Miracle-dz/UniFAR-FTUs huggingface dataset:
T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems

Simulation has emerged as a popular method to study the long-term societal consequences of recommender systems. This approach allows researchers to specify their theoretical model explicitly and observe the evolution of system-level outcomes over time. However, performing simulation-based studies often requires researchers to build their own simulation environments from the ground up, which creates a high barrier to entry, introduces room for implementation error, and makes it difficult to disentangle whether observed outcomes are due to the model or the implementation. We introduce T-RECS, an open-sourced Python package designed for researchers to simulate recommendation systems and other types of sociotechnical systems in which an algorithm mediates the interactions between multiple stakeholders, such as users and content creators. To demonstrate the flexibility of T-RECS, we perform a replication of two prior simulation-based research on sociotechnical systems. We additionally show how T-RECS can be used to generate novel insights with minimal overhead. Our tool promotes reproducibility in this area of research, provides a unified language for simulating sociotechnical systems, and removes the friction of implementing simulations from scratch.
A Duality-based Approach for Real-time Obstacle Avoidance between Polytopes with Control Barrier Functions

—Developing controllers for obstacle avoidance be- tween polytopes is a challenging and necessary problem for navigation in a tight space. Traditional approaches can only formulate the obstacle avoidance problem as an ofﬂine optimization problem. To address these challenges, we propose a duality-based safety-critical optimal control using control barrier functions for obstacle avoidance between polytopes, which can be solved in real-time with a QP-based optimization problem. A dual optimization problem is introduced to represent the minimum distance between polytopes and the Lagrangian function for the dual form is applied to construct a control barrier function. We demonstrate the proposed controller on a moving sofa problem where non-conservative maneuvers can be achieved in a tight space.
OODformer: Out-Of-Distribution Detection Transformer

A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model training, but performs much worse for out-of-distribution (OOD) samples. In real-world safety-critical applications, in particular, it is important to be aware if a new data point is OOD. To date, OOD detection is typically addressed using either confidence scores, auto-encoder based reconstruction, or by contrastive learning. However, the global image context has not yet been explored to discriminate the non-local objectness between in-distribution and OOD samples. This paper proposes a first-of-its-kind OOD detection architecture named OODformer that leverages the contextualization capabilities of the transformer. Incorporating the trans\-former as the principal feature extractor allows us to exploit the object concepts and their discriminate attributes along with their co-occurrence via visual attention. Using the contextualised embedding, we demonstrate OOD detection using both class-conditioned latent space similarity and a network confidence score. Our approach shows improved generalizability across various datasets. We have achieved a new state-of-the-art result on CIFAR-10/-100 and ImageNet30.
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues

Data missingness and quality are common problems in machine learning, especially for high-stakes applications such as healthcare. Developers often train machine learning models on carefully curated datasets using only high quality data; however, this reduces the utility of such models in production environments. We propose a novel neural network modification to mitigate the impacts of low quality and missing data which involves replacing the fixed weights of a fully-connected layer with a function of an additional input. This is inspired from neuromodulation in biological neural networks where the cortex can up- and down-regulate inputs based on their reliability and the presence of other data. In testing, with reliability scores as a modulating signal, models with modulating layers were found to be more robust against degradation of data quality, including additional missingness. These models are superior to imputation as they save on training time by completely skipping the imputation process and further allow the introduction of other data quality measures that imputation cannot handle. Our results suggest that explicitly accounting for reduced information quality with a modulating fully connected layer can enable the deployment of artificial intelligence systems in real-time applications.
Heterogeneous Face Frontalization via Domain Agnostic Learning

Recent advances in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on thermal to visible face synthesis and matching problems. However, current DCNN-based synthesis models do not perform well on thermal faces with large pose variations. In order to deal with this problem, heterogeneous face frontal-ization methods are needed in which a model takes a thermal profile face image and generates a frontal visible face. This is an extremely difficult problem due to the large domain as well as large pose discrepancies between the two modalities. Despite its applications in biometrics and surveillance, this problem is relatively unexplored in the literature. We propose a domain agnostic learning-based generative adversarial network (DAL-GAN) which can synthesize frontal views in the visible domain from thermal faces with pose variations. DAL-GAN consists of a generator with an auxiliary classifier and two discriminators which capture both local and global texture discriminations for better synthesis. A contrastive constraint is enforced in the latent space of the generator with the help of a dual-path training strategy, which improves the feature vector's discrimination. Finally, a multi-purpose loss function is utilized to guide the network in synthesizing identity-preserving cross-domain frontalization. Extensive experimental results demonstrate that DAL-GAN can generate better quality frontal views compared to the other baseline methods.
Connecting the proxy-SU(3) symmetry to the shell model

