Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Intents behind Interactions with Knowledge Graph for Recommendation

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity. In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Technically, we model each intent as an attentive combination of KG relations, encouraging the independence of different intents for better model capability and interpretability. Furthermore, we devise a new information aggregation scheme for GNN, which recursively integrates the relation sequences of long-range connectivity (i.e., relational paths). This scheme allows us to distill useful information about user intents and encode them into the representations of users and items. Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-of-the-art methods like KGAT [41], KGNN-LS [38], and CKAN [47]. Further analyses show that KGIN offers interpretable explanations for predictions by identifying influential intents and relational paths. The implementations are available at https://github.com/huangtinglin/Knowledge_Graph_based_Intent_Network.
The Peculiar X-Ray Transient Swift J0840.7−3516: An Unusual Low-mass X-Ray Binary or a Tidal Disruption Event?

We report on the X-ray properties of the new transient Swift J0840.7−3516, discovered with Swift/BAT in 2020 February, using extensive data from Swift, MAXI, NICER, and NuSTAR. The source flux increased for ∼103 s after the discovery, decayed rapidly over ∼5 orders of magnitude in five days, and then remained almost constant over nine months. Large-amplitude short-term variations on timescales of 1–104 s were observed throughout the decay. In the initial flux rise, the source showed a hard power-law-shaped spectrum with a photon index of ∼1.0 extending up to ∼30 keV, above which an exponential cutoff was present. The photon index increased in the following rapid decay and became ∼2 at the end of the decay. A spectral absorption feature at 3–4 keV was detected in the decay. It is not straightforward to explain all the observed properties by any known class of X-ray sources. We discuss the possible nature of the source, including a Galactic low-mass X-ray binary with multiple extreme properties and a tidal disruption event by a supermassive black hole or a Galactic neutron star.
Direct Flux Gradient Approximation to Close Moment Model for Kinetic Equations

To close the moment model deduced from kinetic equations, the canonical approach is to provide an approximation to the flux function not able to be depicted by the moments in the reduced model. In this paper, we propose a brand new closure approach with remarkable advantages than the canonical approach. Instead of approximating the flux function, the new approach close the moment model by approximating the flux gradient. Precisely, we approximate the space derivative of the distribution function by an ansatz which is a weighted polynomial, and the derivative of the closing flux is computed by taking the moments of the ansatz. Consequently, the method provides us an improved framework to derive globally hyperbolic moment models, which preserve all those conservative variables in the low order moments. It is shown that the linearized system at the weight function, which is often the local equilibrium, of the moment model deduced by our new approach is automatically coincided with the system deduced from the classical perturbation theory, which can not be satisfied by previous hyperbolic regularization framework. Taking the Boltzmann equation as example, the linearlization of the moment model gives the correct Navier-Stokes-Fourier law same as that the Chapman-Enskog expansion gives. Most existing globally hyperbolic moment models are re-produced by our new approach, and several new models are proposed based on this framework.
Finding Anomalies in Scratch Assignments

In programming education, teachers need to monitor and assess the progress of their students by investigating the code they write. Code quality of programs written in traditional programming languages can be automatically assessed with automated tests, verification tools, or linters. In many cases these approaches rely on some form of manually written formal specification to analyze the given programs. Writing such specifications, however, is hard for teachers, who are often not adequately trained for this task. Furthermore, similar support for popular block-based introductory programming languages like Scratch is lacking. Anomaly detection is an approach to automatically identify deviations of common behavior in datasets without any need for writing a specification. In this paper, we use anomaly detection to automatically find deviations of Scratch code in a classroom setting, where anomalies can represent erroneous code, alternative solutions, or distinguished work. Evaluation on solutions of different programming tasks demonstrates that anomaly detection can successfully be applied to tightly specified as well as open-ended programming tasks.
FastHand: Fast Hand Pose Estimation From A Monocular Camera

—Hand gesture recognition constitutes the initial step in most methods related to human-robot interaction. There are two key challenges in this task. The ﬁrst one corresponds to the difﬁculty of achieving stable and accurate hand landmark predictions in real-world scenarios, while the second to the decreased time of forward inference. In this paper, we propose a fast and accurate framework for hand pose estimation, dubbed as “FastHand”. Using a lightweight encoder-decoder network architecture, we achieve to fulﬁl the requirements of practical applications running on embedded devices. The encoder consists of deep layers with a small number of parameters, while the decoder makes use of spatial location information to obtain more accurate results. The evaluation took place on two publicly available datasets demonstrating the improved performance of the proposed pipeline compared to other state-of-the-art approaches. FastHand offers high accuracy scores while reaching a speed of 25 frames per second on an NVIDIA Jetson TX2 graphics processing unit.
STruD: Truss Decomposition of Simplicial Complexes

