Miracle-dz/UniFAR-FTUs huggingface dataset:
Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

Most existing personalization systems promote items that match a user’s previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on random walks with erasure is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.
Automated crystal orientation mapping with sub-degree angular resolution using precession electron diffraction assisted 4D scanning transmission electron microscopy

The recent development of electron sensitive and pixelated detectors has attracted the use of four-dimensional scanning transmission electron microscopy (4D-STEM). Here, we present a precession electron diffraction assisted 4D-STEM technique for automated orientation mapping using diffraction spot patterns directly captured by an in-column scintillator based complementary metal-oxide-semiconductor (CMOS) detector. We compare the results to a conventional approach, which utilizes a phosphor screen filmed by an external CCD camera. The high dynamic range and signal to noise characteristics of the detector largely improve the image quality of the diffraction patterns, especially the visibility of diffraction spots at high scattering angles. Moreover, the afterimage formation during probe scanning, which obscures orientation indexing in the conventional approach, is suppressed by the fast read-out rate of the detector. The scintillator was found to be robust during the acquisition of nanobeam diffraction pattern including the unblocked transmitted beam in a TEM operated at 200 kV. In the orientation maps reconstructed via the template matching process, the CMOS data yields a significant reduction of false indexing and a higher reliability compared to the conventional approach. This is because the orientation sensitive, weak and small diffraction spots at high scattering angle are more significant. The angular resolution could also be improved in both accuracy and precision by masking reflections close to the direct beam. The results show that fine details such as nano 2 grains, nano twins and sub-grain boundaries can be resolved with sub-degree angular resolution which is comparable Kikuchi-diffraction based orientation mapping.
Astrophysical Constraints on the Symmetry Energy and the Neutron Skin of ^{208}Pb with Minimal Modeling Assumptions.

The symmetry energy and its density dependence are crucial inputs for many nuclear physics and astrophysics applications, as they determine properties ranging from the neutron-skin thickness of nuclei to the crust thickness and the radius of neutron stars. Recently, PREX-II reported a value of 0.283±0.071 fm for the neutron-skin thickness of ^{208}Pb, implying a slope parameter L=106±37 MeV, larger than most ranges obtained from microscopic calculations and other nuclear experiments. We use a nonparametric equation of state representation based on Gaussian processes to constrain the symmetry energy S_{0}, L, and R_{skin}^{^{208}Pb} directly from observations of neutron stars with minimal modeling assumptions. The resulting astrophysical constraints from heavy pulsar masses, LIGO/Virgo, and NICER clearly favor smaller values of the neutron skin and L, as well as negative symmetry incompressibilities. Combining astrophysical data with PREX-II and chiral effective field theory constraints yields S_{0}=33. 0_{-1.8}^{+2.0} MeV, L=53_{-15}^{+14} MeV, and R_{skin}^{^{208}Pb}=0. 17_{-0.04}^{+0.04} fm.
Controller Synthesis for Golog Programs over Finite Domains with Metric Temporal Constraints

Executing a Golog program on an actual robot typically requires additional steps to account for hardware or software details of the robot platform, which can be formulated as constraints on the program. Such constraints are often temporal, refer to metric time, and require modifications to the abstract Golog program. We describe how to formulate such constraints based on a modal variant of the Situation Calculus. These constraints connect the abstract program with the platform models, which we describe using timed automata. We show that for programs over finite domains and with fully known initial state, the problem of synthesizing a controller that satisfies the constraints while preserving the effects of the original program can be reduced to MTL synthesis. We do this by constructing a timed automaton from the abstract program and synthesizing an MTL controller from this automaton, the platform models, and the constraints. We prove that the synthesized controller results in execution traces which are the same as those of the original program, possibly interleaved with platform-dependent actions, that they satisfy all constraints, and that they have the same effects as the traces of the original program. By doing so, we obtain a decidable procedure to synthesize a controller that satisfies the specification while preserving the original program.
The origins of off-centre massive black holes in dwarf galaxies

Massive black holes often exist within dwarf galaxies, and both simulations and observations have shown that a substantial fraction of these may be off-centre with respect to their hosts. We trace the evolution of off-centre massive black holes (MBHs) in dwarf galaxies using cosmological hydrodynamical simulations, and show that the reason for off-centre locations is mainly due to galaxy–galaxy mergers. We calculate dynamical time-scales and show that off-centre MBHs are unlikely to sink to their galaxys’ centres within a Hubble time, due to the shape of the hosts’ potential wells and low stellar densities. These wandering MBHs are unlikely to be detected electromagnetically, nor is there a measurable dynamical effect on the galaxy’s stellar population. We conclude that off-centre MBHs may be common in dwarfs, especially if the mass of the MBH is small or the stellar mass of the host galaxy is large. However, detecting them is extremely challenging, because their accretion luminosities are very low and they do not measurably alter the dynamics of their host galaxies.
Local Convergence of Adaptive Gradient Descent Optimizers