Proxy-SU(3) symmetry is an approximation scheme extending the Elliott SU(3) algebra of the sd shell to heavier shells. When introduced in 2017, the approximation had been justified by calculations carried out within the Nilsson model. Recently our group managed to map the cartesian basis of the Elliott SU(3) model onto the spherical shell model basis, proving that the proxy-SU(3) approximation corresponds to the replacement of the intruder orbitals by their de Shalit-Goldhaber partners, paving the way for using the proxy-SU(3) approximation in shell model calculations. The connection between the proxy-SU(3) scheme and the spherical shell model has also been worked out in the original framework of the Nilsson model, with identical results.
Optimal Resource Allocation for Serverless Queries

Optimizing resource allocation for analytical workloads is vital for reducing costs of cloud-data services. At the same time, it is incredibly hard for users to allocate resources per query in serverless processing systems, and they frequently misallocate by orders of magnitude. Unfortunately, prior work focused on predicting peak allocation while ignoring aggressive trade-offs between resource allocation and run-time. Additionally, these methods fail to predict allocation for queries that have not been observed in the past. In this paper, we tackle both these problems. We introduce a system for optimal resource allocation that can predict performance with aggressive trade-offs, for both new and past observed queries. We introduce the notion of a performance characteristic curve (PCC) as a parameterized representation that can compactly capture the relationship between resources and performance. To tackle training data sparsity, we introduce a novel data augmentation technique to efficiently synthesize the entire PCC using a single run of the query. Lastly, we demonstrate the advantages of a constrained loss function coupled with GNNs, over traditional ML methods, for capturing the domain specific behavior through an extensive experimental evaluation over SCOPE big data workloads at Microsoft.
Physical characterization of the active asteroid (6478) Gault

We report dense light-curve photometry, BVRc colours and phase–mag curve of (6478) Gault, an active asteroid with sporadic comet-like ejection of dust. We collected optical observations along the 2020 July–November months during which the asteroid appear always star-like, without any form of perceptible activity. We found complex light curves, with low amplitude around opposition and a bit higher amplitude far opposition, with a mean best rotation period of 2.46±0.02 h. Shape changes were observed in the phased light curves after opposition, a probable indication of concavities and surface irregularities. We suspect the existence of an Amplitude–Phase Relationship in C band. The mean colours are B − V = +0.84±0.04, V − Rc = +0.43±0.03, and B − Rc = +1.27±0.02, compatible with an S-type asteroid, but variables with the rotational phase index of a non-homogeneous surface composition. From our phase–mag curve and Shevchenko’s empirical photometric system, the geometric albedo result pV = 0.13±0.04, lower than the average value of the S-class. We estimate an absolute mag in V band of about + 14.9 and this, together with the albedo value, allows us to estimate a diameter of about 3–4 km, so Gault may be smaller than previously thought.
A Radiomics-Boosted Deep-Learning Model for COVID-19 and Non-COVID-19 Pneumonia Detection Using Chest X-ray Image

To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-learning models were trained based on a pre-trained VGG-16 architecture: in the 1st model, X-ray image was the sole input;in the 2nd model, X-ray image and 2 radiomic feature maps (RFM) selected by the saliency map analysis of the 1st model were stacked as the input. Both models were developed using 812 chest X-ray images with 262/288/262 COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. In 1st model using X-ray as the sole input, the 1) sensitivity, 2) specificity, 3) accuracy, and 4) ROC Area-Under-the-Curve of COVID-19 vs Non-COVID-19 pneumonia detection were 1) 0.90$\pm$0.07 vs 0.78$\pm$0.09, 2) 0.94$\pm$0.04 vs 0.94$\pm$0.04, 3) 0.93$\pm$0.03 vs 0.89$\pm$0.03, and 4) 0.96$\pm$0.02 vs 0.92$\pm$0.04. In the 2nd model, two RFMs, Entropy and Short-Run-Emphasize, were selected with their highest cross-correlations with the saliency maps of the 1st model. The corresponding results demonstrated significant improvements (p<0.05) of COVID-19 vs Non-COVID-19 pneumonia detection: 1) 0.95$\pm$0.04 vs 0.85$\pm$0.04, 2) 0.97$\pm$0.02 vs 0.96$\pm$0.02, 3) 0.97$\pm$0.02 vs 0.93$\pm$0.02, and 4) 0.99$\pm$0.01 vs 0.97$\pm$0.02. The reduced variations suggested a superior robustness of 2nd model design.
A stepped sampling method for video detection using LSTM