A simplicial complex is a generalization of a graph: a collection of n-ary relationships (instead of binary as the edges of a graph), named simplices. In this paper, we develop a new tool to study the structure of simplicial complexes: we generalize the graph notion of truss decomposition to complexes, and show that this more powerful representation gives rise to different properties compared to the graph-based one. This power, however, comes with important computational challenges derived from the combinatorial explosion caused by the downward closure property of complexes. Drawing upon ideas from itemset mining and similarity search, we design a memory-aware algorithm, dubbed STruD, which is able to efficiently compute the truss decomposition of a simplicial complex. STruDadapts its behavior to the amount of available memory by storing intermediate data in a compact way. We then devise a variant that computes directly the n simplices of maximum trussness. By applying STruDto several datasets, we prove its scalability, and provide an analysis of their structure. Finally, we show that the truss decomposition can be seen as a filtration, and as such it can be used to study the persistent homology of a dataset, a method for computing topological features at different spatial resolutions, prominent in Topological Data Analysis.
Highly efficient parallel grand canonical simulations of interstitial-driven diffusion-deformation processes

Absorption of interstitial alloying elements like H, O, C, and N in metals and their continuous relocation and interactions with various microstructural features such as vacancies, dislocations, and grain boundaries have crucial influences on metals’ properties. However, besides limitations in experimental tools in capturing these mechanisms, the inefficiency of numerical tools also inhibits modeling efforts. Here, we present an efficient framework to perform hybrid grand canonical Monte Carlo and molecular dynamics simulations that allow for parallel insertion/deletion of Monte Carlo moves. A new methodology for calculation of the energy difference at trial moves that can be applied to many-body potentials as well as pair ones is a primary feature of our implementation. We study H diffusion in Fe (ferrite phase) and Ni polycrystalline samples to demonstrate the efficiency and scalability of the algorithm and its application. The computational cost of using our framework for half a million atoms is a factor of 250 less than the cost of using existing libraries.
Cooperation and Reputation Dynamics with Reinforcement Learning

Creating incentives for cooperation is a challenge in natural and artificial systems. One potential answer is reputation, whereby agents trade the immediate cost of cooperation for the future benefits of having a good reputation. Game theoretical models have shown that specific social norms can make cooperation stable, but how agents can independently learn to establish effective reputation mechanisms on their own is less understood. We use a simple model of reinforcement learning to show that reputation mechanisms generate two coordination problems: agents need to learn how to coordinate on the meaning of existing reputations and collectively agree on a social norm to assign reputations to others based on their behavior. These coordination problems exhibit multiple equilibria, some of which effectively establish cooperation. When we train agents with a standard Q-learning algorithm in an environment with the presence of reputation mechanisms, convergence to undesirable equilibria is widespread. We propose two mechanisms to alleviate this: (i) seeding a proportion of the system with fixed agents that steer others towards good equilibria; and (ii), intrinsic rewards based on the idea of introspection, i.e., augmenting agents' rewards by an amount proportionate to the performance of their own strategy against themselves. A combination of these simple mechanisms is successful in stabilizing cooperation, even in a fully decentralized version of the problem where agents learn to use and assign reputations simultaneously. We show how our results relate to the literature in Evolutionary Game Theory, and discuss implications for artificial, human and hybrid systems, where reputations can be used as a way to establish trust and cooperation.
Adaptive Optimization of Autonomous Vehicle Computational Resources for Performance and Energy Improvement

Autonomous vehicles usually consume a large amount of computational power for their operations, especially for the tasks of sensing and perception with artificial intelligence algorithms. Such a computation may not only cost a significant amount of energy but also cause performance issues when the onboard computational resources are limited. To address this issue, this paper proposes an adaptive optimization method to online allocate the onboard computational resources of an autonomous vehicle amongst multiple vehicular subsystems depending on the contexts of the situations that the vehicle is facing. Different autonomous driving scenarios were designed to validate the proposed approach and the results showed that it could help improve the overall performance and energy consumption of autonomous vehicles compared to existing computational arrangement.
A systematic survey for z< 0.04 CLAGNs

