Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal Intervention for Leveraging Popularity Bias in Recommendation

Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method perspective, collaborative filtering methods are prone to amplify the bias by over-recommending popular items. It is undoubtedly critical to consider popularity bias in recommender systems, and existing work mainly eliminates the bias effect with propensity-based unbiased learning or causal embeddings. However, we argue that not all biases in the data are bad, \ie some items demonstrate higher popularity because of their better intrinsic quality. Blindly pursuing unbiased learning may remove the beneficial patterns in the data, degrading the recommendation accuracy and user satisfaction. This work studies an unexplored problem in recommendation --- how to leverage popularity bias to improve the recommendation accuracy. The key lies in two aspects: how to remove the bad impact of popularity bias during training, and how to inject the desired popularity bias in the inference stage that generates top-K recommendations. This questions the causal mechanism of the recommendation generation process. Along this line, we find that item popularity plays the role ofconfounder between the exposed items and the observed interactions, causing the bad effect of bias amplification. To achieve our goal, we propose a new training and inference paradigm for recommendation named Popularity-bias Deconfounding and Adjusting (PDA). It removes the confounding popularity bias in model training and adjusts the recommendation score with desired popularity bias via causal intervention. We demonstrate the new paradigm on the latent factor model and perform extensive experiments on three real-world datasets from Kwai, Douban, and Tencent. Empirical studies validate that the deconfounded training is helpful to discover user real interests and the inference adjustment with popularity bias could further improve the recommendation accuracy. We release our code at https://github.com/zyang1580/PDA.
Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks

Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate models is to provide metrics of mean and extreme climate changes, particularly under these alternative future scenarios, as these quantities drive the impacts of climate on society and natural systems. Because of the need to explore a wide range of alternative scenarios and other sources of uncertainties in a computationally efficient manner, climate models can only take us so far, as they require significant computational resources, especially when attempting to characterize extreme events, which are rare and thus demand long and numerous simulations in order to accurately represent their changing statistics. Here we use deep learning in a proof of concept that lays the foundation for emulating global climate model output for different scenarios. We train two"loosely conditioned"Generative Adversarial Networks (GANs) that emulate daily precipitation output from a fully coupled Earth system model: one GAN modeling Fall-Winter behavior and the other Spring-Summer. Our GANs are trained to produce spatiotemporal samples: 32 days of precipitation over a 64x128 regular grid discretizing the globe. We evaluate the generator with a set of related performance metrics based upon KL divergence, and find the generated samples to be nearly as well matched to the test data as the validation data is to test. We also find the generated samples to accurately estimate the mean number of dry days and mean longest dry spell in the 32 day samples. Our trained GANs can rapidly generate numerous realizations at a vastly reduced computational expense, compared to large ensembles of climate models, which greatly aids in estimating the statistics of extreme events.
Benchmarking a Novel Efficient Numerical Method for Localized 1D Fermi-Hubbard Systems on a Quantum Simulator

Quantum simulators have made a remarkable progress towards exploring the dynamics of many-body systems, many of which offer a formidable challenge to both theoretical and numerical methods. While state-of-the-art quantum simulators are in principle able to simulate quantum dynamics well outside the domain of classical computers, they are noisy and limited in the variability of the initial state of the dynamics and the observables that can be measured. Despite these limitations, here we show that such a quantum simulator can be used to in-effect solve for the dynamics of a many-body system. We develop an efficient numerical technique that facilitates classical simulations in regimes not accessible to exact calculations or other established numerical techniques. The method is based on approximations that are well suited to describe localized one-dimensional Fermi-Hubbard systems. Since this new method does not have an error estimate and the approximations do not hold in general, we use a neutral-atom Fermi-Hubbard quantum simulator with $L_{\text{exp}}\simeq290$ lattice sites to benchmark its performance in terms of accuracy and convergence for evolution times up to $700$ tunnelling times. We then use these approximations in order to derive a simple prediction of the behaviour of interacting Bloch oscillations for spin-imbalanced Fermi-Hubbard systems, which we show to be in quantitative agreement with experimental results. Finally, we demonstrate that the convergence of our method is the slowest when the entanglement depth developed in the many-body system we consider is neither too small nor too large. This represents a promising regime for near-term applications of quantum simulators.
Lipschitz Stability and Hadamard Directional Differentiability for Elliptic and Parabolic Obstacle-Type Quasi-variational Inequalities