Adaptive Moment Estimation (ADAM) is a very popular training algorithm for deep neural networks and belongs to the family of adaptive gradient descent optimizers. However to the best of the authors knowledge no complete convergence analysis exists for ADAM. The contribution of this paper is a method for the local convergence analysis in batch mode for a deterministic fixed training set, which gives necessary conditions for the hyperparameters of the ADAM algorithm. Due to the local nature of the arguments the objective function can be non-convex but must be at least twice continuously differentiable. Then we apply this procedure to other adaptive gradient descent algorithms and show for most of them local convergence with hyperparameter bounds.
Recovering orthogonal tensors under arbitrarily strong, but locally correlated, noise

We consider the problem of recovering an orthogonally decomposable tensor with a subset of elements distorted by noise with arbitrarily large magnitude. We focus on the particular case where each mode in the decomposition is corrupted by noise vectors with components that are correlated locally, that is, with nearby components. We show that this deterministic tensor completion problem has the unusual property that it can be solved in polynomial time if the rank of the tensor is sufficiently large. This is the polar opposite of the low‐rank assumptions of typical low‐rank tensor and matrix completion settings. We show that our problem can be solved through a system of coupled Sylvester‐like equations and show how to accelerate their solution by an alternating solver. This enables recovery even with a substantial number of missing entries, for instance for n$$ n $$ ‐dimensional tensors of rank n$$ n $$ with up to 40%$$ 40\% $$ missing entries.
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem

In this paper, we study the problem of fair sparse regression on a biased dataset where bias depends upon a hidden binary attribute. The presence of a hidden attribute adds an extra layer of complexity to the problem by combining sparse regression and clustering with unknown binary labels. The corresponding optimization problem is combinatorial, but we propose a novel relaxation of it as an invex optimization problem. To the best of our knowledge, this is the ﬁrst invex relaxation for a combinatorial problem. We show that the inclusion of the debi-asing/fairness constraint in our model has no adverse effect on the performance. Rather, it enables the recovery of the hidden attribute. The support of our recovered regression parameter vector matches exactly with the true parameter vector. Moreover, we simultaneously solve the clustering problem by recovering the exact value of the hidden attribute for each sample. Our method uses carefully constructed primal dual witnesses to provide theoretical guarantees for the combinatorial problem. To that end, we show that the sample complexity of our method is logarithmic in terms of the dimension of the regression parameter vector.
The effect of surface topography on the retention of microorganisms

This paper reviews the effect of substratum topography on microbial retention. It is generally acknowledged that an increased substratum surface roughness affects the retention of microorganisms on that surface. However, this premise is only valid for certain feature dimensions: if the features are considerably larger than the microbial cells, then retention is not significant; if the features are of microbial dimensions, then retention may become a problem. Surface features of nano-dimensions have been shown to affect cell behaviour and increase microbial retention. If features are linear (e.g., scratches), then cocci may be trapped, but rod shaped bacteria may not depending on cell alignment, and binding energy within or across surface features. This is also true for more well separated features, such as pits. Much work has been carried out on the effect of microbial retention on surfaces with randomly sized, orientated and shaped features. To gain a better understanding of the effect of surface roughness on microbial retention it is essential that surfaces with defined features, hydrophobicities and chemistries are used. Using this knowledge, the development of more easily cleanable, hygienic surfaces is possible.
Incentives for accelerating the production of Covid-19 vaccines in the presence of adjustment costs

Delays in the availability of vaccines are costly as the pandemic continues. However, in the presence of adjustment costs firms have an incentive to increase production capacity only gradually. The existing contracts specify only a fixed quantity to be supplied over a certain period and thus provide no incentive for an accelerated buildup in capacity. A high price does not change this. The optimal contract would specify a decreasing price schedule over time which can replicate the social optimum.
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy

In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions to the task. We propose a task-oriented representation of complex actions, called procedures, to learn online task relationships and unbounded sequences of action primitives to control the different observables of the environment. Combining both goal-babbling with imitation learning, and active learning with transfer of knowledge based on intrinsic motivation, our algorithm self-organises its learning process. It chooses at any given time a task to focus on; and what, how, when and from whom to transfer knowledge. We show with a simulation and a real industrial robot arm, in cross-task and cross-learner transfer settings, that task composition is key to tackle highly complex tasks. Task decomposition is also efficiently transferred across different embodied learners and by active imitation, where the robot requests just a small amount of demonstrations and the adequate type of information. The robot learns and exploits task dependencies so as to learn tasks of every complexity.
Layer-based Composite Reputation Bootstrapping

We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.
Identification of Phase-Locked Loop System From Its Experimental Time Series

