Miracle-dz/UniFAR-FTUs huggingface dataset:
Factor-level Attentive ICF for Recommendation

Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender systems. ICF recommends items to a target user based on their similarities to the previously interacted items of the user. Great progresses have been achieved for ICF in recent years by applying advanced machine learning techniques (e.g., deep neural networks) to learn the item similarity from data. The early methods simply treat all the historical items equally and recent ones distinguish the different importance of items for a prediction. Despite the progress, we argue that those ICF models neglect the diverse intents of users on adopting items (e.g., watching a movie because of the director, leading actors, or the visual effects). As a result, they fail to estimate the item similarity on a finer-grained level to predict the user’s preference for an item, resulting in sub-optimal recommendation. In this work, we propose a general factor-level attention method for ICF models. The key of our method is to distinguish the importance of different factors when computing the item similarity for a prediction. To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and factor-level attention for neural ICF models. It is model-agnostic and easy-to-implement. We apply it to two baseline ICF models and evaluate its effectiveness on six public datasets. Extensive experiments show the factor-level attention enhanced models consistently outperform their counterparts, demonstrating the potential of differentiate user intents on the factor-level for ICF recommendation models.
The low-redshift circumgalactic medium in simba

We examine the properties of the low-redshift circumgalactic medium (CGM) around star-forming and quenched galaxies in the simba cosmological hydrodynamic simulations, focusing on comparing H i and metal line absorption to observations from the Cosmic Origins Spectrograph (COS)-Halos and COS-Dwarfs surveys. Halo baryon fractions are generally ${\lesssim}50{{\ \rm per\ cent}}$ of the cosmic fraction due to stellar feedback at low masses, and jet-mode AGN feedback at high masses. Baryons and metals in the CGM of quenched galaxies are ${\gtrsim}90{{\ \rm per\ cent}}$ hot gas, while the CGM of star-forming galaxies is more multiphase. Hot CGM gas has low metallicity, while warm and cool CGM gas have metallicity close to that of galactic gas. Equivalent widths, covering fractions and total path absorption of H i and selected metal lines (Mg ii, Si iii, C iv, and O vi) around a matched sample of simba star-forming galaxies are mostly consistent with COS-Halos and COS-Dwarfs observations to ${\lesssim}0.4$ dex, depending on ion and assumed ionizing background. Around matched quenched galaxies, absorption in all ions is lower, with H i absorption significantly underpredicted. Metal-line absorption is sensitive to choice of photoionizing background; assuming recent backgrounds, simba matches O vi but underpredicts low ions, while an older background matches low ions but underpredicts O vi. Simba reproduces the observed dichotomy of O vi absorption around star-forming and quenched galaxies. CGM metals primarily come from stellar feedback, while jet-mode AGN feedback reduces absorption particularly for lower ions.
Biodegradation of Blown Films Based on Poly(lactic acid) under Natural Conditions

Commercially available polymer Bioflex® 219F, blend of polylactic acid and biodegradable co-polyester, was used for film preparation, performed on mono-extrusion blown moulding machine. Resulting thin film was investigated on biodegradability in composting conditions for 6 weeks. The influence of microbial attack on mechanical, physico-chemical properties, weight loss and surface morphology was tested weekly. The results obtained during 6 weeks of composting indicate relatively good accessibility to biological degradation. Moreover, the time course of studied properties was observed through the test period.
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations

Recent advances in implicit neural representations show great promise when it comes to generating numerical solutions to partial differential equations. Compared to conventional alternatives, such representations employ parameterized neural networks to define, in a mesh-free manner, signals that are highly-detailed, continuous, and fully differentiable. In this work, we present a novel machine learning approach for topology optimization -- an important class of inverse problems with high-dimensional parameter spaces and highly nonlinear objective landscapes. To effectively leverage neural representations in the context of mesh-free topology optimization, we use multilayer perceptrons to parameterize both density and displacement fields. Our experiments indicate that our method is highly competitive for minimizing structural compliance objectives, and it enables self-supervised learning of continuous solution spaces for topology optimization problems.
Transport Services: A Modern API for an Adaptive Internet Transport Layer

Transport services (TAPS) is a working group of the the Internet Engineering Task Force (IETF). TAPS defines a new recommended API for the Internet's transport layer. This API gives access to a wide variety of services from various protocols, and it is protocol-independent: the transport layer becomes adaptive, and applications are no longer statically bound to a particular protocol and/or network interface. We give an overview of the TAPS API, and we demonstrate its flexibility and ease of use with an example using a Python-based open source implementation.
Multilingual Answer Sentence Reranking via Automatically Translated Data

