significantly. The PODS eigenvalue spectra and the spatial structures of the eigenfunctions indicate the presence of observed streaks, streamwise vortices, and ejection/sweep events. Dynamical models of the full channel turbulence are constructed by projecting the Navier–Stokes equations onto the PODS eigenfunctions. The unresolved modes are taken into account by closure models adapted from a constant eddy-viscosity model, dynamic Smagorinsky, and dynamic one-coefficient mixed subgrid-scale (SGS) model. Statistical moments obtained from PODS-based simulations are much more accurate than their counterparts from POD-based simulations. This is particularly true for the mean velocity, streamwise turbulent intensity, Reynolds shear stress and the correlation coefficient of streamwise and wall-normal velocity.
Transient hygro- and hydro-expansion of freely and restrained dried paper: the fiber-network coupling

The transient dimensional changes during \textit{hygro}-expansion and \textit{hydro}-expansion of freely and restrained dried, softwood and hardwood sheets and fibers is monitored, to unravel the governing micro-mechanisms occurring during gradual water saturation. The response of individual fibers is measured using a full-field global digital height correlation method, which has been extended to monitor the transient \textit{hydro}-expansion of fibers from dry to fully saturated. The \textit{hygro}- and \textit{hydro}-expansion is larger for freely versus restrained dried and softwood versus hardwood handsheets. The transient sheet-scale \textit{hydro}-expansion reveals a sudden strain and moisture content step. It is postulated that the driving mechanism is the moisture-induced softening of the so-called"dislocated regions"in the fiber's cellulose micro-fibrils, unlocking further fiber swelling. The strain step is negligible for restrained dried handsheets, which is attributed to the"dislocated cellulose regions"being locked in their stretched configuration during restrained drying, which is supported by the single fiber \textit{hydro}-expansion measurements. Finally, an inter-fiber bond model is exploited and adapted to predict the sheet-scale \textit{hygro}-expansion from the fiber level characteristics. The model correctly predicts the qualitative differences between freely versus restrained dried and softwood versus hardwood handsheets, yet, its simplified geometry does not allow for more quantitative predictions of the sheet-scale \textit{hydro}-expansion.
Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition

Demographic biases in source datasets have been shown as one of the causes of unfairness and discrimination in the predictions of Machine Learning models. One of the most prominent types of demographic bias are statistical imbalances in the representation of demographic groups in the datasets. In this paper, we study the measurement of these biases by reviewing the existing metrics, including those that can be borrowed from other disciplines. We develop a taxonomy for the classification of these metrics, providing a practical guide for the selection of appropriate metrics. To illustrate the utility of our framework, and to further understand the practical characteristics of the metrics, we conduct a case study of 20 datasets used in Facial Emotion Recognition (FER), analyzing the biases present in them. Our experimental results show that many metrics are redundant and that a reduced subset of metrics may be sufficient to measure the amount of demographic bias. The paper provides valuable insights for researchers in AI and related fields to mitigate dataset bias and improve the fairness and accuracy of AI models. The code is available at https://github.com/irisdominguez/dataset_bias_metrics.
Fractal geometry of the PAM in 2D and 3D with white noise potential

We study the parabolic Anderson model (PAM) \begin{equation} {\partial \over \partial t}u(t,x) =\frac{1}{2}\Delta u(t,x) + u(t,x)\xi(x), \quad t>0, x\in \mathbb{R}^d, \quad \text{and} \quad u(0,x) \equiv 1, \quad \forall x\in \mathbb{R}^d, \end{equation} where $\xi$ is spatial white noise on $\mathbb{R}^d$ with $d \in\{2,3\}$. We show that the peaks of the PAM are macroscopically multifractal. More precisely, we prove that the spatial peaks of the PAM have infinitely many distinct values and we compute the macroscopic Hausdorff dimension (introduced by Barlow and Taylor) of those peaks. As a byproduct, we obtain the exact spatial asymptotics of the solution of the PAM. We also study the spatio-temporal peaks of the PAM and show their macroscopic multifractality. Some of the major tools used in our proof techniques include paracontrolled calculus and tail probabilities of the largest point in the spectrum of the Anderson Hamiltonian.
Conditions for symmetry reduction of polysymplectic and polycosymplectic structures

For Hamiltonian field theories on polysymplectic manifolds with a symmetry group action and a momentum map, we explore the redundancy in a set of necessary conditions that has appeared in the literature, for a generalized version of the Marsden–Weinstein symmetry reduction theorem. Next, we prove a necessary and sufficient condition for polycosymplectic reduction. We relate polycosymplectic reduction in a one-to-one way to the reduction of an associated larger polysymplectic manifold. Throughout the paper, we provide examples and discuss special cases.
On the role of dust and mass-loss in the extended main sequence turnoff of star clusters: the case of NGC 1783

