Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonparametric approximation of conditional expectation operators

Given the joint distribution of two random variables $X,Y$ on some second countable locally compact Hausdorff space, we investigate the statistical approximation of the $L^2$-operator defined by $[Pf](x) := \mathbb{E}[ f(Y) \mid X = x ]$ under minimal assumptions. By modifying its domain, we prove that $P$ can be arbitrarily well approximated in operator norm by Hilbert-Schmidt operators acting on a reproducing kernel Hilbert space. This fact allows to estimate $P$ uniformly by finite-rank operators over a dense subspace even when $P$ is not compact. In terms of modes of convergence, we thereby obtain the superiority of kernel-based techniques over classically used parametric projection approaches such as Galerkin methods. This also provides a novel perspective on which limiting object the nonparametric estimate of $P$ converges to. As an application, we show that these results are particularly important for a large family of spectral analysis techniques for Markov transition operators. Our investigation also gives a new asymptotic perspective on the so-called kernel conditional mean embedding, which is the theoretical foundation of a wide variety of techniques in kernel-based nonparametric inference.
Global Context Networks

The non-local network (NLNet) presents a pioneering approach for capturing long-range dependencies within an image, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by the non-local network are almost the same for different query positions. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further replace the one-layer transformation function of the non-local block by a two-layer bottleneck, which further reduces the parameter number considerably. The resulting network element, called the global context (GC) block, effectively models global context in a lightweight manner, allowing it to be applied at multiple layers of a backbone network to form a global context network (GCNet). Experiments show that GCNet generally outperforms NLNet on major benchmarks for various recognition tasks. The code and network configurations are available at https://github.com/xvjiarui/GCNet.
Generalization in portfolio-based algorithm selection

Portfolio-based algorithm selection has seen tremendous practical success over the past two decades. This algorithm configuration procedure works by first selecting a portfolio of diverse algorithm parameter settings, and then, on a given problem instance, using an algorithm selector to choose a parameter setting from the portfolio with strong predicted performance. Oftentimes, both the portfolio and the algorithm selector are chosen using a training set of typical problem instances from the application domain at hand. In this paper, we provide the first provable guarantees for portfolio-based algorithm selection. We analyze how large the training set should be to ensure that the resulting algorithm selector's average performance over the training set is close to its future (expected) performance. This involves analyzing three key reasons why these two quantities may diverge: 1) the learning-theoretic complexity of the algorithm selector, 2) the size of the portfolio, and 3) the learning-theoretic complexity of the algorithm's performance as a function of its parameters. We introduce an end-to-end learning-theoretic analysis of the portfolio construction and algorithm selection together. We prove that if the portfolio is large, overfitting is inevitable, even with an extremely simple algorithm selector. With experiments, we illustrate a tradeoff exposed by our theoretical analysis: as we increase the portfolio size, we can hope to include a well-suited parameter setting for every possible problem instance, but it becomes impossible to avoid overfitting.
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding

Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks. A promising approach is to use contrastive learning to learn a latent space where features are close for similar data samples and far apart for dissimilar ones. This approach has demonstrated tremendous success for pretraining both image and point cloud feature extractors, but it has been barely investigated for multi-modal RGB-D scans, especially with the goal of facilitating high-level scene understanding. To solve this problem, we propose contrasting"pairs of point-pixel pairs", where positives include pairs of RGB-D points in correspondence, and negatives include pairs where one of the two modalities has been disturbed and/or the two RGB-D points are not in correspondence. This provides extra flexibility in making hard negatives and helps networks to learn features from both modalities, not just the more discriminating one of the two. Experiments show that this proposed approach yields better performance on three large-scale RGB-D scene understanding benchmarks (ScanNet, SUN RGB-D, and 3RScan) than previous pretraining approaches.
Quantum tomography of pure states with projective measurements generated by MUBs and SIC-POVM

The article undertakes the problem of pure state estimation based on projective measurements. Two generic frames for qubit tomography are considered – one defined by the vectors from the mutually unbiased bases (MUBs) and the other composed of the elements of the SIC-POVM. Both frames are combined with the method of least squares in order to reconstruct a sample of input qubits. The accuracy of each frame is quantified by the average fidelity and purity. The efficiency of the frames is compared and discussed. The method can be generalized to higher-dimensional states and transferred to other fields where the problem of complex vectors reconstruction appears.
Resolving the nonequilibrium Kondo singlet in energy- and position-space using quantum measurements