This paper is concerned with the sensitivity analysis of a class of parameterized fixed-point problems that arise in the context of obstacle-type quasi-variational inequalities. We prove that, if the operators in the considered fixed-point equation satisfy a positive superhomogeneity condition, then the maximal and minimal element of the solution set of the problem depend locally Lipschitz continuously on the involved parameters. We further show that, if certain concavity conditions hold, then the maximal solution mapping is Hadamard directionally differentiable and its directional derivatives are precisely the minimal solutions of suitably defined linearized fixed-point equations. In contrast to prior results, our analysis requires neither a Dirichlet space structure, nor restrictive assumptions on the mapping behavior and regularity of the involved operators, nor sign conditions on the directions that are considered in the directional derivatives. Our approach further covers the elliptic and parabolic setting simultaneously and also yields Hadamard directional differentiability results in situations in which the solution set of the fixed-point equation is a continuum and a characterization of directional derivatives via linearized auxiliary problems is provably impossible. To illustrate that our results can be used to study interesting problems arising in practice, we apply them to establish the Hadamard directional differentiability of the solution operator of a nonlinear elliptic quasi-variational inequality, which emerges in impulse control and in which the obstacle mapping is obtained by taking essential infima over certain parts of the underlying domain, and of the solution mapping of a parabolic quasi-variational inequality, which involves boundary controls and in which the state-to-obstacle relationship is described by a partial differential equation.
Multi-scale Radio and X-Ray Structure of the High-redshift Quasar PMN J0909+0354

The high-redshift quasar PMN J0909+0354 (z = 3.288) is known to have a parsec-scale compact jet structure, based on global 5 GHz very long baseline interferometry (VLBI) observations performed in 1992. Its kiloparsec-scale structure was studied with the Karl G. Jansky Very Large Array (VLA) in the radio and the Chandra space telescope in X-rays. Apart from the north-northwestern jet component seen in both the VLA and Chandra images at 2. ″3 separation from the core, there is another X-ray feature at 6. ″48 in the northeastern (NE) direction. To uncover more details and possible structural changes in the inner jet, we conducted new observations at 5 GHz using the European VLBI Network in 2019. These data confirm the northward direction of the one-sided inner jet already suspected from the 1992 observations. A compact core and multiple jet components were identified that can be traced up to ∼0.25 kpc projected distance toward the north, where the structure becomes more and more diffuse. A comparison with arcsecond-resolution imaging with the VLA shows that the radio jet bends by ∼30° between the two scales. The direction of the parsec-scale jet as well as the faint optical counterpart found for the newly detected X-ray point source (NE) favors the nature of the latter as being a background or foreground object in the field of view. However, the extended (∼160 kpc) emission around the positions of the quasar core and NE detected by the Wide-field Infrared Survey Explorer in the mid-infrared might suggest a physical interaction of the two objects.
Why Does Multi-Epoch Training Help?

Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in each updating step, and good performance. Standard excess risk bounds show that SGD only needs to take one pass over the training data and more passes could not help to improve the performance. Empirically, it has been observed that SGD taking more than one pass over the training data (multi-pass SGD) has much better excess risk bound performance than the SGD only taking one pass over the training data (one-pass SGD). However, it is not very clear that how to explain this phenomenon in theory. In this paper, we provide some theoretical evidences for explaining why multiple passes over the training data can help improve performance under certain circumstance. Specifically, we consider smooth risk minimization problems whose objective function is non-convex least squared loss. Under PolyakLojasiewicz (PL) condition, we establish faster convergence rate of excess risk bound for multi-pass SGD than that for one-pass SGD.
Relation-aware Hierarchical Attention Framework for Video Question Answering

Video Question Answering (VideoQA) is a challenging video understanding task since it requires a deep understanding of both question and video. Previous studies mainly focus on extracting sophisticated visual and language embeddings, fusing them by delicate hand-crafted networks. However, the relevance of different frames, objects, and modalities to the question are varied along with the time, which is ignored in most of existing methods. Lacking understanding of the the dynamic relationships and interactions among objects brings a great challenge to VideoQA task. To address this problem, we propose a novel Relation-aware Hierarchical Attention (RHA) framework to learn both the static and dynamic relations of the objects in videos. In particular, videos and questions are embedded by pre-trained models firstly to obtain the visual and textual features. Then a graph-based relation encoder is utilized to extract the static relationship between visual objects. To capture the dynamic changes of multimodal objects in different video frames, we consider the temporal, spatial, and semantic relations, and fuse the multimodal features by hierarchical attention mechanism to predict the answer. We conduct extensive experiments on a large scale VideoQA dataset, and the experimental results demonstrate that our RHA outperforms the state-of-the-art methods.
Graph Learning based Recommender Systems: A Review

