Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems

Many large-scale recommender systems consist of two stages. The first stage efficiently screens the complete pool of items for a small subset of promising candidates, from which the second-stage model curates the final recommendations. In this paper, we investigate how to ensure group fairness to the items in this two-stage architecture. In particular, we find that existing first-stage recommenders might select an irrecoverably unfair set of candidates such that there is no hope for the second-stage recommender to deliver fair recommendations. To this end, motivated by recent advances in uncertainty quantification, we propose two threshold-policy selection rules that can provide distribution-free and finite-sample guarantees on fairness in first-stage recommenders. More concretely, given any relevance model of queries and items and a point-wise lower confidence bound on the expected number of relevant items for each threshold-policy, the two rules find near-optimal sets of candidates that contain enough relevant items in expectation from each group of items. To instantiate the rules, we demonstrate how to derive such confidence bounds from potentially partial and biased user feedback data, which are abundant in many large-scale recommender systems. In addition, we provide both finite-sample and asymptotic analyses of how close the two threshold selection rules are to the optimal thresholds. Beyond this theoretical analysis, we show empirically that these two rules can consistently select enough relevant items from each group while minimizing the size of the candidate sets for a wide range of settings.
Combining vs. Transferring Knowledge: Investigating Strategies for Improving Demographic Inference in Low Resource Settings

For some learning tasks, generating a large labeled data set is impractical. Demographic inference using social media data is one such task. While different strategies have been proposed to mitigate this challenge, including transfer learning, data augmentation, and data combination, they have not been explored for the task of user level demographic inference using social media data. This paper explores two of these strategies: data combination and transfer learning. First, we combine labeled training data from multiple data sets of similar size to understand when the combination is valuable and when it is not. Using data set distance, we quantify the relationship between our data sets to help explain the performance of the combination strategy. Then, we consider supervised transfer learning, where we pretrain a model on a larger labeled data set, fine-tune the model on smaller data sets, and incorporate regularization as part of the transfer learning process. We empirically show the strengths and limitations of the proposed techniques on multiple Twitter data sets.
A Tutorial on Domain Generalization

With the availability of massive labeled training data, powerful machine learning models can be trained. However, the traditional I.I. D. assumption that the training and testing data should follow the same distribution is often violated in reality. While existing domain adaptation approaches can tackle domain shift, it relies on the target samples for training. Domain generalization is a promising technology that aims to train models with good generalization ability to unseen distributions. In this tutorial, we will present the recent advance of domain generalization. Specifically, we introduce the background, formulation, and theory behind this topic. Our primary focus is on the methodology, evaluation, and applications. We hope this tutorial can draw interest of the community and provide a thorough review of this area. Eventually, more robust systems can be built for responsible AI. All tutorial materials and updates can be found online at https://dgresearch.github. io/.
A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework

Spatiotemporal data forecasting is a fundamental task in the field of graph data mining. Typical spatiotemporal data prediction methods usually capture spatial dependencies by directly aggregating features of local neighboring vertices in a fixed graph. However, this kind of aggregators can only capture localized correlations between vertices, and while been stacked for larger receptive field, they fall into the dilemma of over-smoothing. Additional, in temporal perspective, traditional methods focus on fixed graphs, while the correlations among vertexes can be dynamic. And time series components integrated strategies in traditional spatiotemporal learning methods can hardly handle frequently and drastically changed sequences. To overcome those limitations of existing works, in this paper, we propose a novel multi-graph based dynamic learning framework. First, a novel Dynamic Neighbor Search (DNS) mechanism is introduced to model global dynamic correlations between vertices by constructing a feature graph (FG), where the adjacency matrix is dynamically determined by DNS. Then we further alleviate the over-smoothing issue with our newly designed Adaptive Heterogeneous Representation (AHR) module. Both FG and origin graph (OG) are fed into the AHR modules and fused in our proposed Multi-graph Fusion block. Additionally, we design a Differential Vertex Representation (DVR) module which takes advantage of differential information to model temporal trends. Extensive experiments illustrate the superior forecasting performances of our proposed multi-graph based dynamic learning framework on six real-world spatiotemporal datasets from different cities and domains, and this corroborates the solid effectiveness of our proposed framework and its superior generalization ability.
Disentangled Negative Sampling for Collaborative Filtering