We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems. The main idea is to transfer data, created from one resource rich language, e.g., English, to other languages, less rich in terms of resources. The main findings of this paper are: (i) the training data for AS2 translated into a target language can be used to effectively fine-tune a Transformer-based model for that language; (ii) one multilingual Transformer model it is enough to rank answers in multiple languages; and (iii) mixed-language question/answer pairs can be used to fine-tune models to select answers from any language, where the input question is just in one language. This highly reduces the complexity and technical requirement of a multilingual QA system. Our experiments validate the findings above, showing a modest drop, at most 3%, with respect to the state-of-the-art English model.
Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion

Image decomposition is a crucial subject in the field of image processing. It can extract salient features from the source image. We propose a new image decomposition method based on convolutional neural network. This method can be applied to many image processing tasks. In this paper, we apply the image decomposition network to the image fusion task. We input infrared image and visible light image and decompose them into three high-frequency feature images and a low-frequency feature image respectively. The two sets of feature images are fused using a specific fusion strategy to obtain fusion feature images. Finally, the feature images are reconstructed to obtain the fused image. Compared with the state-of-the-art fusion methods, this method has achieved better performance in both subjective and objective evaluation.
Do Generative Models Know Disentanglement? Contrastive Learning is All You Need

Disentangled generative models are typically trained with an extra regularization term, which encourages the traversal of each latent factor to make a distinct and independent change at the cost of generation quality. When traversing the latent space of generative models trained without the disentanglement term, the generated samples show semantically meaningful change, raising the question: do generative models know disentanglement? We propose an unsupervised and model-agnostic method: Dis entanglement via Co ntrast ( DisCo ) in the Variation Space . DisCo consists of: ( i ) a Navigator providing traversal directions in the latent space, and ( ii ) a ∆ - Contrastor composed of two shared-weight En-coders, which encode image pairs along these directions to disentangled representations respectively, and a difference operator to map the encoded representations to the Variation Space . We propose two more key techniques for DisCo : entropy-based domination loss to make the encoded representations more disentangled and the strategy of ﬂipping hard negatives to address directions with the same semantic meaning. By optimizing the Navigator to discover disentan-gled directions in the latent space and Encoders to extract disentangled representations from images with Contrastive Learning, DisCo achieves the state-of-the-art disentanglement given pre-trained non-disentangled generative models, including GAN, VAE, and Flow. Project page at https://github.com/xrenaa/DisCo .
Singer Identification Using Deep Timbre Feature Learning with KNN-NET

In this paper, we study the issue of automatic singer identification (SID) in popular music recordings, which aims to recognize who sang a given piece of song. The main challenge for this investigation lies in the fact that a singer’s singing voice changes and intertwines with the signal of background accompaniment in time domain. To handle this challenge, we propose the KNN-Net for SID, which is a deep neural network model with the goal of learning local timbre feature representation from the mixture of singer voice and background music. Unlike other deep neural networks using the softmax layer as the output layer, we instead utilize the KNN as a more interpretable layer to output target singer labels. Moreover, attention mechanism is first introduced to highlight crucial timbre features for SID. Experiments on the existing artist20 dataset show that the proposed approach outperforms the state-of-the-art method by 4%. We also create singer32 and singer60 datasets consisting of Chinese pop music to evaluate the reliability of the proposed method. The more extensive experiments additionally indicate that our proposed model achieves a significant performance improvement compared to the state-of-the-art methods.
Superradiance and anomalous hyperfine splitting in inhomogeneous ensembles

Collective effects in the interaction of light with ensembles of identical scatterers play an important role in many fields of physics. However, often the term “identical” is not accurate due to the presence of hyperfine fields which induce inhomogeneous transition shifts and splittings. Here we develop a formalism based on the Green function method to model the linear response of such inhomogeneous ensembles in one-dimensional waveguides. We obtain a compact formula for the collective spectrum, which exhibits deviations from the uniform frequency shift and broadening expected of two level systems. In particular, if the coherent contribution to the collective coupling is large, the effect of inhomogeneous broadening can be suppressed, with the linewidth approaching that of the superradiant value. We apply this formalism to describe collective effects in x-ray scattering off thin-film waveguides for inhomogeneous hyperfine parameters.
Changing Beliefs about Scientific Issues: The Role of Moral and Social Belief Networks