Phase-locked loops (PLLs) are now widely used in communication systems and have been a classic system for more than 60 years. Well-known mathematical models of such systems are constructed in a number of approximations, so questions about how they describe the experimental dynamics qualitatively and quantitatively, and how the accuracy of the model description depends on the dynamic mode, remain open. One of the most direct approaches to the verification of any model is its reconstruction from the time series obtained in experiment. If it is possible to fit the model to experimental data and the resulting parameter values are close to the expected values (calculated from the first principles), the quantitative correspondence is nearly proved. In this brief, for the first time, the equations of the PLL model with a bandpass filter are reconstructed from experimental signals of the generator in various modes. The reconstruction shows that the model known from literature generally describes the experimental dynamics in regular and chaotic regimes. The relative error of parameter estimation is between 2% and 50%. The reconstructed nonlinear function of phase is not harmonic and highly asymmetric in contrast to model one.
Transient-optimized real-bogus classification with Bayesian convolutional neural networks – sifting the GOTO candidate stream

Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filtering to accurately sift through the vast quantities of incoming data generated. In this paper, we present a new real-bogus classifier based on a Bayesian convolutional neural network that provides nuanced, uncertainty-aware classification of transient candidates in difference imaging, and demonstrate its application to the datastream from the GOTO wide-field optical survey. Not only are candidates assigned a well-calibrated probability of being real, but also an associated confidence that can be used to prioritize human vetting efforts and inform future model optimization via active learning. To fully realize the potential of this architecture, we present a fully automated training set generation method which requires no human labelling, incorporating a novel data-driven augmentation method to significantly improve the recovery of faint and nuclear transient sources. We achieve competitive classification accuracy (FPR and FNR both below 1 per cent) compared against classifiers trained with fully human-labelled data sets, while being significantly quicker and less labour-intensive to build. This data-driven approach is uniquely scalable to the upcoming challenges and data needs of next-generation transient surveys. We make our data generation and model training codes available to the community.
Neural Kalman Filtering

The Kalman filter is a fundamental filtering algorithm that fuses noisy sensory data, a previous state estimate, and a dynamics model to produce a principled estimate of the current state. It assumes, and is optimal for, linear models and white Gaussian noise. Due to its relative simplicity and general effectiveness, the Kalman filter is widely used in engineering applications. Since many sensory problems the brain faces are, at their core, filtering problems, it is possible that the brain possesses neural circuitry that implements equivalent computations to the Kalman filter. The standard approach to Kalman filtering requires complex matrix computations that are unlikely to be directly implementable in neural circuits. In this paper, we show that a gradient-descent approximation to the Kalman filter requires only local computations with variance weighted prediction errors. Moreover, we show that it is possible under the same scheme to adaptively learn the dynamics model with a learning rule that corresponds directly to Hebbian plasticity. We demonstrate the performance of our method on a simple Kalman filtering task, and propose a neural implementation of the required equations.
Improved Denoising Diffusion Probabilistic Models

Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion
Market Effects of Loyalty and Cost Factors in a Price Discrimination Environment

Product cost heterogeneity across firms and loyalty models of customers are two topics that have garnered limited attention in prior studies on competitive price discrimination. Costs are generally assumed negligible or equal for all firms, and loyalty is modeled as an additive bias in customer valuations. We extend these previous treatments by considering cost asymmetry and a richer class of loyalty models in a game-theoretic model involving two asymmetric firms. Here firms may incur different non-negligible product costs, and customers can have firm-specific loyalty levels. We characterize the effects of loyalty levels and product cost difference on market outcomes such as prices, market share and profits. Our analysis and numerical simulations shed new light on market equilibrium structures arising from the interplay between product cost difference and loyalty levels.
In and out of Lockdown: Propagation of Supply and Demand Shocks in a Dynamic Input-Output Model

Economic shocks due to COVID-19 were exceptional in their severity, suddenness and heterogeneity across industries. To study the upstream and downstream propagation of these industry-specific demand and supply shocks, we build a dynamic input-output model inspired by previous work on the economic response to natural disasters. We argue that standard production functions, at least in their most parsimonious parametrizations, are not adequate to model input substitutability in the context of COVID-19 shocks. We use a survey of industry analysts to evaluate, for each industry, which inputs were absolutely necessary for production over a short time period. We calibrate our model on the UK economy and study the economic effects of the lockdown that was imposed at the end of March and gradually released in May. Looking back at predictions that we released in May, we show that the model predicted aggregate dynamics very well, and sectoral dynamics to a large extent. We discuss the relative extent to which the model's dynamics and performance was due to the choice of the production function or the choice of an exogenous shock scenario. To further explore the behavior of the model, we use simpler scenarios with only demand or supply shocks, and find that popular metrics used to predict a priori the impact of shocks, such as output multipliers, are only mildly useful.
Progressive Transformer-Based Generation of Radiology Reports

Inspired by Curriculum Learning, we propose a consecutive (i.e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps. Contrary to generating the full radiology report from the image at once, the model generates global concepts from the image in the first step and then reforms them into finer and coherent texts using a transformer architecture. We follow the transformer-based sequence-to-sequence paradigm at each step. We improve upon the state-of-the-art on two benchmark datasets.