Negative sampling is essential for implicit collaborative filtering to generate negative samples from massive unlabeled data. Unlike existing strategies that consider items as a whole when selecting negative items, we argue that normally user interactions are mainly driven by some relevant, but not all, factors of items, leading to a new direction of negative sampling. In this paper, we introduce a novel disentangled negative sampling (DENS) method. We first disentangle the relevant and irrelevant factors of positive and negative items using a hierarchical gating module. Next, we design a factor-aware sampling strategy to identify the best negative samples by contrasting the relevant factors while keeping irrelevant factors similar. To ensure the credibility of the disentanglement, we propose to adopt contrastive learning and introduce four pairwise contrastive tasks, which enable to learn better disentangled representations of the relevant and irrelevant factors and remove the dependency on ground truth. Extensive experiments on five real-world datasets demonstrate the superiority of DENS against several state-of-the-art competitors, achieving over 7% improvement over the strongest baseline in terms of Recall@20 and NDCG@20. Our code is publically available at https://github.com/Riwei-HEU/DENS .
Exploiting Explicit and Implicit Item relationships for Session-based Recommendation

The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in HR@20, and 49.10% and 115.29% improvement in MRR@20 on Diginetica and LastFM datasets.
Solving least squares problems

Since the lm function provides a lot of features it is rather complicated. So we are going to instead use the function lsfit as a model. It computes only the coefficient estimates and the residuals. Now would be a good time to read the help file for lsfit. Note that lsfit supports the fitting of multiple least squares models and weighted least squares. Our function will not, hence we can omit the arguments wt, weights and yname. Also, changing tolerances is a little advanced so we will trust the default values and omit the argument tolerance as well.
Study of plasma termination using high-Z noble gas puffing in the JT-60U tokamak

Argon, krypton and xenon were puffed with and without simultaneous hydrogen gas puffing into Ohmically heated plasmas of the JT-60U tokamak with low plasma currents in order to study the capability of disruption mitigation. It was found that krypton gas puffing can provide a plasma termination with smaller amounts of runaway electrons in comparison to argon and xenon gas puffing.
Limit Theorems for Stochastic Processes

Let us consider a sequence of processes $\xi _n (t)$ such that the multivariate distribution of $\xi _n (t_1 ),\xi _n (t_2 ), \cdots ,\xi _n (t_k )$ tends to the multivariate distribution of $\xi _0 (t_1 ),\xi _0 (t_2 ), \cdots ,\xi _0 (t_k )$ for all k and $t_1 ,t_2 , \cdots ,t_k $. Let f be the functional for which $f(\xi _n (t))$ are determined with a probability of 1, the latter being random variables (i.e, those that have probability distributions). This paper contains several sufficient conditions, for which the distributions of $f(\xi _n (t))$ tend to the distribution of $f(\xi _0 (t))$ as $n \to \infty $. Let K be the space of all functions not having discontinuities higher than simple jumps, and let us assume that $\xi _n (t)$ with a probability of 1 is in K.Several topologies in K are defined. The necessary and sufficient conditions are found for all functionals f that are continuous in these topologies for which the distribution of $f(\xi _n (t))$ tends to the distribution of $f(\xi _0 (t))$. The r.
Soft and Repulsive:. Relationship Between Lipid Membrane In-Plane Fluctuations, Bending Rigidity, and Repulsive Undulation Forces

Lipid membranes are soft and flexible bilayer surfaces that exhibit a substantial degree of in-plane fluctuations which become very strong near lipid phase transitions and in phase separation regions. The fluctuations couple to the out-of-plane motions as well as the large-scale mechanical modulii of the membrane leading to a thermal renormalization of, e.g., the bending rigidity. For multilamellar arrays of membranes, changes in the bending rigidity in turn lead to changes in the entropic repulsive undulation forces that act between the lamellae and which determine their swelling behavior. We briefly review recent results obtained from theoretical and experimental studies of phospholipid bilayers that clarify the relationship between lipid bilayer in-plane fluctuations (in density and composition), bending rigidity, and repulsive undulation forces. The results discussed derive from computer simulation calculations, field theory, as well as small angle neutron scattering.
Active mesh and neural network pipeline for cell aggregate segmentation

Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology, due to improvements in capacity and accuracy of microscopy techniques. Here we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary duct organoids imaged over 24 hours with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin which implement active meshes deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction. Statement of significance In vitro culture of organ-like structures derived from stem cells, so-called organoids, allows to image tissue morphogenetic processes with high temporal and spatial resolution. Three-dimensional segmentation of cell shape in timelapse movies of these developing organoids is however a significant challenge. In this work, we propose an image analysis pipeline for cell aggregates that combines deep learning with active contour segmentations. This combination offers a flexible and efficient way to segment three-dimensional cell images, which we illustrate with by segmenting datasets of growing mammary gland organoids and mouse embryonic stem cells.
Turbulence in the Interplanetary Medium: Can Discrete Modes Co‐Exist With Turbulence?