The skepticism towards childhood vaccines and genetically modified (GM) food has grown against scientific evidence of their safety. Distrust in scientific research has important societal consequences, from the spread of diseases to hunger in poorer regions. However, these scientific beliefs are hard to change because they are entrenched within many related moral beliefs and perceived beliefs of one’s social network. To understand when belief change is possible, we propose a cognitive network model which integrates both moral and social beliefs and provides testable empirical predictions. Using a probabilistic nationally representative longitudinal study, we find that individuals who changed their beliefs, either towards more positive or negative beliefs about childhood vaccines or GM food, had a reduction in the estimated dissonance of their cognitive belief network. These results are in line with model predictions, shed light on the mechanisms leading to belief change, and have implications for science communication.
Studentized permutation method for comparing two restricted mean survival times with small sample from randomized trials

Recent observations, especially in cancer immunotherapy clinical trials with time‐to‐event outcomes, show that the commonly used proportional hazard assumption is often not justifiable, hampering an appropriate analysis of the data by hazard ratios. An attractive alternative advocated is given by the restricted mean survival time (RMST), which does not rely on any model assumption and can always be interpreted intuitively. Since methods for the RMST based on asymptotic theory suffer from inflated type‐I error under small sample sizes, a permutation test was proposed recently leading to more convincing results in simulations. However, classical permutation strategies require an exchangeable data setup between comparison groups which may be limiting in practice. Besides, it is not possible to invert related testing procedures to obtain valid confidence intervals, which can provide more in‐depth information. In this paper, we address these limitations by proposing a studentized permutation test as well as respective permutation‐based confidence intervals. In an extensive simulation study, we demonstrate the advantage of our new method, especially in situations with relatively small sample sizes and unbalanced groups. Finally, we illustrate the application of the proposed method by re‐analyzing data from a recent lung cancer clinical trial.
Data to Physicalization: A Survey of the Physical Rendering Process

Physical representations of data offer physical and spatial ways of looking at, navigating, and interacting with data. While digital fabrication has facilitated the creation of objects with data‐driven geometry, rendering data as a physically fabricated object is still a daunting leap for many physicalization designers. Rendering in the scope of this research refers to the back‐and‐forth process from digital design to digital fabrication and its specific challenges. We developed a corpus of example data physicalizations from research literature and physicalization practice. This survey then unpacks the “rendering” phase of the extended InfoVis pipeline in greater detail through these examples, with the aim of identifying ways that researchers, artists, and industry practitioners “render” physicalizations using digital design and fabrication tools.
Three dimensional unique identifier based automated georeferencing and coregistration of point clouds in underground environment

Spatially referenced and geometrically accurate laser scans are essential for mapping and monitoring applications in underground mines to ensure safe and smooth operation. However, obtaining an absolute 3D map in an underground mine environment is challenging using laser scanning due to the unavailability of global navigation satellite system (GNSS) signals. Consequently, applications that require georeferenced point cloud or coregistered multitemporal point clouds such as detecting changes, monitoring deformations, tracking mine logistics, measuring roadway convergence rate and evaluating construction performance become challenging. Current mapping practices largely include a manual selection of discernable reference points in laser scans for georeferencing and coregistration which is often time-consuming, arduous and error-prone. Moreover, challenges in obtaining a sensor positioning framework, the presence of structurally symmetric layouts and highly repetitive features (such as roof bolts) makes the multitemporal scans difficult to georeference and coregister. This study aims at overcoming these practical challenges through development of three-dimensional unique identifiers (3DUIDs) and a 3D registration (3DReG) workflow. Field testing of the developed approach in an underground coal mine has been found effective with an accuracy of 1.76 m in georeferencing and 0.16 m in coregistration for a scan length of 850 m. Additionally, automatic extraction of mine roadway profile has been demonstrated using 3DUID which is often a compliant and operational requirement for mitigating roadway related hazards that includes roadway convergence rate, roof/rock falls, floor heaves and vehicle clearance for collision avoidance. Potential applications of 3DUID include roadway profile extraction, guided automation, sensor calibration, reference targets for a routine survey and deformation monitoring.
Tracing the evolution of ultraluminous infrared galaxies into radio galaxies with low frequency radio observations

We present radio observations of ultraluminous infrared galaxies (ULIRGs) using the Giant Metrewave Radio Telescope (GMRT) and combine them with archival multifrequency observations to understand whether ULIRGs are the progenitors of the powerful radio loud galaxies in the local Universe. ULIRGs are characterized by large infrared luminosities (LIR > 1012 L⊙), large dust masses (∼108 M⊙), and vigorous star formation (star formation rates ∼10–100 M⊙ yr−1). Studies show that they represent the end stages of mergers of gas-rich spiral galaxies. Their luminosity can be due to both starburst activity and active galactic nuclei (AGNs). We study a sample of 13 ULIRGs that have optically identified AGN characteristics with 1.28 GHz GMRT observations. Our aim is to resolve any core-jet structures or nuclear extensions and hence examine whether the ULIRGs are evolving into radio loud ellipticals. Our deep, low frequency observations show marginal extension for only one source. However, the integrated radio spectra of 9 ULIRGs show characteristics that are similar to that of GPS/CSS/CSO/young radio sources. The estimated spectral ages are 0.4–20 Myr and indicate that they are young radio sources and possible progenitors of radio galaxies. Hence, we conclude that although most ULIRGs do not show kpc scale extended radio emission associated with nuclear activity, their radio spectral energy distributions do show signatures of young radio galaxies.
Causal Policy Gradients