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics and popularity for Recommender Systems (RS). Differently from other approaches, including content based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract knowledge from graphs to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area.
Delocalization of quantum information in long-range interacting systems

We investigate the delocalization of quantum information in the nonequilibrium dynamics of the XY spin chain with asymptotically decaying interactions∼ 1/r. As a figure of merit, we employ the tripartite mutual information (TMI), whose sign indicates if quantum information is predominantly shared globally. Interestingly, the sign of the TMI distinguishes regimes of the exponent α that are known for different behaviour of information propagation. While an effective causal region bounds the propagation of information, if interactions decay sufficiently fast, this information is mainly delocalized, which leads to the necessity of global measurements. Furthermore, the results indicate that mutual information is monogamous for all possible partitionings in this case, implying that quantum entanglement is the dominant correlation. If interactions decay sufficiently slow, though information can propagate (quasi-)instantaneously, it is mainly accessible by local measurements at early times. Furthermore, it takes some finite time until correlations start to become monogamous, which suggests that entanglement is not the dominant correlation at early times. Our findings give new insights into the dynamics, and structure of quantum information in many-body systems with long-range interactions, and might get verified on state-of-the-art experimental platforms.
Rayleigh-Benard convection in a nonuniformly rotating electrically conductive medium in an external spiral magnetic field

The research is devoted to the stability of convective flow in a nonuniformly rotating layer of an electrically conducting fluid in a spiral magnetic field. The stationary and oscillatory modes of magnetic convection are considered depending on the profile of the angular rotation velocity (Rossby number $\textrm{Ro}$) and on the profile of the external azimuthal magnetic field (magnetic Rossby number $\textrm{Rb}$). The nonlinear dynamic system of Lorentz type equations is obtained by using the Galerkin method. Numerical analysis of these equations has shown the presence of chaotic behavior of convective flows. The criteria of the occurrence of chaotic movements are found. It depends on the parameters of convection: dimensionless numbers of Rayleigh $\textrm{Ra}$, Chandrasekhar $\textrm{Q}$, Taylor $\textrm{Ta}$, and external azimuthal magnetic field with the Rossby magnetic number $\textrm{Rb}=-1$ for Rayleigh $(\textrm{Ro}=-1)$ and Kepler $(\textrm{Ro}=-3/4)$ profiles of the angular rotation velocity of the medium.
The Wald–Zoupas prescription for asymptotic charges at null infinity in general relativity

We use the formalism developed by Wald and Zoupas to derive explicit covariant expressions for the charges and fluxes associated with the Bondi–Metzner–Sachs symmetries at null infinity in asymptotically flat spacetimes in vacuum general relativity. Our expressions hold in non-stationary regions of null infinity, are local and covariant, conformally-invariant, and are independent of the choice of foliation of null infinity and of the chosen extension of the symmetries away from null infinity. While similar expressions have appeared previously in the literature in Bondi–Sachs coordinates (to which we compare our own), such a choice of coordinates obscures these properties. Our covariant expressions can be used to obtain charge formulae in any choice of coordinates at null infinity. We also include detailed comparisons with other expressions for the charges and fluxes that have appeared in the literature: the Ashtekar–Streubel flux formula, the Komar formulae, and the linkage and twistor charge formulae. Such comparisons are easier to perform using our explicit expressions, instead of those which appear in the original work by Wald and Zoupas.
Efficient Lightweight 3D-CNN using Frame Skipping and Contrast Enhancement for Facial Macro- and Micro-expression Spotting

Micro-expression spotting is the preliminary step for any micro-expression related analysis to avoid excessive false positives. We propose an efﬁcient lightweight macro-and micro-expression spotting method which takes advantage of the duration differences of macro-and micro-expressions. Using effective frame skips, local contrast normalisation, depthwise separable convolutions and residual connections, we construct Efﬁcient Lightweight 3D-Convolutional Network using Frame Skipping and Contrast Enhancement (EL-FACE) for the micro-expression spotting task. Our model achieves state-of-the-art performance in SAMM Long Videos and remained competitive in the CAS(ME) 2 dataset.
Nonclassical kernels in continuous-variable systems

