Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling homophily beyond the pool of opportunities

Unveiling individuals' preferences for connecting with similar others (choice homophily) beyond the structural factors determining the pool of opportunities, is a challenging task. Here, we introduce a robust methodology for quantifying and inferring choice homophily in a variety of social networks. Our approach employs statistical network ensembles to estimate and standardize homophily measurements. We control for group size imbalances and activity disparities by counting the number of possible network configurations with a given number of inter-group links using combinatorics. This method provides a principled measure of connection preferences and their confidence intervals. Our framework is versatile, suitable for undirected and directed networks, and applicable in scenarios involving multiple groups. To validate our inference method, we test it on synthetic networks and show that it outperforms traditional metrics. Our approach accurately captures the generative homophily used to build the networks, even when we include additional tie-formation mechanisms, such as preferential attachment and triadic closure. Results show that while triadic closure has some influence on the inference, its impact is small in homophilic networks. On the other hand, preferential attachment does not perturb the results of the inference method. We apply our method to real-world networks, demonstrating its effectiveness in unveiling underlying gender homophily. Our method aligns with traditional metrics in networks with balanced populations, but we obtain different results when the group sizes or degrees are imbalanced. This finding highlights the importance of considering structural factors when measuring choice homophily in social networks.
Locality Sensitive Sparse Encoding for Learning World Models Online

Acquiring an accurate world model online for model-based reinforcement learning (MBRL) is challenging due to data nonstationarity, which typically causes catastrophic forgetting for neural networks (NNs). From the online learning perspective, a Follow-The-Leader (FTL) world model is desirable, which optimally fits all previous experiences at each round. Unfortunately, NN-based models need re-training on all accumulated data at every interaction step to achieve FTL, which is computationally expensive for lifelong agents. In this paper, we revisit models that can achieve FTL with incremental updates. Specifically, our world model is a linear regression model supported by nonlinear random features. The linear part ensures efficient FTL update while the nonlinear random feature empowers the fitting of complex environments. To best trade off model capacity and computation efficiency, we introduce a locality sensitive sparse encoding, which allows us to conduct efficient sparse updates even with very high dimensional nonlinear features. We validate the representation power of our encoding and verify that it allows efficient online learning under data covariate shift. We also show, in the Dyna MBRL setting, that our world models learned online using a single pass of trajectory data either surpass or match the performance of deep world models trained with replay and other continual learning methods.
Three closed characteristics on non-degenerate star-shaped hypersurfaces in $\mathbf{R}^{6}$

In this paper, we prove that for every non-degenerate $C^3$ compact star-shaped hypersurface $\Sigma$ in $\mathbf{R}^{6}$ which carries no prime closed characteristic of Maslov-type index $0$ or no prime closed characteristic of Maslov-type index $-1$, there exist at least three prime closed characteristics on $\Sigma$.
Bayesian sampling using interacting particles

Bayesian sampling is an important task in statistics and machine learning. Over the past decade, many ensemble-type sampling methods have been proposed. In contrast to the classical Markov chain Monte Carlo methods, these new methods deploy a large number of interactive samples, and the communication between these samples is crucial in speeding up the convergence. To justify the validity of these sampling strategies, the concept of interacting particles naturally calls for the mean-field theory. The theory establishes a correspondence between particle interactions encoded in a set of coupled ODEs/SDEs and a PDE that characterizes the evolution of the underlying distribution. This bridges numerical algorithms with the PDE theory used to show convergence in time. Many mathematical machineries are developed to provide the mean-field analysis, and we showcase two such examples: The coupling method and the compactness argument built upon the martingale strategy. The former has been deployed to show the convergence of ensemble Kalman sampler and ensemble Kalman inversion, and the latter will be shown to be immensely powerful in proving the validity of the Vlasov-Boltzmann simulator.
Magneto-chiral Nonlinear Optical Effect with Large Anisotropic Response in Two-Dimensional Halide Perovskite.