Policy gradient methods can solve complex tasks but often fail when the dimensionality of the action-space or objective multiplicity grow very large. This occurs, in part, because the variance on score-based gradient estimators scales quadratically with the number of targets. In this paper, we propose a causal baseline which exploits in-dependence structure encoded in a novel action-target inﬂuence network. Causal policy gradients (CPGs), which follow, provide a common framework for analysing key state-of-the-art algorithms, are shown to generalise traditional policy gradients, and yield a principled way of incorporating prior knowledge of a problem domain’s generative processes. We provide an analysis of the proposed estimator and identify the conditions under which variance is guaranteed to improve. The algorithmic aspects of CPGs are also discussed, including optimal policy factorisations, their complexity, and the use of conditioning to efﬁciently scale to extremely large, concurrent tasks. The performance advantages for two variants of the algorithm are demonstrated on large-scale bandit and concurrent inventory management problems.
Program Synthesis Guided Reinforcement Learning

A key challenge for reinforcement learning is solving long-horizon planning problems. Recent work has leveraged programs to guide reinforcement learning in these settings. However, these approaches impose a high manual burden on the user since they must provide a guiding program for every new task. Partially observed environments further complicate the programming task because the program must implement a strategy that correctly, and ideally optimally, handles every possible configuration of the hidden regions of the environment. We propose a new approach, model predictive program synthesis (MPPS), that uses program synthesis to automatically generate the guiding programs. It trains a generative model to predict the unobserved portions of the world, and then synthesizes a program based on samples from this model in a way that is robust to its uncertainty. In our experiments, we show that our approach significantly outperforms non-program-guided approaches on a set of challenging benchmarks, including a 2D Minecraft-inspired environment where the agent must complete a complex sequence of subtasks to achieve its goal, and achieves a similar performance as using handcrafted programs to guide the agent. Our results demonstrate that our approach can obtain the benefits of program-guided reinforcement learning without requiring the user to provide a new guiding program for every new task.
Logarithmic Regret in Feature-based Dynamic Pricing

Feature-based dynamic pricing is an increasingly popular model of setting prices for highly differentiated products with applications in digital marketing, online sales, real estate and so on. The problem was formally studied as an online learning problem [Javanmard&Nazerzadeh, 2019] where a seller needs to propose prices on the fly for a sequence of $T$ products based on their features $x$ while having a small regret relative to the best --"omniscient"-- pricing strategy she could have come up with in hindsight. We revisit this problem and provide two algorithms (EMLP and ONSP) for stochastic and adversarial feature settings, respectively, and prove the optimal $O(d\log{T})$ regret bounds for both. In comparison, the best existing results are $O\left(\min\left\{\frac{1}{\lambda_{\min}^2}\log{T}, \sqrt{T}\right\}\right)$ and $O(T^{2/3})$ respectively, with $\lambda_{\min}$ being the smallest eigenvalue of $\mathbb{E}[xx^T]$ that could be arbitrarily close to $0$. We also prove an $\Omega(\sqrt{T})$ information-theoretic lower bound for a slightly more general setting, which demonstrates that"knowing-the-demand-curve"leads to an exponential improvement in feature-based dynamic pricing.
Direct and inverse spin-orbit torques in antiferromagnetic and ferromagnetic FeRh/W(001)

We use ab-initio calculations to investigate spin-orbit torques (SOTs) in FeRh(001) deposited on W(100). Since FeRh undergoes a ferromagnetic-antiferromagnetic phase transition close to room temperature, we consider both phases of FeRh. In the antiferromagnetic case we find that the effective magnetic field of the even torque is staggered and therefore ideal to induce magnetization dynamics or to switch the antiferromagnet (AFM). At the antiferromagnetic resonance the inverse SOT induces a current density, which can be determined from the SOT. In the ferromagnetic case our calculations predict both even and odd components of the SOT, which can also be used to describe the ac and dc currents induced at the ferromagnetic resonance. For comparison we compute the SOTs in the c(2 × 2) AFM state of Fe/W(001).