Kernel methods are ubiquitous in classical machine learning, and their formal similarity with quantum mechanics has recently been established. Understanding the properties of the nonclassical kernel functions can provide us with valuable information about the potential advantage of quantum machine learning. In this paper, we derive a nonclassicality witness for kernel functions in continuous-variable systems. We discuss the implication of our witness in terms of the resulting distribution of data points in the feature space.
Dynamical Origin for Winner-Take-All Competition in A Biological Network of The Hippocampal Dentate Gyrus

We consider a biological network of the hippocampal dentate gyrus (DG). The DG is a pre-processor for pattern separation which facilitates pattern storage and retrieval in the CA3 area of the hippocampus. The main encoding cells in the DG are the granule cells (GCs) which receive the input from the entorhinal cortex (EC) and send their output to the CA3. We note that the activation degree of GCs is so low (~ 5%). This sparsity has been thought to enhance the pattern separation. We investigate the dynamical origin for winner-take-all (WTA) competition which leads to sparse activation of the GCs. The whole GCs are grouped into lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell (BC) along with excitatory (E) GCs. There are three kinds of external inputs into the GCs; the direct excitatory EC input, the indirect inhibitory EC input, mediated by the HIPP (hilar perforant path-associated) cells, and the excitatory input from the hilar mossy cells (MCs). The firing activities of the GCs are determined via competition between the external E and I inputs. The E-I conductance ratio (given by the time average of the external E to I conductances) may represents well the degree of such external E-I input competition. It is thus found that GCs become active when their is larger than a threshold , and then the mean firing rates of the active GCs are strongly correlated with . In each GC cluster, the feedback inhibition of the BC may select the winner GCs. GCs with larger than the threshold survive, and they become winners; all the other GCs with smaller become silent. In this way, WTA competition occurs via competition between the firing activity of the GCs and the feedback inhibition from the BC in each GC cluster. In this case, the hilar MCs are also found to play an essential role of enhancing the WTA competition in each GC cluster by exciting both the GCs and the BC. PACS numbers: 87.19. lj, 87.19. lm, 87.19. lv
Matters Arising in --"Plasmon-driven carbon-fluorine (C(sp^$3$)-F) bond activation with mechanistic insights into hot-carrier-mediated pathways"

In a recent paper [1], Robatjazi et al. demonstrate hydrodefluorination on Al nanocrystals decorated by Pd islands under illumination and under external heating. They conclude that photocatalysis accomplishes the desired transformation CH3F + D2 −−→ CH3D + DF efficiently and selectively due to “hot” electrons, as evidenced by an illumination-induced reduction of the activation energy. Although some of the problems identified in prior work by the same group [2, 3] have been addressed , scrutiny of the data in [1] raises doubts about both the methodology and the central conclusions. First, we show that the thermal control experiments in [1] do not separate thermal from ”hot electron” contributions, and therefore any conclusions drawn from these experiments are invalid. We then show that an improved but still non-ideal thermal control implies that the activation energy of the reaction does not change, and that an independent purely thermal calculation (based solely on the sample parameters provided in the original manuscript [1]) explains the measured data perfectly. For the sake of completeness, we also address technical problems in the calibration of the thermal camera, an unjustifiable disqualification of some of the measured data, as well as concerning aspects of the rest of the main results, including the mass spectrometry approach used to investigate the selectivity of the reaction, and claims about the stoichiometry and reaction order. All this shows that the burden of proof for involvement of hot electrons has not been met.
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On

We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions. In contrast to existing methods that require an undesirable postprocessing step to fix garment-body interpenetrations at test time, our approach directly outputs 3D garment configurations that do not collide with the underlying body. Key to our success is a new canonical space for garments that removes pose-and-shape deformations already captured by a new diffused human body model, which extrapolates body surface properties such as skinning weights and blendshapes to any 3D point. We leverage this representation to train a generative model with a novel self-supervised collision term that learns to reliably solve garment-body interpenetrations. We extensively evaluate and compare our results with recently proposed data-driven methods, and show that our method is the first to successfully address garment-body contact in unseen body shapes and motions, without compromising realism and detail.