The chiral organic-inorganic halide perovskites (OIHPs) are vital candidates for superior nonlinear optical (NLO) effects associated with circularly polarized (CP) light. NLO in chiral materials often couples with magnetic dipole (MD) transition, as well as the conventional electric dipole (ED) transition. However, the importance of MD transition in NLO process of chiral OIHPs has not yet been well recognized. Here, the circular polarized probe analysis of second harmonic generation circular dichroism (SHG-CD) provides the direct evidence that the contribution of MD leads to a large anisotropic response to CP lights in chiral OIHPs, (R-/S-MBACl)2PbI4. The thin films exhibit great sensitivity to CP lights over a wide wavelength range, and the g-value reaches up to 1.57 at the wavelength where the contribution of MD is maximized. Furthermore, it is also effective as CP light generator, outputting CP-SHG with maximum g-factor of 1.76 upon the stimulation of linearly polarized light. This study deepens the understanding of relation between chirality and magneto-optical effect, and such an efficient discrimination and generation of CP light signal is highly applicable for chirality-based sensor and optical communication devices.
MambaByte: Token-free Selective State Space Model

Token-free language models learn directly from raw bytes and remove the bias of subword tokenization. Operating on bytes, however, results in significantly longer sequences, and standard autoregressive Transformers scale poorly in such settings. We experiment with MambaByte, a token-free adaptation of the Mamba state space model, trained autoregressively on byte sequences. Our experiments indicate the computational efficiency of MambaByte compared to other byte-level models. We also find MambaByte to be competitive with and even outperform state-of-the-art subword Transformers. Furthermore, owing to linear scaling in length, MambaByte benefits from fast inference compared to Transformers. Our findings establish the viability of MambaByte in enabling token-free language modeling.
Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain

Face recognition technology has been deployed in various real-life applications. The most sophisticated deep learning-based face recognition systems rely on training millions of face images through complex deep neural networks to achieve high accuracy. It is quite common for clients to upload face images to the service provider in order to access the model inference. However, the face image is a type of sensitive biometric attribute tied to the identity information of each user. Directly exposing the raw face image to the service provider poses a threat to the user's privacy. Current privacy-preserving approaches to face recognition focus on either concealing visual information on model input or protecting model output face embedding. The noticeable drop in recognition accuracy is a pitfall for most methods. This paper proposes a hybrid frequency-color fusion approach to reduce the input dimensionality of face recognition in the frequency domain. Moreover, sparse color information is also introduced to alleviate significant accuracy degradation after adding differential privacy noise. Besides, an identity-specific embedding mapping scheme is applied to protect original face embedding by enlarging the distance among identities. Lastly, secure multiparty computation is implemented for safely computing the embedding distance during model inference. The proposed method performs well on multiple widely used verification datasets. Moreover, it has around 2.6% to 4.2% higher accuracy than the state-of-the-art in the 1:N verification scenario.
Direct spectroscopy of Rubidium using a narrow-line transition at 420 nm

The 5S$\to$6P transition in Rubidium (Rb) at 420 nm offers the advantage of a narrower linewidth and diverse applications in quantum technologies. However, the direct spectroscopy at this transition is challenging due to its weak transition strength. In this paper, we have discussed the saturated absorption spectroscopy (SAS) of Rb using the narrow-line transition at 420 nm. We have studied the effect of the temperature of the Rb cell, pump power and the beam size on the SAS dip heights and their linewidths. Additionally, our study offers a comprehensive examination, encompassing all eight error signals of Rb for the 5S$\to$6P transition at 420 nm and 421 nm. These findings contribute valuable insights to the field of laser frequency stabilization of Rb at blue transition and can be useful in quantum technologies based on this transition.
Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding

This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics. Our examination involves a manually crafted exam encompassing 126 multiple-choice questions, spanning various aspects of mechanics courses, including Fluid Mechanics, Mechanical Vibration, Engineering Statics and Dynamics, Mechanics of Materials, Theory of Elasticity, and Continuum Mechanics. Three LLMs, including ChatGPT (GPT-3.5), ChatGPT (GPT-4), and Claude (Claude-2.1), were subjected to evaluation against engineering faculties and students with or without mechanical engineering background. The findings reveal GPT-4's superior performance over the other two LLMs and human cohorts in answering questions across various mechanics topics, except for Continuum Mechanics. This signals the potential future improvements for GPT models in handling symbolic calculations and tensor analyses. The performances of LLMs were all significantly improved with explanations prompted prior to direct responses, underscoring the crucial role of prompt engineering. Interestingly, GPT-3.5 demonstrates improved performance with prompts covering a broader domain, while GPT-4 excels with prompts focusing on specific subjects. Finally, GPT-4 exhibits notable advancements in mitigating input bias, as evidenced by guessing preferences for humans. This study unveils the substantial potential of LLMs as highly knowledgeable assistants in both mechanical pedagogy and scientific research.
Cobalt-Based Pyroxenes: A New Playground for Kitaev Physics and Ising Model Realization

