Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter-Efficient Conversational Recommender System as a Language Processing Task

Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes external knowledge graphs for items’ semantic information, a language model for dialogue generation, and a recommendation module for ranking relevant items. This combination of multiple components suffers from a cumber-some training process, and leads to semantic misalignment issues between dialogue generation and item recommendation. In this paper, we represent items in natural language and formulate CRS as a natural language processing task. Accordingly, we leverage the power of pre-trained language models to encode items, understand user intent via conversation, perform item recommendation through semantic matching, and generate dialogues. As a unified model, our PECRS (Parameter-Efficient CRS), can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph. Experiments on two benchmark CRS datasets, ReDial and INSPIRED, demonstrate the effectiveness of PECRS on recommendation and conversation. Our code is available at: https://github.com/Ravoxsg/efficient_unified_crs.
An analytic version of stable arithmetic regularity

We prove a structure theorem for stable functions on amenable groups, which extends the arithmetic regularity lemma for stable subsets of finite groups. Given a group $G$, a function $f\colon G\to [-1,1]$ is called stable if the binary function $f(x\cdot y)$ is stable in the sense of continuous logic. Roughly speaking, our main result says that if $G$ is amenable, then any stable function on $G$ is almost constant on all translates of a unitary Bohr set in $G$ of bounded complexity. The proof uses ingredients from topological dynamics and continuous model theory. We also discuss some applications, including a short proof of the noncommutative analogue of Bogolyubov's Lemma for amenable groups.
Multiview Graph Learning with Consensus Graph

Graph topology inference, i.e., learning graphs from a given set of nodal observations, is a significant task in many application domains. Existing approaches are mostly limited to learning a single graph assuming that the observed data is homogeneous. This is problematic because many modern datasets are heterogeneous or mixed and involve multiple related graphs, i.e., multiview graphs. Recent work proposing to learn multiview graphs ensures the similarity of learned view graphs through pairwise regularization, where each pair of views is encouraged to have similar structures. However, this approach cannot infer the shared structure across views. In this work, we propose an alternative method based on consensus regularization, where views are ensured to be similar through a learned consensus graph representing the common structure of the views. In particular, we propose an optimization problem, where graph data is assumed to be smooth over the multiview graph and the topology of the individual views and that of the consensus graph are learned, simultaneously. Our optimization problem is designed to be general in the sense that different regularization functions can be used depending on what the shared structure across views is. Moreover, we propose two regularization functions that extend fused and group graphical lasso to consensus based regularization. Proposed multiview graph learning is evaluated on simulated data and shown to have better performance than existing methods. It is also employed to infer the functional brain connectivity networks of multiple subjects from their electroencephalogram (EEG) recordings. The proposed method reveals the structure shared by subjects as well as the characteristics unique to each subject.
Heat kernel estimates for kinetic SDEs with drifts being unbounded and in Kato's class

In this paper we investigate the existence and uniqueness of weak solutions for kinetic stochastic differential equations with H\"older diffusion and unbounded singular drifts in Kato's class. Moreover, we also establish sharp two-sided estimates for the density of the solution. In particular, the drift $b$ can be in the mixed $L^q_tL^{p_1}_{x_1}L^{p_2}_{x_2}$ space with $\frac2q+\frac{d}{p_1}+\frac{3d}{p_2}<1$. As an application, we show the existence and uniqueness of weak solution to a second order singular interacting particle system in ${\mathbb R}^{d N}$.
Uncovering Heterogeneity of Solar Flare Mechanism With Mixture Models

The physics of solar flares occurring on the Sun is highly complex and far from fully understood. However, observations show that solar eruptions are associated with the intense kilogauss fields of active regions, where free energies are stored with field-aligned electric currents. With the advent of high-quality data sources such as the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), recent works on solar flare forecasting have been focusing on data-driven methods. In particular, black box machine learning and deep learning models are increasingly adopted in which underlying data structures are not modeled explicitly. If the active regions indeed follow the same laws of physics, there should be similar patterns shared among them, reflected by the observations. Yet, these black box models currently used in the literature do not explicitly characterize the heterogeneous nature of the solar flare data, within and between active regions. In this paper, we propose two finite mixture models designed to capture the heterogeneous patterns of active regions and their associated solar flare events. With extensive numerical studies, we demonstrate the usefulness of our proposed method for both resolving the sample imbalance issue and modeling the heterogeneity for rare energetic solar flare events.
How Are Paid and Volunteer Open Source Developers Different? A Study of the Rust Project