The Kondo effect, a hallmark of strong correlation physics, is characterized by the formation of an extended cloud of singlet states around magnetic impurities at low temperatures. While many implications of the Kondo cloud's existence have been verified, the existence of the singlet cloud itself has not been directly demonstrated. We suggest a route for such a demonstration by considering an observable that has no classical analog, but is still experimentally measurable: ``singlet weights'', or projections onto particular entangled two-particle states. Using approximate theoretical arguments, we show that it is possible to construct highly specific energy- and position-resolved probes of Kondo correlations. Furthermore, we consider a quantum transport setup that can be driven away from equilibrium by a bias voltage. There, we show that singlet weights are enhanced by voltage even as the Kondo effect is weakened by it. This exposes a patently nonequilibrium mechanism for the generation of Kondo-like entanglement that is inherently different from its equilibrium counterpart.
Objective Class-based Micro-Expression Recognition through Simultaneous Action Unit Detection and Feature Aggregation

Micro-expression recognition (MER) has attracted lots of researchers' attention due to its potential value in many practical applications. In this paper, we investigate Micro-Expression Recognition (MER) is a challenging task as the subtle changes occur over different action regions of a face. Changes in facial action regions are formed as Action Units (AUs), and AUs in micro-expressions can be seen as the actors in cooperative group activities. In this paper, we propose a novel deep neural network model for objective class-based MER, which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks (GCN). Specifically, we propose two new strategies in our AU detection module for more effective AU feature learning: the attention mechanism and the balanced detection loss function. With those two strategies, features are learned for all the AUs in a unified model, eliminating the error-prune landmark detection process and tedious separate training for each AU. Moreover, our model incorporates a tailored objective class-based AU knowledge-graph, which facilitates the GCN to aggregate the AU-level features into a micro-expression-level feature representation. Extensive experiments on two tasks in MEGC 2018 show that our approach significantly outperforms the current state-of-the-arts in MER. Additionally, we also report our single model-based micro-expression AU detection results.
Energy scales in 
4f1
 delafossite magnets: Crystal-field splittings larger than the strength of spin-orbit coupling in 
KCeO2

Ytterbium-based delafossites with effective S̃ = 1/2 moments are intensively investigated as candidates for quantum spin-liquid ground states. While the synthesis of related cerium compounds has also been reported, many important details concerning their crystal, electronic, and magnetic structures are unclear. Here we analyze the S̃ = 1/2 system KCeO2, combining complementary theoretical methods. The lattice geometry was optimized and the band structure investigated using density functional theory extended to the level of a GGA+U calculation in order to reproduce the correct insulating behavior. The Ce 4f states were then analysed in more detail with the help of ab initio wave-function-based computations. Unusually large effective crystal-field splittings of up to 320 meV are predicted, which puts KCeO2 in the strong field coupling regime. Our results reveal a subtle interplay between ligand-cage electrostatics and the trigonal field generated by the extended crystalline surroundings, relevant in the context of recent studies on tunning the nature of the ground-state wave-function in 4f triangular-lattice and pyrochlore compounds. It also makes KCeO2 an interesting model system in relation to the effect of large crystal-field splittings on the anisotropy of intersite exchange in spin-orbit coupled quantum magnets.
Profiles of learner readers and their early literacy skills and environmental predictors: a large-scale longitudinal study from preschool to grade 1

As part of the French national cohort ELFE, a two-year longitudinal study was conducted and children’s literacy skills were assessed twice (N = 2,767), namely at the end of preschool (t1) and at the end of Grade 1 (t2). At t1, letter-name knowledge (LNK), phonological skills (PhoSk) and vocabulary were assessed and at t2, phonemic segmentation, word reading, listening and reading comprehension. Latent profile analyses conducted at t2 resulted in five profiles, ranging from good readers (48.8%), to average readers above the mean (16.2%), readers with very low phonological awareness skill (8.2%), poor readers with low reading comprehension skill (19.4%), and poor readers with general difficulties (7.4%). Two early literacy skills (LNK and PhoSk) and two environmental variables (parents’ educational level and household income) constituted good predictors of membership of a profile. However, home literacy practices also predicted membership of the good reader/poor reader profiles, albeit at a lower level.
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets

Purpose: Lesion segmentation in medical imaging is key to evaluating treatment response. We have recently shown that reinforcement learning can be applied to radiological images for lesion localization. Furthermore, we demonstrated that reinforcement learning addresses important limitations of supervised deep learning; namely, it can eliminate the requirement for large amounts of annotated training data and can provide valuable intuition lacking in supervised approaches. However, we did not address the fundamental task of lesion/structure-of-interest segmentation. Here we introduce a method combining unsupervised deep learning clustering with reinforcement learning to segment brain lesions on MRI. Materials and Methods: We initially clustered images using unsupervised deep learning clustering to generate candidate lesion masks for each MRI image. The user then selected the best mask for each of 10 training images. We then trained a reinforcement learning algorithm to select the masks. We tested the corresponding trained deep Q network on a separate testing set of 10 images. For comparison, we also trained and tested a U-net supervised deep learning network on the same set of training/testing images. Results: Whereas the supervised approach quickly overfit the training data and predictably performed poorly on the testing set (16% average Dice score), the unsupervised deep clustering and reinforcement learning achieved an average Dice score of 83%. Conclusion: We have demonstrated a proof-of-principle application of unsupervised deep clustering and reinforcement learning to segment brain tumors. The approach represents human-allied AI that requires minimal input from the radiologist without the need for hand-traced annotation.
SubICap: Towards Subword-informed Image Captioning

Existing Image Captioning (IC) systems model words as atomic units in captions and are unable to exploit the structural information in the words. This makes representation of rare words very difficult and out-of-vocabulary words impossible. Moreover, to avoid computational complexity, existing IC models operate over a modest sized vocabulary of frequent words, such that the identity of rare words is lost. In this work we address this common limitation of IC systems in dealing with rare words in the corpora. We decompose words into smaller constituent units ‘subwords’ and represent captions as a sequence of subwords instead of words. This helps represent all words in the corpora using a significantly lower subword vocabulary, leading to better parameter learning. Using subword language modeling, our captioning system improves various metric scores, with a training vocabulary size approximately 90% less than the baseline and various state-of-the-art word-level models. Our quantitative and qualitative results and analysis signify the efficacy of our proposed approach.
Schlafen4+-MDSC in Helicobacter-induced gastric metaplasia reveals role for GTPases

Introduction MDSCs express SCHLAFEN 4 (SLFN4) in Helicobacter-infected stomachs coincident with spasmolytic polypeptide-expressing metaplasia (SPEM), a precursor of gastric cancer. We aimed to characterize SLFN4+ cell identity and the role of Slfn4 in these cells. Methods Single-cell RNA sequencing was performed on immune cells sorted from PBMCs and stomachs prepared from uninfected and 6-month H. felis-infected mice. Knockdown of Slfn4 by siRNA or PDE5/6 inhibition by sildenafil were performed in vitro. Intracellular ATP/GTP levels and GTPase activity of immunoprecipitated Slfn4 complexes were measured using the GTPase-Glo assay kit. The intracellular level of ROS was quantified by the DCF-DA fluorescent staining, and apoptosis was determined by cleaved Caspase-3 and Annexin V expression. Gli1CreERT2 x Slfn4 fl/fl mice were generated and infected with H. felis. Sildenafil was administered twice over 2 weeks by gavaging H. felis infected mice ~4 months after inoculation once SPEM had developed. Results Slfn4 was highly induced in both monocytic and granulocytic MDSCs from infected stomachs. Both Slfn4 +-MDSC populations exhibited strong transcriptional signatures for type-I interferon responsive GTPases and exhibited T cell suppressor function. SLFN4-containing protein complexes immunoprecipitated from myeloid cell cultures treated with IFNa exhibited GTPase activity. Knocking down Slfn4 or PDE5/6 inhibition with sildenafil blocked IFNa induction of GTP, SLFN4 and NOS2. Moreover, IFNa induction of Slfn +-MDSC function was inhibited by inducing their reactive oxygen species (ROS) production and apoptosis through protein kinase G activation. Accordingly, in vivo disruption of Slfn4 in Gli1CreERT2 x Slfn4 fl/fl mice or pharmacologic inhibition by sildenafil after Helicobacter infection also suppressed SLFN4 and NOS2, reversed T cell suppression and mitigated SPEM development. Conclusion Taken together, SLFN4 regulates the activity of the GTPase pathway in MDSCs and precludes these cells from succumbing to the massive ROS generation when they acquire MDSC function.
Upper Confidence Bounds for Combining Stochastic Bandits