Artificial neural networks that simulate human achieves great successes. From the perspective of simulating human memory method, we propose a stepped sampler based on the"repeated input". We repeatedly inputted data to the LSTM model stepwise in a batch. The stepped sampler is used to strengthen the ability of fusing the temporal information in LSTM. We tested the stepped sampler on the LSTM built-in in PyTorch. Compared with the traditional sampler of PyTorch, such as sequential sampler, batch sampler, the training loss of the proposed stepped sampler converges faster in the training of the model, and the training loss after convergence is more stable. Meanwhile, it can maintain a higher test accuracy. We quantified the algorithm of the stepped sampler. We assume that, the artificial neural networks have human-like characteristics, and human learning method could be used for machine learning.
Balanced Hyperbolic and Divisorially Hyperbolic Compact Complex Manifolds

We introduce two notions of hyperbolicity for not necessarily Kähler n-dimensional compact complex manifolds X. The first, called balanced hyperbolicity, generalises Gromov’s Kähler hyperbolicity by means of Gauduchon’s balanced metrics. The second, called divisorial hyperbolicity, generalises the Brody hyperbolicity by ruling out the existence of non-degenerate holomorphic maps from Cn−1 to X that have what we term a subexponential growth. Our main result in the first part of the paper asserts that every balanced hyperbolic X is also divisorially hyperbolic. We provide a certain number of examples and counter-examples and discuss various properties of these manifolds. In the second part of the paper, we introduce the notions of divisorially Kähler and divisorially nef real De Rham cohomology classes of degree 2 and study their properties. They also apply to C∞, not necessarily holomorphic, complex line bundles and are expected to be implied in certain cases by the hyperbolicity properties introduced in the first part of the work. While motivated by the observation of hyperbolicity properties of certain non-Kähler manifolds, all these four new notions seem to have a role to play even in the Kähler and the projective settings.
Full-sky, Arcminute-scale, 3D Models of Galactic Microwave Foreground Dust Emission Based on Filaments

We present the DustFilaments code, a full-sky model for the millimeter Galactic emission of thermal dust. Our model, composed of millions of filaments that are imperfectly aligned with the magnetic field, is able to reproduce the main features of the dust angular power spectra at 353 GHz as measured by the Planck mission. Our model is made up of a population of filaments with sizes following a Pareto distribution ∝La−2.445 , with an axis ratio between short and long semiaxes ϵ ∼ 0.16 and an angle of magnetic field misalignment with a dispersion rms(θ LH) = 10°. On large scales, our model follows a Planck-based template. On small scales, our model produces spectra that behave like power laws up to ℓ ∼ 4000 or smaller scales by considering even smaller filaments, limited only by computing power. We can produce any number of Monte Carlo realizations of small-scale Galactic dust. Our model will allow tests of how the small-scale non-Gaussianity affects CMB weak lensing and the consequences for the measurement of primordial gravitational waves or relativistic light relic species. Our model also can generate frequency decorrelation on the modified blackbody spectrum of dust and is freely adjustable to different levels of decorrelation. This can be used to test the performance of component separation methods and the impact of frequency spectrum residuals on primordial B-mode surveys. The filament density we paint in the sky is also able to reproduce the general level of non-Gaussianities measured by Minkowski functionals in the Planck 353 GHz channel map.
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples

Local explanation methods such as LIME have become popular in MIR as tools for generating post-hoc, model-agnostic explanations of a model's classification decisions. The basic idea is to identify a small set of human-understandable features of the classified example that are most influential on the classifier's prediction. These are then presented as an explanation. Evaluation of such explanations in publications often resorts to accepting what matches the expectation of a human without actually being able to verify if what the explanation shows is what really caused the model's prediction. This paper reports on targeted investigations where we try to get more insight into the actual veracity of LIME's explanations in an audio classification task. We deliberately design adversarial examples for the classifier, in a way that gives us knowledge about which parts of the input are potentially responsible for the model's (wrong) prediction. Asking LIME to explain the predictions for these adversaries permits us to study whether local explanations do indeed detect these regions of interest. We also look at whether LIME is more successful in finding perturbations that are more prominent and easily noticeable for a human. Our results suggest that LIME does not necessarily manage to identify the most relevant input features and hence it remains unclear whether explanations are useful or even misleading.
A New Distributed Method for Training Generative Adversarial Networks