It is now commonplace for organizations to pay developers to work on specific open source software (OSS) projects to pursue their business goals. Such paid developers work alongside voluntary contributors, but given the different motivations of these two groups of developers, conflict may arise, which may pose a threat to a project's sustainability. This paper presents an empirical study of paid developers and volunteers in Rust, a popular open source programming language project. Rust is a particularly interesting case given considerable concerns about corporate participation. We compare volunteers and paid developers through contribution characteristics and long-term participation, and solicit volunteers' perceptions on paid developers. We find that core paid developers tend to contribute more frequently; commits contributed by one-time paid developers have bigger sizes; peripheral paid developers implement more features; and being paid plays a positive role in becoming a long-term contributor. We also find that volunteers do have some prejudices against paid developers. This study suggests that the dichotomous view of paid vs. volunteer developers is too simplistic and that further subgroups can be identified. Companies should become more sensitive to how they engage with OSS communities, in certain ways as suggested by this study.
Heteroscedasticity-aware stratified sampling to improve uplift modeling

In many business applications, including online marketing and customer churn prevention, randomized controlled trials (RCT's) are conducted to investigate on the effect of specific treatment (coupon offers, advertisement mailings,.). Such RCT's allow for the estimation of average treatment effects as well as the training of (uplift) models for the heterogeneity of treatment effects between individuals. The problem with these RCT's is that they are costly and this cost increases with the number of individuals included into the RCT. For this reason, there is research how to conduct experiments involving a small number of individuals while still obtaining precise treatment effect estimates. We contribute to this literature a heteroskedasticity-aware stratified sampling (HS) scheme, which leverages the fact that different individuals have different noise levels in their outcome and precise treatment effect estimation requires more observations from the"high-noise"individuals than from the"low-noise"individuals. By theory as well as by empirical experiments, we demonstrate that our HS-sampling yields significantly more precise estimates of the ATE, improves uplift models and makes their evaluation more reliable compared to RCT data sampled completely randomly. Due to the relative ease of application and the significant benefits, we expect HS-sampling to be valuable in many real-world applications.
Topological characterization of the continuum of allosteric response

Allosteric regulation in proteins is often accompanied by conformational changes that facilitate transmission of mechanical signals between distant ligand binding sites. Typically, these deformations are classified in terms of specific archetypes, including various types of hinge mechanisms or allosteric pathways localized to sequences of amino acids.However, many allosteric deformations resist such strict categorization. Here, we introduce a quantitative topological description of allosteric deformation, unifying all archetypal mechanisms into a single framework. The topological description aligns with two key structural features often associated with allosteric deformations, namely hinge domains and allosteric pathways, enabling us to quantify the significance of each of these features. To develop the analysis, we tune computer-generated mechanical networks to perform allostery-like functions, obtaining an ensemble of networks that establish a range of possible allosteric deformations. The analysis shows that these networks' allosteric mechanisms cannot be described in terms of discrete archetypes - they fall on a continuum. We then apply the same analysis to a collection of allosteric proteins with similar results, showing that our framework encompasses these proteins as well as designed allosteric networks. Our results provide a new picture for allostery, demonstrating not only how it can be described quantitatively, but also giving insight into how it emerges as a collective property.
Thermal and Magnetoelastic Properties of the van der Waals Ferromagnet Fe$_{3-\delta}$GeTe$_2$: Anisotropic Spontaneous Magnetostriction and Ferromagnetic Magnon Excitations

By determining the lattice parameters as a function of temperature of the hexagonal van der Waals ferromagnet Fe$_{2.92(1)}$Ge$_{1.02(3)}$Te$_2$ we obtain the temperature dependence of the spontaneous in-plane magnetostriction in the ferromagnetic and the linear thermal expansion coefficients in the paramagnetic state. The spontaneous magnetostriction is clearly seen in the temperature dependence of the in-plane lattice parameter $a(T)$, but less well pronounced perpendicular to the planes along $c$. Below $T_{\rm C}$ the spontaneous magnetostriction follows the square of the magnetization and leads to an expansion of the hexagonal layers. Extrapolating to $T\rightarrow$ 0~K we obtain a spontaneous in-plane saturation magnetostriction of $\lambda_{{\rm sp},a}(T \rightarrow 0) \approx-220 ~\times~10^{-6}$. In the paramagnetic state the linear thermal expansion coefficients amount to 13.9(1)$\times$10$^{-6}$~K$^{-1}$ and to 23.2(2)$\times$10$^{-6}$~K$^{-1}$ for the in-plane and out-of-plane direction, respectively, indicating a linear volume thermal expansion coefficient of 50.8(4)$\times$10$^{-6}$K$^{-1}$ which we use to estimate the volume thermal expansion contribution to the heat capacity determined at constant pressure. A Sommerfeld-type linear term in the low-temperature heat capacities can be quantitatively ascribed to 2dim ferromagnetic magnon excitations.
Near-field radiative heat transfer between shifted graphene gratings