Spacecraft observations during the last decade suggest the presence of discrete frequency wave‐like signals embedded in turbulent solar wind fluctuations. A basic plasma physics question immediately arises how a single monochromatic wave mode can survive in the presence of magnetofluid turbulence. Early one‐ and two‐dimensional magnetofluid simulation studies appear to refute the observations by showing that discrete modes are destroyed within fractions of a characteristic nonlinear time scale. Here we readdress the problem using fully‐compressible three‐dimensional magnetofluid simulations. We investigate the persistence of discrete wave‐like spatial structures in the presence of strong low‐frequency turbulence and seek to identify the time scales and other conditions that permit them to remain as identifiable modes in the turbulent medium. We find that discrete modes can survive from one to many nonlinear times, and that the two main factors that determine their survivability is the presence of a mean m.
Interferometric studies of rapid rotators

Abstract Stellar rotation, like stellar mass and metallicity, is a fundamental property of stars. Rapid rotation distorts the stellar photosphere and affects a star's luminosity, abundances and evolution. It is also linked to stellar wind and mass loss. The distortion of the stellar photosphere due to rapid rotation causes the stellar surface brightness and effective temperature to vary with latitude, leading to a bright pole and a dark equator - a phenomenon known as ‘Gravity Darkening’. Thanks to the development of long baseline optical interferometry in recent years, optical interferometers have resolved the elongation of rapidly rotating stars, and have even imaged a few systems for the first time, directly confirming the gravity darkening effect. In this paper, we review the recent interferometric studies of rapid rotators, particularly the imaging results from CHARA-MIRC. These sub-milliarcsecond resolution observations permit the determination of the inclination, the polar and equatorial radius and temperature, as well as the fractional rotation speed of several rapid rotators with unprecedented precision. The modeling also allows the determination of the true effective temperatures and luminosities of these stars, permitting the investigation of their true locations on the HR diagram. Discrepancies from standard models were also found in some measurements, suggesting the requirement of more sophisticated mechanisms such as non-uniform rotation in the model. These observations have demonstrated that optical interferometry is now sufficiently mature to provide valuable constraints and even model-independent images to shed light on the basic physics of stars.
S1000: a better taxonomic name corpus for biomedical information extraction

Motivation The recognition of mentions of species names in text is a critically important task for biomedical text mining. While deep learning-based methods have made great advances in many named entity recognition tasks, results for species name recognition remain poor. We hypothesize that this is primarily due to the lack of appropriate corpora. Results We introduce the S1000 corpus, a comprehensive manual re-annotation and extension of the S800 corpus. We demonstrate that S1000 makes highly accurate recognition of species names possible (F-score=93.1%), both for deep learning and dictionary-based methods. Availability All resources introduced in this study are available under open licenses from https://jensenlab.org/resources/s1000/. The webpage contains links to a Zenodo project and two GitHub repositories associated with the study. Contact sampo.pyysalo@utu.fi, lars.juhl. jensen@cpr.ku. dk Supplementary information Supplementary data are available at Bioinformatics online.
Cysteine-Rich Intestinal Protein 1 is a Novel Surface Marker for Myometrial Stem/Progenitor Cells

Myometrial stem/progenitor cells (MyoSPCs) have been proposed as the cells of origin for uterine fibroids, which are benign tumors that develop in the myometrium of most reproductive age women, but the identity of the MyoSPC has not been well established. We previously identified SUSD2 as a possible MyoSPC marker, but the relatively poor enrichment in stem cell characteristics of SUSD2+ over SUSD2- cells compelled us to find better discerning markers for more rigorous downstream analyses. We combined bulk RNA-seq of SUSD2+/- cells with single cell RNA-seq to identify markers capable of further enriching for MyoSPCs. We observed seven distinct cell clusters within the myometrium, with the vascular myocyte cluster most highly enriched for MyoSPC characteristics and markers, including SUSD2. CRIP1 expression was found highly upregulated in both techniques and was used as a marker to sort CRIP1+/PECAM1- cells that were both enriched for colony forming potential and able to differentiate into mesenchymal lineages, suggesting that CRIP1+/PECAM1- cells could be used to better study the etiology of uterine fibroids.
Highly efficient absorbing boundary conditions for the beam propagation method