Recent advances in the study of cobaltites have unveiled their potential as a promising platform for realizing Kitaev physics in honeycomb systems and the Ising model in weakly coupled chain materials. In this manuscript, we explore the magnetic properties of pyroxene SrCoGe$_2$O$_6$ using a combination of neutron scattering, {\it ab initio} methods, and linear spin-wave theory. Through careful examination of inelastic neutron scattering powder spectra, we propose a modified Kitaev model to accurately describe the twisted chains of edge-sharing octahedra surrounding Co$^{2+}$ ions. The extended Kitaev-Heisenberg model, including a significant anisotropic bond-dependent exchange term with $K/|J|=0.96$, is identified as the key descriptor of the magnetic interactions in SrCoGe$_2$O$_6$. Furthermore, our heat capacity measurements reveal an effect of an external magnetic field (approximately 13~T) which shifts the system from a fragile antiferromagnetic ordering with $T_{\mathrm{N}}=9$~K to a field-induced state. We argue that pyroxenes, particularly those modified by substituting Ge with Si and its less extended $p$ orbitals, emerge as a novel platform for the Kitaev model. This opens up possibilities for advancing our understanding of Kitaev physics.
Two High-amplitude δ Scuti–γ Doradus Hybrids Constrained by the Radial Fundamental p and Equally Spaced g Modes

Based on 2 minutes cadence TESS data, we investigate pulsations of TIC 65138566 and TIC 139729335 and discover them to be two new high-amplitude δ Scuti stars with equally spaced g modes. We recognize the radial fundamental mode f 1 = 18.3334 c d−1 and the first overtone f 3 = 23.6429 c d−1 for TIC 65138566, and identify the highest peak f 1 = 19.0955 c d−1 as the radial fundamental mode for TIC 139729335. For g modes, both stars display a regular period spacing of 2413 s. Through detailed seismological analysis, we deduce that these period-spacing patterns correspond to modes with ℓ = 1. Moreover, our analysis reveals that with increases in masses and metallicities, the star should display a higher degree of evolution to match a specific period spacing Π0. Conversely, the star should have a lower extent of evolution to match the radial fundamental mode. These two contradictory behaviors allow us to precisely obtain stellar physical parameters. TIC 65138566 and TIC 139729335 are determined to be two main-sequence stars that have almost the same range of masses and metallicities, with M = 1.36 ± 0.06 M ⊙ and Z = 0.005 ± 0.002. The hydrogen abundance in the core of TIC 65138566 is estimated to be about 0.28, while TIC 139729335 has a slightly higher value of around 0.31. Finally, we suggest that the high-amplitude δ Scuti–γ Doradus star TIC 308396022 is a main-sequence star with M = 1.54 ± 0.08 M ⊙, Z = 0.007 ± 0.001, and X c = 0.18 ± 0.02.
Estimating the severity of dental and oral problems via sentiment classification over clinical reports

Analyzing authors' sentiments in texts as a technique for identifying text polarity can be practical and useful in various fields, including medicine and dentistry. Currently, due to factors such as patients' limited knowledge about their condition, difficulties in accessing specialist doctors, or fear of illness, particularly in pandemic conditions, there might be a delay between receiving a radiology report and consulting a doctor. In some cases, this delay can pose significant risks to the patient, making timely decision-making crucial. Having an automatic system that can inform patients about the deterioration of their condition by analyzing the text of radiology reports could greatly impact timely decision-making. In this study, a dataset comprising 1,134 cone-beam computed tomography (CBCT) photo reports was collected from the Shiraz University of Medical Sciences. Each case was examined, and an expert labeled a severity level for the patient's condition on each document. After preprocessing all the text data, a deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect the severity level of the patient's problem based on sentiment analysis in the radiologist's report. The model's performance was evaluated on two datasets, each with two and four classes, in both imbalanced and balanced scenarios. Finally, to demonstrate the effectiveness of our model, we compared its performance with that of other classification models. The results, along with one-way ANOVA and Tukey's test, indicated that our proposed model (CNN-LSTM) performed the best according to precision, recall, and f-measure criteria. This suggests that it can be a reliable model for estimating the severity of oral and dental diseases, thereby assisting patients.
A BFF-Based Attention Mechanism for Trajectory Estimation in mmWave MIMO Communications