We examine the near-field radiative heat transfer between finite-thickness planar fused silica slabs covered with graphene gratings, through the utilization of the exact Fourier modal method augmented with local basis functions (FMM-LBF), with focus on the lateral shift effect. To do so, we propose and validate a minor modification of the FMM-LBF theory to account for the lateral shift. This approach goes far beyond the effective medium approximation because this latter cannot account for the lateral shift. We show that the heat flux can exhibit significant oscillations with the lateral shift and, at short separation, it can experience up to a 60%-70% reduction compared to the aligned case. Such a lateral shift effect is found to be sensitive to the geometric factor $d/D$ (separation distance to grating period ratio). When $d/D>2$ (realized through large separation or small grating period), the two graphene gratings see each other as an effective whole rather than in detail, and thus the lateral shift effect on heat transfer becomes less important. Therefore, we can clearly distinguish two asymptotic regimes for radiative heat transfer: the LSE (Lateral Shift Effect) regime, where a significant lateral shift effect is observed, and the non-LSE regime, where this effect is negligible. Furthermore, regardless of the lateral shift, the radiative heat flux shows a non-monotonic dependence on the graphene chemical potential. That is, we can get an optimal radiative heat flux (peaking at about 0.3eV chemical potential) by $\textit{in situ}$ modulating the chemical potential. This work has the potential to unveil new avenues for harnessing the lateral shift effect on radiative heat transfer in graphene-based nanodevices.
Behavioral sequences across multiple animal species in the wild share common structural features

Animal behavior can be decomposed into a continuous sequence of discrete activity bouts over time. Analyzing the statistical structure of such behavioral sequences provides insights into the drivers of behavioral decisions in animals. Laboratory studies, predominantly in invertebrates, have suggested that behavioral sequences are characterized by multiple timescales and exhibit long-range memory, but the extent to which these results can be generalized to other taxa and to animals living in natural settings remains unclear. By analyzing accelerometer-inferred predictions of behavioral states in three species of social mammals (meerkats, white-nosed coatis, and spotted hyenas) in the wild, we discovered surprisingly consistent structuring of behavioral sequences across all behavioral states, all individuals and all study species. Behavioral bouts were characterized by decreasing hazard functions, wherein the longer a behavioral bout had progressed, the less likely it was to end within the next instant. The predictability of an animal’s future behavioral state as a function of its present state always decreased as a truncated power law for predictions made farther into the future, with very similar estimates for the power law exponent across all three species. Finally, the distributions of bout durations were also heavy-tailed (typically truncated power-law or lognormal). These patterns suggest similar structural principles in behavioral sequences across all study species. Why such shared patterns emerge remains unknown. We explore multiple plausible explanations, including environmental non-stationarity, behavioral self-reinforcement via positive feedback, or a combination of multiple relevant timescales driven by the hierarchical nature of behavior. The existence of highly consistent patterns in behavioral sequences across all three study species suggests that these phenomena could be widespread in nature, and points to the existence of fundamental properties of behavioral dynamics that could drive such convergent patterns. Significance statement The study of animal behavior seeks to understand how and why animals do what they do. This pursuit of general principles governing behavior across species can be approached by first understanding when animals choose to change their behavioral states (e.g., switching from walking to standing, or to running). Using accelerometer-inferred behaviors of three social mammals, we uncover common structural patterns in their sequences of behavior. We explore two explanations, involving either positive feedbacks or the interaction of several independent time-scales, about how such common patterns arise.
Taste cells expressing Ionotropic Receptor 94e reciprocally impact feeding and egg laying in Drosophila

Chemosensory cells across the body of Drosophila melanogaster evaluate the environment and play a crucial role in neural circuits that prioritize feeding, mating, or egg laying. Previous mapping of gustatory receptor neurons (GRNs) on the fly labellum identified a set of neurons in L-type sensilla defined by expression of Ionotropic Receptor 94e (IR94e), but the impact of IR94e GRNs on behavior remained unclear. To understand their behavioral output, we used optogenetics and chemogenetics to activate IR94e neurons and found that they drive mild suppression of feeding but enhanced egg laying. In vivo calcium imaging revealed that IR94e GRNs respond strongly to certain amino acids, including glutamate. Furthermore, we found that IR94e is necessary and sufficient for the detection of amino acid ligands, and co-receptors IR25a and IR76b are also required for IR94e GRN activation. Finally, IR94e mutants show behavioral changes to solutions containing amino acids, including increased consumption and decreased egg laying. Overall, our results suggest that IR94e GRNs on the fly labellum discourage feeding and encourage egg laying as part of an important behavioral switch in response to certain chemical cues.
MetaIBS - large-scale amplicon-based meta analysis of irritable bowel syndrome