Two kinds of absorbing boundary conditions for the finite difference BPM are investigated, namely the operation of Berenger layers and the use of a mere nth-order differential relation with constant coefficients (UABC-n). Practical formulas are given for their implementation and their optimization is discussed. Their efficiency is demonstrated in 2-D test problems where the Hadley technique fails. Berenger layers with 5 to 10 points or 6th-order UABC should be useful for most practical problems. The extra numerical cost in 3-D problems should be either near negligible (UABC) or quite moderate (Berenger layers).
Si-rich/SiO2 nanostructured multilayers by reactive magnetron sputtering

Silicon-rich (SR)/SiO2 multilayered systems were produced by reactive magnetron sputtering, through an approach based on the ability of hydrogen, when alternatively mixed to the argon of the plasma, to reduce the oxygen originated from the SiO2 target. Optimum values of both hydrogen partial pressure (45 mTorr) and deposition temperature (500 °C) have led to the highest incorporation of Si in the SR layer which crystallizes after annealing. The SR/SiO2 superlattices grown with such conditions showed that the size of the Si nanocrystals is limited by the thickness of the SR layer. Considering the difference observed between the photoluminescence peak position and the predicted band gap for Si nanocrystals, the results suggest that we deal with a quantum-size confinement assisted by a Si–O vibration located at the interface.
Computation of Leading Eigenvalues and Eigenvectors in the Linearized Navier-Stokes Equations using Krylov Subspace Method

In the hydrodynamic stability analysis of fluid flow, an eigenproblem with a large asymmetric matrix can be produced by the linearized Navier-Stokes equations. To find the leading eigenvalues and eigenvectors is one of the key issues in the stability analysis. In this study, the Krylov subspace method has been used to develop a general algorithm and then solve the eigenproblem with a large asymmetric matrix in the linearized Navier-Stokes equations. The Krylov subspace method is to construct a set of linearly independent vectors from the linearized equations in which the leading eigenvalues with the largest real parts are included, and orthonormalize the vectors. Then, the Arnoldi's method is used for extracting the desirable eigenvalues with the largest real parts. The advantage of the present method is its ability to calculate a given number of leading eigenvalues including the largest real parts, and corresponding eigenvectors in a large asymmetric matrix. Therefore, this algorithm is general for the hydrodynamic stability analysis with asymmetric matrices resulting from the linearized Navier-Stokes equations. This method has been verified and applied to detect the critical Reynolds number of fluid flow past a circular cylinder. The finite element method with equal-order interpolation was used in this application for discretization of the linearized Navier-Stokes equations.
The Cold, Massive Molecular Cloud G216–2.5. III. Infrared Properties

We present results concerning the star-forming activities and dust properties of the unusual giant molecular cloud G216–2.5. As suggested by earlier CO observations, we confirm that this cloud is unusually cold for a GMC and shows no evidence for massive star formation. The average temperature of the dust based on the extended emission at 60 and 100 μm is 22–26 K. We attribute this low value to the lack of substantial internal heating sources. Dust heating in the cloud is probably dominated by the interstellar radiation field. Near-infrared imaging of the IRAS point sources seen toward G216–2.5 reveal at least four sites of low-luminosity, and presumably low-mass, star formation within the cloud. All four sites lie preferentially toward the edge of the cloud. Two small groupings of stars are identified as the near-infrared counterparts to two of the IRAS point sources, and the group members may be similar to T Tauri stars. Somewhat more luminous and massive stars appear to be associated with two other IRAS point sources. Even the most luminous star formation site in G216–2.5 has a far-infrared luminosity of only 300 L☉.
Plasma kinetics mechanisms of an optimized copper vapour laser

An investigation has been made into plasma kinetics mechanisms of an optimized copper vapour laser by applying a genetic algorithm: (1) an impedance resonance of the thyratron takes place when the pulsed laser appears, and the matching between the laser tube and the discharging circuit is well improved by the optimization; (2) both the rapidly rising plasma electron temperature and the increasing electron density pump more particles so as to populate more quickly the upper laser levels during the discharging pulsed period after the optimization; (3) maintenance of a high wall temperature can increase the lasing population.