The colour–magnitude diagram (CMD) morphology of the ‘extended’ main sequence turnoff (eMSTO) and upper main sequence (MS) of the intermediate age (≲ 2 Gyr) Large Magellanic Cloud Cluster NGC 1783 shows the presence of a small group of UV-dim stars, that, in the ultraviolet Hubble Space Telescope filters, are located at colours on the red side of the typical ‘fan’ shape displayed by the eMSTO. We model the UV-dim stars by assuming that some of the stars which would intrinsically be located on the left side of the eMSTO are obscured by a ring of dust due to grain condensation at the periphery of the excretion disc expelled when they spin at the high rotation rates typical of stars in the Be stage. A reasonably low optical depth at 10μ is necessary to model the UV-dim group. Introduction of dust in the interpretation of the eMSTO may require a substantial re-evaluation of previous conclusions concerning the role of age and/or rotation spreads in the MC clusters: the entire eMSTO can be populated by dusty stars, and the reddest UV-dim stars simply represents the tail of the distribution with both maximum obscuration and the dust ring seen along the line of sight. The model stars having higher rotational projected velocity (vsin i) are predicted to be preferentially redder than the slowly rotating stars. The mass-loss responsible for the dust may also cause the non-monotonic distribution of stars in the upper main sequence, with two peaks and gaps showing up in the UV CMD.
StarNet: Style-Aware 3D Point Cloud Generation

This paper investigates an open research task of reconstructing and generating 3D point clouds. Most existing works of 3D generative models directly take the Gaussian prior as input for the decoder to generate 3D point clouds, which fail to learn disentangled latent codes, leading noisy interpolated results. Most of the GAN-based models fail to discriminate the local geometries, resulting in the point clouds generated not evenly distributed at the object surface, hence degrading the point cloud generation quality. Moreover, prevailing methods adopt computation-intensive frameworks, such as flow-based models and Markov chains, which take plenty of time and resources in the training phase. To resolve these limitations, this paper proposes a unified style-aware network architecture combining both point-wise distance loss and adversarial loss, StarNet which is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network that can effectively disentangle the Gaussian prior from input's high-level attributes in the mapped latent space to generate realistic interpolated objects. Experimental results demonstrate that our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks, but is more lightweight in model size, requires much fewer parameters and less time for model training.
Ultrafast Variability in AGN Jets: Intermittency and Lighthouse Effect

Gamma-ray flares from active galactic nuclei (AGNs) show substantial variability on ultrafast timescales (i.e., shorter than the light-crossing time of the AGN’s supermassive black hole). We propose that ultrafast variability is a by-product of the turbulent dissipation of the jet Poynting flux. Due to the intermittency of the turbulent cascade, the dissipation is concentrated in a set of reconnecting current sheets. Electrons energized by reconnection have a strong pitch-angle anisotropy, i.e., their velocity is nearly aligned with the guide magnetic field. Then each current sheet produces a narrow radiation beam, which dominates the emission from the whole jet when it is directed toward the observer. The ultrafast variability is set by the light-crossing time of a single current sheet, which is much shorter than the light-crossing time of the whole emission region. The predictions of our model are (i) the bolometric luminosity of ultrafast AGN flares is dominated by the inverse Compton (IC) emission as the lower-energy synchrotron emission is suppressed due to the pitch-angle anisotropy; (ii) if the observed luminosity includes a nonflaring component, the variations of the synchrotron luminosity have a small amplitude; and (iii) the synchrotron and IC emission are less variable at lower frequencies, as the cooling time of the radiating particles exceeds the light-crossing time of the current sheet. Simultaneous multiwavelength observations of ultrafast AGN flares can test these predictions.
Billion-years old proteins show the importance of N-lobe orientation in Imatinib-kinase selectivity

The molecular origins of proteins' functions are a combinatorial search problem in the proteins' sequence space, which requires enormous resources to solve. However, evolution has already solved this optimization problem for us, leaving behind suboptimal solutions along the way. Comparing suboptimal proteins along the evolutionary pathway, or ancestors, with more optimal modern proteins can lead us to the exact molecular origins of a particular function. In this paper, we study the long-standing question of the selectivity of Imatinib, an anti-cancer kinase inhibitor drug. We study two related kinases, Src and Abl, and four of their common ancestors, to which Imatinib has significantly different affinities. Our results show that the orientation of the N-lobe with respect to the C-lobe varies between the kinases along their evolutionary pathway and is consistent with Imatinib's inhibition constants as measured experimentally. The conformation of the DFG-motif (Asp-Phe-Gly) and the structure of the P-loop also seem to have different stable conformations along the evolutionary pathway, which is aligned with Imatinib's affinity.
Synthetic Population of Binary Cepheids. II. The Effect of Companion Light on the Extragalactic Distance Scale