Background Irritable Bowel Syndrome (IBS) is a chronic functional bowel disorder causing abdominal discomfort, as well as transit deregulation with constipation and/or diarrhea. The pathophysiology of IBS is poorly understood and believed to be multifactorial. The role of gut microbiota in IBS has been investigated in several case-control studies, in particular via 16S rRNA amplicon sequencing surveys. These studies, however, have not yet led to a consistent picture of significant changes in gut microbial compositions across health and disease. One key bottleneck is the modest cohort sizes of most individual studies and a high diversity of experimental, bioinformatics, and statistical analysis approaches across studies. Results We address these shortcomings by presenting MetaIBS, an open-access data repository and associated meta-analysis workflow of thirteen 16S rRNA amplicon datasets comprising both fecal matter and sigmoid biopsy samples spanning ∽2,500 IBS and healthy individuals. MetaIBS includes a tailored computational framework that (i) enables coherent de novo processing and taxonomic assignments of the raw 16S rRNA amplicon reads across experimental protocols and sequencing technologies, and (ii) statistical workflows for visualization and analysis at different taxonomic ranks and data granularity. Our statistical meta-analysis shows that popular high-level microbiome summary statistics, including Firmicutes/Bacteroidota ratios or diversity indices, are insufficient for reliable discrimination between IBS patients and healthy controls. Fine-grained multi-method differential abundance and classification analysis, however, can identify sets of differentially abundant taxa that replicate across multiple datasets, including Coprococcus eutactus and Alistipes finegoldii. Conclusions MetaIBS provides a curated and reproducible data and (meta-)analysis resource for amplicon-based IBS research at unprecedented scale. MetaIBS allows assessing the heterogeneity of IBS cohorts across multiple experimental protocols, sample types, and IBS phenotypes. Our framework will likely contribute to more coherent insights into the role of the microbiome in IBS and the discovery of reliable microbial IBS biomarkers for follow-up functional and translational studies.
Multiple Sclerosis Stages and their Differentially Expressed Genes: A Bioinformatics Analysis

Multiple Sclerosis (MS) is an inflammatory, chronic, autoimmune, and demyelinating disease of the central nervous system. MS is a heterogeneous disease with three main clinical forms, affecting the progression and therefore the treatment of the disease. Thus, finding key genes and microRNAs (miRNA) associated with MS stages and analyzing their interactions is important to better understand the molecular mechanism underlying the occurrence and the evolution of MS. Based on publicly available datasets of mRNA and miRNA expression profiles, differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between patients with different stages of MS and healthy controls and between relapsing and remitting phases of RRMS were determined using Deseq2 and GEO2R tools. We then analyzed miRNA-mRNA regulatory interactions and gene ontology for the DEGs. Based on miRNA-mRNA regulatory interactions, we identified potential biomarkers of RRMS, 13 upregulated miRNA regulators of 30 downregulated genes and 17 downregulated miRNA regulators of 32 upregulated genes. We also identified 9 downregulated miRNA regulators of 12 upregulated genes as potential biomarkers of SPMS. Our study findings highlight some key protein-coding genes and miRNAs that are involved in the occurrence and evolution of MS.
Whole-genome phylogenomics of the tinamous (Aves: Tinamidae): comparing gene tree estimation error between BUSCOs and UCEs illuminates rapid divergence with introgression

Incomplete lineage sorting (ILS) and introgression increase genealogical discordance across the genome, which complicates phylogenetic inference. In such cases, identifying orthologs that result in gene trees with low estimation error is crucial because phylogenomic methods rely on accurate gene histories. We sequenced whole genomes for the tinamous (Aves: Tinamidae) to dissect the sources of gene and species-tree discordance and reconstruct their interrelationships. We compared results based on four ortholog sets: (1) coding genes (BUSCOs), (2) ultraconserved elements (UCEs) with short flanking regions, (3) UCEs with intermediate flanks, and (4) UCEs with long flanks. We hypothesized that orthologs with more phylogenetically informative sites would result in more accurate species trees because the resulting gene trees contain lower error. Consistent with our hypothesis, we found that long UCEs had the most informative sites and lowest rates of error. However, despite having many informative sites, BUSCO gene trees contained high error compared to long UCEs. Unlike UCEs, BUSCO gene sequences showed a positive association between the proportion of parsimony informative sites and gene tree error. Thus, BUSCO and UCE datasets have different underlying properties of molecular evolution, and these differences should be considered when selecting loci for phylogenomic analysis. Still, species trees from different datasets were mostly congruent. Only one clade, with a history of ILS and introgression, exhibited substantial species-tree discordance across the different data sets. Overall, we present the most complete phylogeny for tinamous to date, identify a new species, and provide a case study for species-level phylogenomic analysis using whole-genomes.