We provide a simple method to combine stochastic bandit algorithms. Our approach is based on a"meta-UCB"procedure that treats each of $N$ individual bandit algorithms as arms in a higher-level $N$-armed bandit problem that we solve with a variant of the classic UCB algorithm. Our final regret depends only on the regret of the base algorithm with the best regret in hindsight. This approach provides an easy and intuitive alternative strategy to the CORRAL algorithm for adversarial bandits, without requiring the stability conditions imposed by CORRAL on the base algorithms. Our results match lower bounds in several settings, and we provide empirical validation of our algorithm on misspecified linear bandit and model selection problems.
Rheological Behavior of Coating Colors: Influence of Thickener

In this work we have investigated the effect of rheology modifiers on the rheological properties of concentrated (65 solids mass%) kaolin suspensions and six different coating colors. Measurements have been performed on kaolin-based suspensions, without rheology modifier and with either a classic cellulose thickener or associative polymers. It was noticed that suspensions containing a thickener had a much larger viscosity and storage and loss moduli than suspensions containing no rheology modifier. The enhancement of the rheological properties was found to be much more important for the suspensions containing the associative polymer. These observations have been related to steric stabilization of the suspensions, and to the occurrence of entanglements and bridging when the associative polymer was used as the thickener. The influence of the thickener on the rheological properties of the coating colors was found to be similar to that observed for the concentrated kaolin suspensions. Dans ce travail nous avons etudie l'effet des modificateurs de rheologie sur les proprietes rheologiques de suspensions concentrees de kaolin (concentration massique en solide de 65 %) et de six sauces de couchage industrielles. Des mesures ont ete effectuees sur les suspensions de kaolin, sans modificateur de rheologie et avec un epaississant classique de cellulose ou des polymeres associatifs. Nous avons pu observer que les suspensions contenant un epaississant ont eu une viscosite et des modules de conservation et de perte beaucoup plus importants que les suspensions ne contenant aucun modificateur de rheologie. L'augmentation des proprietes rheologiques s'est averee beaucoup plus importante pour les suspensions contenant le polymere associatif. Ces observations ont ete liees a la stabilisation sterique des suspensions et a l'occurrence d'enchevetrements et de ponts lorsque le polymere associatif est employe comme epaississant. L'influence de l'epaississant sur les proprietes rheologiques des sauces de couchage industrielles s'est averee semblable a celle observee pour les suspensions concentrees de kaolin.
Majorizing Measures for the Optimizer

The theory of majorizing measures, extensively developed by Fernique, Talagrand and many others, provides one of the most general frameworks for controlling the behavior of stochastic processes. In particular, it can be applied to derive quantitative bounds on the expected suprema and the degree of continuity of sample paths for many processes. One of the crowning achievements of the theory is Talagrand's tight alternative characterization of the suprema of Gaussian processes in terms of majorizing measures. The proof of this theorem was difficult, and thus considerable effort was put into the task of developing both shorter and easier to understand proofs. A major reason for this difficulty was considered to be theory of majorizing measures itself, which had the reputation of being opaque and mysterious. As a consequence, most recent treatments of the theory (including by Talagrand himself) have eschewed the use of majorizing measures in favor of a purely combinatorial approach (the generic chaining) where objects based on sequences of partitions provide roughly matching upper and lower bounds on the desired expected supremum. In this paper, we return to majorizing measures as a primary object of study, and give a viewpoint that we think is natural and clarifying from an optimization perspective. As our main contribution, we give an algorithmic proof of the majorizing measures theorem based on two parts: (1) We make the simple (but apparently new) observation that finding the best majorizing measure can be cast as a convex program. This also allows for efficiently computing the measure using off-the-shelf methods from convex optimization. (2) We obtain tree-based upper and lower bound certificates by rounding, in a series of steps, the primal and dual solutions to this convex program. [. ]
EDN: Salient Object Detection via Extremely-Downsampled Network

Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and discovering fine details, respectively. However, most efforts are devoted to low-level feature learning by fusing multi-scale features or enhancing boundary representations. High-level features, which although have long proven effective for many other tasks, yet have been barely studied for SOD. In this paper, we tap into this gap and show that enhancing high-level features is essential for SOD as well. To this end, we introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image, leading to accurate salient object localization. To accomplish better multi-level feature fusion, we construct the Scale-Correlated Pyramid Convolution (SCPC) to build an elegant decoder for recovering object details from the above extreme downsampling. Extensive experiments demonstrate that EDN achieves state-of-the-art performance with real-time speed. Our efficient EDN-Lite also achieves competitive performance with a speed of 316fps. Hence, this work is expected to spark some new thinking in SOD. Code is available at https://github.com/yuhuan-wu/EDN.
Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.