Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Recommendation Diversity by Re-ranking with Large Language Models

It has long been recognized that it is not enough for a Recommender System (RS) to provide recommendations based only on their relevance to users. Among many other criteria, the set of recommendations may need to be diverse in order to handle uncertainty and offer a meaningful choice. The literature reports many ways of measuring diversity and ways of improving the diversity of a set of recommendations, most notably by re-ranking and selecting from a larger set of candidate recommendations. Driven by promising insights from the literature on how to incorporate versatile Large Language Models (LLMs) into the RS pipeline, in this paper, we show how LLMs can be used for diversity re-ranking. We begin with an informal study that verifies that LLMs can be used for re-ranking tasks and do have some understanding of the concept of diversity. Then, we design a more rigorous methodology where LLMs are prompted to generate a diverse ranking from a candidate ranking using various prompt templates with different re-ranking instructions in a zero-shot fashion. We conduct comprehensive experiments testing state-of-the-art conversational LLMs from the GPT and Llama families. We compare their re-ranking capabilities with random re-ranking and various traditional re-ranking methods from the literature (MMR, xQuAD and RxQuAD). We find that LLM-based re-ranking outperforms random re-ranking across all the metrics that we use but does not perform as well as the traditional re-ranking methods. We gain insight into prompt design for this task (e.g.\ on the whole, it is better to prompt for diversity rather than a balance of diversity and relevance). Given that no special knowledge engineering is needed, we conclude that LLM-based re-ranking is a promising approach, and we highlight directions for future research. We open-source the code of our experiments for reproducibility.
A Comprehensive Correction of the Gaia DR3 XP Spectra

By combining spectra from the CALSPEC and NGSL, as well as spectroscopic data from the LAMOST Data Release 7 (DR7), we have analyzed and corrected the systematic errors of the Gaia DR3 BP/RP (XP) spectra. The errors depend on the normalized spectral energy distribution (simplified by two independent “colors”) and G magnitude. Our corrections are applicable in the range of approximately −0.5 < BP − RP < 2, 3 < G < 17.5, and E(B − V) < 0.8. To validate our correction, we conduct independent tests by comparisons with the MILES and LEMONY spectra. The results demonstrate that the systematic errors of BP − RP and G have been effectively corrected, especially in the near-ultraviolet. The consistency between the corrected Gaia XP spectra and the MILES and LEMONY is better than 2% in the wavelength range of 336–400 nm and 1% in redder wavelengths. A global absolute calibration is also carried out by comparing the synthetic Gaia photometry from the corrected XP spectra with the corrected Gaia DR3 photometry. Our study opens up new possibilities for using XP spectra in many fields. A Python package is publicly available to do the corrections (doi:10.12149/101375 or https://github.com/HiromonGON/GaiaXPcorrection).
On homomorphisms and cv-polynomials between iterated Ore extensions

Motivated by the study of homomorphisms and cv-polynomials presented by Rimmer \cite{Rimmer1978} in the case of Ore extensions of automorphism type, Ferrero and Kishimoto \cite{FerreroKishimoto1980} and Kikumasa \cite{Kikumasa1990} in the setting of these extensions of derivation type, and Lam and Leroy \cite{LamLeroy1992} in the context of Ore extensions of mixed type over division rings, in this paper we investigate these both notions for iterations of these extensions. We show that the iteration of some of the results presented in those papers is non-trivial, and illustrate our treatment with several noncommutative algebras.
Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents

The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-based model. This model has three states, Susceptible, Believer, and Fact-Checker. The dynamics and transition between states are based on neighbors' beliefs, hoax credibility, spreading rate, probability of verifying the news, and probability of forgetting the current state. Our contribution is to push this model to real social networks by considering different classes of agents with their characteristics. We proposed two main strategies for confronting misinformation diffusion. First, we can educate a minor class, like scholars or influencers, to improve their ability to verify the news or remember their state longer. The second strategy is adding fact-checker bots to the network to spread the facts and influence their neighbors' states. Our result shows that both of these approaches can effectively control the misinformation spread.
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency

In stereo-matching knowledge distillation methods of the self-supervised monocular depth estimation, the stereo-matching network's knowledge is distilled into a monocular depth network through pseudo-depth maps. In these methods, the learning-based stereo-confidence network is generally utilized to identify errors in the pseudo-depth maps to prevent transferring the errors. However, the learning-based stereo-confidence networks should be trained with ground truth (GT), which is not feasible in a self-supervised setting. In this paper, we propose a method to identify and filter errors in the pseudo-depth map using multiple disparity maps by checking their consistency without the need for GT and a training process. Experimental results show that the proposed method outperforms the previous methods and works well on various configurations by filtering out erroneous areas where the stereo-matching is vulnerable, especially such as textureless regions, occlusion boundaries, and reflective surfaces.
A Generalization of Bivariate Lack-of-Memory Properties