This paper explores a novel Neural Network (NN) architecture suitable for Beamformed Fingerprint (BFF) localization in a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) outdoor system. The mmWave frequency bands have attracted significant attention due to their precise timing measurements, making them appealing for applications demanding accurate device localization and trajectory estimation. The proposed NN architecture captures BFF sequences originating from various user paths, and through the application of learning mechanisms, subsequently estimates these trajectories. Specifically, we propose a method for trajectory estimation, employing a transformer network (TN) that relies on attention mechanisms. This TN-based approach estimates wireless device trajectories using BFF sequences recorded within a mmWave MIMO outdoor system. To validate the efficacy of our proposed approach, numerical experiments are conducted using a comprehensive dataset of radio measurements in an outdoor setting, complemented with ray tracing to simulate wireless signal propagation at 28 GHz. The results illustrate that the TN-based trajectory estimator outperforms other methods from the existing literature and possesses the ability to generalize effectively to new trajectories outside the training dataset.
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning

Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed learning framework. In this work, we focus on cross-silo FL, where clients become the model owners after training and are only concerned about the model's generalization performance on their local data. Due to the data heterogeneity issue, asking all the clients to join a single FL training process may result in model performance degradation. To investigate the effectiveness of collaboration, we first derive a generalization bound for each client when collaborating with others or when training independently. We show that the generalization performance of a client can be improved only by collaborating with other clients that have more training data and similar data distribution. Our analysis allows us to formulate a client utility maximization problem by partitioning clients into multiple collaborating groups. A hierarchical clustering-based collaborative training (HCCT) scheme is then proposed, which does not need to fix in advance the number of groups. We further analyze the convergence of HCCT for general non-convex loss functions which unveils the effect of data similarity among clients. Extensive simulations show that HCCT achieves better generalization performance than baseline schemes, whereas it degenerates to independent training and conventional FL in specific scenarios.
Does diet quality moderate the long-term effects of discrete but extreme PM2.5 exposure on respiratory symptoms? A study of the Hazelwood coalmine fire

Purpose In 2014, a fire at an open cut coalmine in regional Victoria, Australia burned for 6 weeks. Residents of the nearby town of Morwell were exposed to smoke, which included high levels of fine particulate matter (PM2.5). We investigated whether the long-term effects of PM2.5 on respiratory health were moderated by diet quality. Methods A cross-sectional analysis was conducted of data collected 8.5 years after the mine fire from 282 residents of Morwell and 166 residents from the nearby unexposed town of Sale. Primary outcomes were respiratory symptoms. Exposure was coalmine fire-related PM2.5 and diet quality was assessed with the Australian Eating Survey (AES). The moderating effect of diet quality on respiratory outcomes associated with PM2.5 was assessed using logistic regression models, adjusting for potential confounders. Results Diet quality was poor in this sample, with 60% in the lowest category of overall diet quality. Overall diet quality and fruit and vegetable quality significantly attenuated the association between PM2.5 and prevalence of chronic cough and phlegm. Sauce/condiment intake was associated with a greater effect of PM2.5 on COPD prevalence. No other moderating effects were significant. Conclusion The moderating effects of overall diet quality and vegetable and fruit intake aligned with a priori hypothesis, suggesting potential protective benefits. Considering the poor-quality diet in this community, we would encourage better eating habits including improving the fruit and vegetable intake. Key words: landscape fires, diet quality, respiratory health, PM2.5, fruit quality, vegetable quality, chronic cough
Neuronal Network Dynamics in the Posterodorsal Amygdala: Shaping Reproductive Hormone Pulsatility

Normal reproductive function and fertility rely on the rhythmic secretion of gonadotropin-releasing hormone (GnRH), which is driven by the hypothalamic GnRH pulse generator. A key regulator of the GnRH pulse generator is the posterodorsal subnucleus of the medial amygdala (MePD), a brain region which is involved in processing external environmental cues, including the effect of stress. However, the neuronal pathways enabling the dynamic, stress-triggered modulation of GnRH secretion remain largely unknown. Here, we employ in-silico modelling in order to explore the impact of dynamic inputs on GnRH pulse generator activity. We introduce and analyse a mathematical model representing MePD neuronal circuits composed of GABAergic and glutamatergic neuronal populations, integrating it with our GnRH pulse generator model. Our analysis dissects the influence of excitatory and inhibitory MePD projections’ outputs on the GnRH pulse generator’s activity and reveals a functionally relevant MePD glutamatergic projection to the GnRH pulse generator, which we probe with in vivo optogenetics. Our study sheds light on how MePD neuronal dynamics affect the GnRH pulse generator activity, and offers insights into stress-related dysregulation.
Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients

Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 174 patients, and 90,270 cancer cells. This compilation forms a unique resource tailored for investigating how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.