Generative adversarial networks (GANs) are emerging machine learning models for generating synthesized data similar to real data by jointly training a generator and a discriminator. In many applications, data and computational resources are distributed over many devices, so centralized computation with all data in one location is infeasible due to privacy and/or communication constraints. This paper proposes a new framework for training GANs in a distributed fashion: Each device computes a local discriminator using local data; a single server aggregates their results and computes a global GAN. Specifically, in each iteration, the server sends the global GAN to the devices, which then update their local discriminators; the devices send their results to the server, which then computes their average as the global discriminator and updates the global generator accordingly. Two different update schedules are designed with different levels of parallelism between the devices and the server. Numerical results obtained using three popular datasets demonstrate that the proposed framework can outperform a state-of-the-art framework in terms of convergence speed.
Characterization of Individual Anode Current Signals in Aluminum Reduction Cells

Differing from cell voltage and line current, anode current signals can provide an insight into the localized anodic dynamic behavior in an operating Hall–Heroult reduction cell, and can be used as an alternative in-depth method to study the process in the hostile industrial potline environment. This work involves further investigations on changes in the frequency response of anode current signals (such as peaks and magnitudes) with anode age and anodic reactions. Furthermore, two process abnormalities, anode effect and anode slippage, are studied. This study demonstrates that anode current signals provide an earlier indication of an approaching anode effect than the conventional cell voltage measurements. Frequency domain analysis has been found to be an additional identifier, in separating anode effects from other abnormalities, that can also cause anode current redistribution.
Simulation of the steady-state behaviour of a new design of a single planar Solid Oxide Fuel Cell

Abstract The aim of the work was to develop a mathematical model for computing the steady-state voltage – current characteristics of a planar Solid Oxide Fuel Cell and to determine the performance of a new SOFC design. The design involves cross-flow bipolar plates. Each of the bipolar plates has an air channel system on one side and a fuel channel system on the other side. The proposed model was developed using the ANSYS-Fluent commercial Computational Fluid Dynamics (CFD) software supported by additional Fuel Cell module. The results confirm that the model can well simulate the diagonal current path. The effects of temperature and gas flow through the channels and a Membrane Electrode Assembly (MEA) structure were taken into account. It was shown that a significant increase of the MEA temperature at high current density can lead to hot spots formation and hence electrode damage.
Explanation for the stacking disorder in tris(bicyclo[2.1.1]hexeno)benzene using lattice-energy minimisations

Abstract The stacking disorder in the hexagonal polymorph of tris(bicyclo[2.1.1]hexeno)benzene, C18H18, is explained by lattice-energy minimisations. The compound crystallises in layers with P(-6)2m layer-group symmetry. Each layer can be placed in one of three possible positions. Possible stacking sequences were derived from order-disorder (OD) theory and by a combinatorial approach. The resulting periodic model structures were optimised by lattice-energy minimisations. The calculations show that eclipsed arrangements of layers (sequence e.g. ABA) are energetically less favourable than non-eclipsed ones (sequence e.g. ABC). The reason was found in the deviation from P(-6)2m symmetry. Molecules in eclipsed layers are almost parallel to the layer plane, whereas molecules in non-eclipsed layers are inclined to it by about 3° leading to a more efficient packing. The influence of the next-nearest layers was found to be not a direct one, but mediated by the distortion of the layers between them. Using Boltzmann statistics, the stacking probabilities for all four-layer sequences were calculated. The results match well with the probabilities derived from the diffuse scattering by Bürgi et al. (2005) (Z. Kristallogr. 220, 1066–1075). The lattice-energy minimisations allowed to determine the actual local structures in all individual layers including packing effects like rotation of molecules, lateral shifts, and to calculate the stacking layer thickness, depending on the actual layer sequences. The diffraction pattern, calculated from a lattice-energy optimised structure with 54 layers, is similar to the experimental one, and even approximates the diffuse scattering.
Significance of Low-level Controller for String Stability under Adaptive Cruise Control

Current commercial adaptive cruise control (ACC) systems consist of an upper-level planner controller that decides the optimal trajectory that should be followed, and a low-level controller in charge of sending the gas/brake signals to the mechanical system to actually move the vehicle. We find that the low-level controller has a significant impact on the string stability (SS) even if the planner is string stable: (i) a slow controller deteriorates the SS, (ii) slow controllers are common as they arise from insufficient control gains, from a"weak"gas/brake system or both, and (iii) the integral term in a slow controller causes undesired overshooting which affects the SS. Accordingly, we suggest tuning up the proportional/feedforward gain and ensuring the gas/brake is not"weak". The study results are validated both numerically and empirically with data from commercial cars.