In this paper, we propose an extension of the standard strong and weak lack-of-memory properties. We say that the survival function $\bar{F}$ of the vector $(X,Y)$ satisfies pseudo lack-of-memory property in strong version if \begin{equation} \label{strong}\bar F_{X,Y}(s_1+t_1,s_2+t_2)=\bar F_{X,Y}(s_1,s_2)\otimes_h\bar F_{X,Y}(t_1,t_2), \ t_1,t_2,s_1,s_2 \geq 0 \end{equation} and in weak version if \begin{equation}\label{weak}\bar F_{X,Y}(s_1+t,s_2+t)=\bar F_{X,Y}(s_1,s_2)\otimes_h\bar F_{X,Y}(t,t), \ s_1,s_2,t \geq 0\end{equation} with $a\otimes_hb=h\left (h^{-1}(a)\cdot h^{-1}(b)\right )$, where $h$ is an increasing bijection of $[0,1]$, called generator. After finding sufficient conditions under which the solutions of the above functional equations are bivariate survival functions, we focus on distributions satisfying the latter: we study specific properties in comparison with standard lack-of-memory property and we give a characterization in terms of the random variables $\min(X,Y)$ and $ X -Y$. Finally, we investigate the induced dependence structure, determining their singularity in full generality and studying the upper and lower dependence coefficients for some specific choices of the marginal survival functions and of the generator $h$.
Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset

In this paper, we propose leveraging causal generative learning as an interpretable tool for explaining image classifiers. Specifically, we present a generative counterfactual inference approach to study the influence of visual features (i.e., pixels) as well as causal factors through generative learning. To this end, we first uncover the most influential pixels on a classifier's decision by varying the value of a causal attribute via counterfactual inference and computing both Shapely and contrastive explanations for counterfactual images with these different attribute values. We then establish a Monte-Carlo mechanism using the generator of a causal generative model in order to adapt Shapley explainers to produce feature importances for the human-interpretable attributes of a causal dataset in the case where a classifier has been trained exclusively on the images of the dataset. Finally, we present optimization methods for creating counterfactual explanations of classifiers by means of counterfactual inference, proposing straightforward approaches for both differentiable and arbitrary classifiers. We exploit the Morpho-MNIST causal dataset as a case study for exploring our proposed methods for generating counterfacutl explantions. We employ visual explanation methods from OmnixAI open source toolkit to compare them with our proposed methods. By employing quantitative metrics to measure the interpretability of counterfactual explanations, we find that our proposed methods of counterfactual explanation offer more interpretable explanations compared to those generated from OmnixAI. This finding suggests that our methods are well-suited for generating highly interpretable counterfactual explanations on causal datasets.
Multi-faceted roles of β-arrestins in G protein-coupled receptors endocytosis

Internalization plays a crucial role in regulating the density of cell surface receptors and has been demonstrated to regulate intracellular signaling. Dysregulation of this process has been implicated in various diseases. The vast majority of GPCRs were considered to adopt one way for internalization. We challenged this conventional view by showing that multiple pathways converge to regulate the internalization of a specific receptor, based on an unparalleled characterization of 60 GPCR internalization profiles, both in the absence and presence of individual β-arrestins (βarrs). Furthermore, we revealed the internalization mechanism of the glucagon-like peptide-1 receptor (GLP-1R), a class B GPCR pivotal in promoting insulin secretion from pancreatic beta cells to maintain glucose homeostasis. GLP-1R can undergo agonist-induced internalization without βarrs, but can recruit and form stable complexes with βarrs. We found that GLP-1R recruits clathrin adaptor protein-2 for agonist-induced internalization in both βarr-dependent and -independent manners. These results provide a valuable resource for GPCR signaling and reveal the plasticity of different GPCRs to employ or not βarrs in the clathrin-mediated internalization.
Histological analysis of Scots pine (Pinus sylvestris L.) seedlings in response to root rot pathogen Heterobasidion annosum inoculation

Heterobasidion root rot is one of the most serious and economically destructive forest diseases in the Northern Hemisphere. Although several studies have explored the genetic and chemical responses of Scots pine to Heterobasidion spp. infection, the histological defense responses of this species remain poorly understood. In this study, we investigated the histological responses of three-year-old Scots pine seedlings to Heterobasidion annosum inoculation and a wounding treatment with no inoculation, focusing on lesion length and traumatic resin-duct characteristics (density, size). Our results showed that H. annosum-inoculated seedlings exhibited significantly more browning necrotic lesions than wounded seedlings. Traumatic resin duct density was significantly higher in H. annosum-inoculated seedlings compared to wounded seedlings, particularly within the first two cm from the inoculation point. However, as the distance from the inoculation point increased, the resin duct density decreased. Notably, there were neither statistically significant differences in the mean size of traumatic and constitutive resin ducts between the two treatments, nor within the H. annosum inoculation treatment itself. In contrast, within the wound treatment, the mean size of traumatic resin ducts was found to be significantly smaller than that of constitutive resin ducts. Furthermore, traumatic resin ducts did not prove to be reliable markers for dating H. annosum infection in Scots pine. Overall, this study advances knowledge about Scots pine’s histological defense mechanisms against H. annosum invasion, which has significant implications.
A general theoretical framework unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses

Sounds are temporal stimuli decomposed into numerous elementary components by the auditory nervous system. For instance, a temporal to spectro-temporal transformation modelling the frequency decomposition performed by the cochlea is a widely adopted first processing step in today’s computational models of auditory neural responses. Similarly, increments and decrements in sound intensity (i.e., of the raw waveform itself or of its spectral bands) constitute critical features of the neural code, with high behavioural significance. However, despite the growing attention of the scientific community on auditory OFF responses, their relationship with transient ON, sustained responses and adaptation remains unclear. In this context, we propose a new general model, based on a pair of linear filters, named AdapTrans, that captures both sustained and transient ON and OFF responses into a unifying and easy to expand framework. We demonstrate that filtering audio cochleagrams with AdapTrans permits to accurately render known properties of neural responses measured in different mammal species such as the dependence of OFF responses on the stimulus fall time and on the preceding sound duration. Furthermore, by integrating our framework into gold standard and stateof-the-art machine learning models that predict neural responses from audio stimuli, following a supervised training on a large compilation of electrophysiology datasets (ready-to-deploy PyTorch models and pre-processed datasets shared publicly), we show that AdapTrans systematically improves the prediction accuracy of estimated responses within different cortical areas of the rat and ferret auditory brain. Together, these results motivate the use of our framework for computational and systems neuroscientists willing to increase the plausibility and performances of their models of audition.
Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most effective predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, an effective data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model’s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.
Spatially targeted inhibitory rhythms differentially affect neuronal integration

Pyramidal neurons form dense recurrently connected networks with multiple types of inhibitory interneurons. A major differentiator between interneuron subtypes is whether they synapse onto perisomatic or dendritic regions. They can also engender local inhibitory rhythms, beta (12-35 Hz) and gamma (40-80 Hz). The interaction between the rhythmicity of inhibition and its spatial targeting on the neuron may determine how it regulates neuronal integration. Thus, we sought to understand how rhythmic perisomatic and distal dendritic inhibition impacted integration in a layer 5 pyramidal neuron model with elaborate dendrites and Na+, NMDA, and Ca2+ dendritic spikes. We found that inhibition regulated the coupling between dendritic spikes and action potentials in a location and rhythm-dependent manner. Perisomatic inhibition principally regulated action potential generation, while distal dendritic inhibition regulated the incidence of dendritic spikes and their temporal coupling with action potentials. Perisomatic inhibition was most effective when provided at gamma frequencies, while distal dendritic inhibition functioned best at beta. Moreover, beta modulated responsiveness to apical inputs in a phase-dependent manner, while gamma did so for basal inputs. These results may provide a functional interpretation for the reported association of soma-targeting parvalbumin positive interneurons with gamma, and dendrite-targeting somatostatin interneurons with beta.
Δ9-tetrahydrocannabinol (THC) Increases the Rewarding Value of Oxycodone During Self-Administration in Rats

Background Cannabis may reduce the nonmedical use of prescription opioids. Causality of polydrug use is difficult to establish from epidemiological data, and thus controlled laboratory models can test whether cannabinoid co-use with opioids can modulate opioid intake. Methods Male and female rats were trained to intravenously self-administer (IVSA) oxycodone (0.15 mg/kg/infusion) during 6 h sessions. Separate groups were injected with the vehicle or with THC (5 mg/kg, i.p.; N=10) 30 minutes before sessions for the first three weeks. Treatments were swapped in the fourth week. One male group was trained in the intracranial self-stimulation (ICSS) procedure and assessed for brain reward thresholds prior to each IVSA session. Results THC treated animals self-administered less oxycodone during acquisition, with a larger differential expressed in the female group. Tolerance to the THC effect developed over the initial weeks, and increasing the dose of THC (10 mg/kg, i.p.) prolonged the suppressing effect on IVSA. While ICSS thresholds increased with sequential IVSA sessions, no differences between THC- and Vehicle-treated groups were observed. Oxycodone IVSA was increased following the first 60 h abstinence interval in THC-treated, but not vehicle-treated, rats. Acute injection of THC, when all animals had been THC abstinent for several weeks, increased breakpoints in a Progressive Ratio procedure. Conclusion These data support the interpretation that THC enhances the reinforcing efficacy of a given dose of oxycodone and may therefore increase the addiction liability.
Cholinergic neuromodulation of prefrontal attractor dynamics controls performance in spatial working memory

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the Nucleus Basalis of Meynert (NB) have been recently examined (Qi et al. 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in PFC. Significance statement Acetylcholine has been thought to improve cognitive performance by sharpening neuronal tuning in prefrontal cortex. Recent work has shown that electrical stimulation of the cholinergic forebrain in awake-behaving monkeys induces a reduction in prefrontal neural tuning under stimulation conditions that improve performance. To reconcile these divergent observations, we provide network simulations showing that these derive consistently from specific conditions in prefrontal attractor dynamics: firing rate saturation leads to increased storage precision and reduced neural tuning upon cholinergic activation via an increase in neural excitability, a reduction in neural correlations, and an increase in excitatory transmission. Our study integrates previously reported data into a consistent mechanistic view of how acetylcholine controls spatial working memory via attractor network dynamics in prefrontal cortex.