We have conducted a systematic survey for z < 0.04 active Galactic nuclei (AGNs) that may have changed spectral class over the past decade. We use SkyMapper, Pan-STARRS and the Véron-Cetty & Véron (2010) catalogue to search the entire sky for these “changinglook” AGNs using a variety of selection methods, where Pan-STARRS has a coverage of 3c steradians (sky north of Declination−30) and SkyMapper has coverage of∼ 21,000 deg2 (sky south of Declination 0). We use small aperture photometry to measure how colour and flux have changed over time, where a change may indicate a change in spectral type. Optical colour and flux are used as a proxy for changing HU equivalent width, while WISE 3.4 `m flux is used to look for changes in the hot dust component. We have identified four AGNs with varying spectra selected using our optical colour selection method. Three AGNs were confirmed from recent observations with WiFeS on the 2.3 m telescope at Siding Spring and the other was identified from archival spectra alone. From this, we identify two new changing look AGNs; NGC 1346 and 2MASX J20075129-1108346. We also recover Mrk 915 and Mrk 609, which are known to have varying spectra in the literature, but they do not meet our specific criteria for changing look AGNs.
Nested closed paths in two-dimensional percolation

For two-dimensional percolation on a domain with the topology of a disc, we introduce a nested-path (NP) operator and thus a continuous family of one-point functions Wk≡⟨R⋅kℓ⟩ , where ℓ is the number of independent (i.e., non-overlapping) nested closed paths surrounding the center, k is a path fugacity, and R projects on configurations having a cluster connecting the center to the boundary. At criticality, we observe a power-law scaling Wk∼L−XNP , with L the linear system size, and we determine the exponent X NP as a function of k. On the basis of our numerical results, we conjecture an analytical formula, XNP(k)=34ϕ2−548ϕ2/(ϕ2−23) with k = 2 cos(πϕ), which reproduces the exact results for k = 0, 1 and agrees with the high-precision estimate of XNP for other k values. In addition, we observe that W 2(L) = 1 for site percolation on the triangular lattice with any size L, and we prove this identity for all self-matching lattices.
Large coverage fluctuations in Google Scholar: a case study

Unlike other academic bibliographic databases, Google Scholar intentionally operates in a way that does not maintain coverage stability: documents that stop being available to Google Scholar's crawlers are removed from the system. This can also affect Google Scholar's citation graph (citation counts can decrease). Furthermore, because Google Scholar is not transparent about its coverage, the only way to directly observe coverage loss is through regular monitorization of Google Scholar data. Because of this, few studies have empirically documented this phenomenon. This study analyses a large decrease in coverage of documents in the field of Astronomy and Astrophysics that took place in 2019 and its subsequent recovery, using longitudinal data from previous analyses and a new dataset extracted in 2020. Documents from most of the larger publishers in the field disappeared from Google Scholar despite continuing to be available on the Web, which suggests an error on Google Scholar's side. Disappeared documents did not reappear until the following index-wide update, many months after the problem was discovered. The slowness with which Google Scholar is currently able to resolve indexing errors is a clear limitation of the platform both for literature search and bibliometric use cases.
Approximation Bounds for Sparse Programs

We show that sparsity constrained optimization problems over low dimensional spaces tend to have a small duality gap. We use the Shapley-Folkman theorem to derive both data-driven bounds on the duality gap, and an efficient primalization procedure to recover feasible points satisfying these bounds. These error bounds are proportional to the rate of growth of the objective with the target cardinality, which means in particular that the relaxation is nearly tight as soon as the target cardinality is large enough so that only uninformative features are added.
Simultaneous Acquisition of Spectral and Spatial Intensity Distribution

A method for recording and reconstructing spectral and spatial intensity distributions of sources is described. Three-dimensional information (two for spatial and one for wavelength) of the sources is simultaneously recorded as their projected forms by an imaging system including a crossed phase grating. A modified multiplicative algebraic technique is developed to reconstruct the original distribution. In experiments we obtained nine projection patterns with equal brightness of a color slide illuminated by a tungsten lamp. The reconstructed distribution shows good agreement with that measured by interference filters.
Healing Products of Gaussian Processes

Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems. One of the approaches to alleviate their cubic training cost is the use of local GP experts trained on subsets of the data. In particular, product-of-expert models combine the predictive distributions of local experts through a tractable product operation. While these expert models allow for massively distributed computation, their predictions typically suffer from erratic behaviour of the mean or uncalibrated uncertainty quantification. By calibrating predictions via a tempered softmax weighting, we provide a solution to these problems for multiple product-of-expert models, including the generalised product of experts and the robust Bayesian committee machine. Furthermore, we leverage the optimal transport literature and propose a new product-of-expert model that combines predictions of local experts by computing their Wasserstein barycenter, which can be applied to both regression and classification.
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization

Large scale distributed optimization has become the default tool for the training of supervised machine learning models with a large number of parameters and training data. Recent advancements in the field provide several mechanisms for speeding up the training, including {\em compressed communication}, {\em variance reduction} and {\em acceleration}. However, none of these methods is capable of exploiting the inherently rich data-dependent smoothness structure of the local losses beyond standard smoothness constants. In this paper, we argue that when training supervised models, {\em smoothness matrices} -- information-rich generalizations of the ubiquitous smoothness constants -- can and should be exploited for further dramatic gains, both in theory and practice. In order to further alleviate the communication burden inherent in distributed optimization, we propose a novel communication sparsification strategy that can take full advantage of the smoothness matrices associated with local losses. To showcase the power of this tool, we describe how our sparsification technique can be adapted to three distributed optimization algorithms -- DCGD, DIANA and ADIANA -- yielding significant savings in terms of communication complexity. The new methods always outperform the baselines, often dramatically so.
Tractable structured natural gradient descent using local parameterizations

Natural-gradient descent (NGD) on structured parameter spaces (e.g., low-rank covariances) is computationally challenging due to difficult Fisher-matrix computations. We address this issue by using local-parameter coordinates to obtain a flexible and efficient NGD method that works well for a wide-variety of structured parameterizations. We show four applications where our method (1) generalizes the exponential natural evolutionary strategy, (2) recovers existing Newton-like algorithms, (3) yields new structured second-order algorithms, and (4) gives new algorithms to learn covariances of Gaussian and Wishart-based distributions. We show results on a range of problems from deep learning, variational inference, and evolution strategies. Our work opens a new direction for scalable structured geometric methods.
Field-flow fractionation and inductively coupled plasma mass spectrometer coupling: History, development and applications

Field-Flow Fractionation (FFF) is now recognised as a versatile pool of techniques allowing particle size or molar mass to be obtained in a wide variety of samples covering numerous applications in the fields of environment, materials or biology. In the same time, Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) has an indisputable place in the field of elemental detectors and the coupling between FFF and ICP-MS can be considered as an emerging technique capable to reach relevant physico-chemical information at sub-micrometre scale and trace element concentration level. This paper gives some key elements of FFF-based fractionation linking theory and practical analytical aspects, from injection and preconcentration to analysis. The different components of the coupling are described. Summary tables of the main operating conditions of FFF-ICP-MS coupling are presented and operating conditions such as carrier composition, flow and nebulizers are discussed. Special attention is given to the FFF-ICP-MS interface. Qualitative and quantitative analysis is also discussed. Applications in the fields of environment, bioanalysis and nanoparticles are presented in order to illustrate the potentialities of such coupling.
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators

We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy. We find that the performance of state-of-the-art offline and model-based RL methods degrade significantly given such limited data availability, even for commonly perceived"solved"benchmark settings such as"MountainCar"and"CartPole". To address this challenge, we propose PerSim, a model-based offline RL approach which first learns a personalized simulator for each agent by collectively using the historical trajectories across all agents, prior to learning a policy. We do so by positing that the transition dynamics across agents can be represented as a latent function of latent factors associated with agents, states, and actions; subsequently, we theoretically establish that this function is well-approximated by a"low-rank"decomposition of separable agent, state, and action latent functions. This representation suggests a simple, regularized neural network architecture to effectively learn the transition dynamics per agent, even with scarce, offline data. We perform extensive experiments across several benchmark environments and RL methods. The consistent improvement of our approach, measured in terms of both state dynamics prediction and eventual reward, confirms the efficacy of our framework in leveraging limited historical data to simultaneously learn personalized policies across agents.
A Mathematical Morel for Drug Release from 0/W Emulsions: Application to Controlled Release Morphine Emulsions

AbstractStable morphine submicronized emulsions were prepared under optimal experimental conditions. Mean droplet size and zeta potential of the morphine emulsions ranged from 400 to 700 nm and from -50 to -70 mv respectively. In vitro release kinetic analysis indicated that the drug transfer from the oily dispersed droplets into the external aqueous phase was the rate determining step in the overall kinetic process providing the main portion of the drug ‘twas localized in the oily phase. A kinetic equation model was proposed and found suitable for the description of morphine release from the emulsion due to conformity of the experimental data to the expected kinetic data as calculated by means of this equation.