Because of their period–luminosity relation (PLR), classical Cepheids play a key role in the calibration of the extragalactic distance scale and the determination of the Hubble–Lemaître constant H 0. Recent findings show that the majority of classical Cepheids should be in binary or multiple systems, which might undermine their accuracy, as the extra—and unaccounted for—light from the companions of Cepheids causes a shift in the PLR. We quantify this shift using synthetic populations of binary Cepheids that we developed for this purpose, as described in Paper I of this series. We find that while all PLRs are shifted toward brighter values due to the excess light from the companions, the bias in the relative distance modulus between two galaxies hosting binary Cepheids can be either positive or negative, depending on the percentage of binary Cepheids in them. If the binarity percentage in the two galaxies is similar, the effect of binarity is canceled. Otherwise, it introduces a shift in the distance modulus of the order of millimagnitudes in the near-infrared passbands and Wesenheit indices, and tens of millimagnitudes in the visual domain; its exact value depends on the variant of the synthetic population (a unique combination of metallicity, star formation history, shape and location of the instability strip, and initial parameter distributions). Such shifts in the distance moduli to Type Ia supernova host galaxies introduce an additional statistical error on H 0, which however does not prevent measuring H 0 with a precision of 1%.
Molecular origins of mutational spectra produced by the environmental carcinogen N-nitrosodimethylamine and SN1 chemotherapeutic agents

Abstract DNA-methylating environmental carcinogens such as N-nitrosodimethylamine (NDMA) and certain alkylators used in chemotherapy form O6-methylguanine (m6G) as a functionally critical intermediate. NDMA is a multi-organ carcinogen found in contaminated water, polluted air, preserved foods, tobacco products, and many pharmaceuticals. Only ten weeks after exposure to NDMA, neonatally-treated mice experienced elevated mutation frequencies in liver, lung and kidney of ∼35-fold, 4-fold and 2-fold, respectively. High-resolution mutational spectra (HRMS) of liver and lung revealed distinctive patterns dominated by GC→AT mutations in 5’-Pu-G-3’ contexts, very similar to human COSMIC mutational signature SBS11. Commonly associated with alkylation damage, SBS11 appears in cancers treated with the DNA alkylator temozolomide (TMZ). When cells derived from the mice were treated with TMZ, N-methyl-N-nitrosourea, and streptozotocin (two other therapeutic methylating agents), all displayed NDMA-like HRMS, indicating mechanistically convergent mutational processes. The role of m6G in shaping the mutational spectrum of NDMA was probed by removing MGMT, the main cellular defense against m6G. MGMT-deficient mice displayed a strikingly enhanced mutant frequency, but identical HRMS, indicating that the mutational properties of these alkylators is likely owed to sequence-specific DNA binding. In sum, the HRMS of m6G-forming agents constitute an early-onset biomarker of exposure to DNA methylating carcinogens and drugs.
Comparative evaluation of three methods of adhesive remnant removal after orthodontic bracket debonding

ABSTRACT Objective: This study aimed to assess the effects of three methods of adhesive remnant removal (carbide bur and low speed handpiece, carbide bur and high speed handpiece, and zircon-rich glass fiber reinforced composite bur), after orthodontic bracket debonding, on tooth color and enamel surface roughness. Methods: Ninety sound premolar teeth were selected. The baseline tooth color was assessed using Vita spectrophotometer. The teeth were subjected to bracket bonding processes and then randomly divided into three equal groups. In each group, composite remnant was removed by one of the three methods of adhesive removal, and the teeth were then subjected to color assessment again. To measure the surface roughness, a scanning electron microscope (SEM) with x400 magnification was used. Results: ANOVA showed that the effect of the three methods of adhesive remnant removal on ∆L, ∆b and ∆E was statistically significant (p=0.01), but without significant effect on ∆a. Comparison of the means showed that composite bur and high speed carbide bur yielded the highest ∆E (p=0.05), and had a significant difference when compared to carbide bur and low speed handpiece. The highest ∆L and ∆b values belonged to samples approached with composite bur and carbide bur with high speed handpiece, respectively. SEM analysis showed that the composite bur created a very smooth surface, compared to the other two methods. Conclusion: Zircon-rich glass fiber reinforced composite created the smoothest enamel surface and highest color change, when compared to the other two methods.