Miracle-dz/UniFAR-FTUs huggingface dataset:
Dead or alive: Distinguishing active from passive particles using supervised learning

A longstanding open question in the field of dense disordered matter is how precisely structure and dynamics are related to each other. With the advent of machine learning, it has become possible to agnostically predict the dynamic propensity of a particle in a dense liquid based on its local structural environment. Thus far, however, these machine-learning studies have focused almost exclusively on simple liquids composed of passive particles. Here we consider a mixture of both passive and active (i.e., self-propelled) Brownian particles, with the aim to identify the active particles from minimal local structural information. We compare a state-of-the-art machine learning approach for passive systems with a new method we develop based on Voronoi tessellation. Both methods accurately identify the active particles based on their structural properties at high activity and low concentrations of active particles. Our Voronoi method is, however, substantially faster to train and deploy because it requires fewer, and easy to compute, input features. Notably, both become ineffective when the activity is low, suggesting a fundamentally different structural signature for dynamic propensity and non-equilibrium activity. Ultimately, these efforts might also find relevance in the context of biological active glasses such as confluent cell layers, where subtle changes in the microstructure can hint at pathological changes in cell dynamics.
A note on limits of sequences of binary trees

We discuss a notion of convergence for binary trees that is based on subtree sizes. In analogy to recent developments in the theory of graphs, posets and permutations we investigate some general aspects of the topology, such as a characterization of the set of possible limits and its structure as a metric space. For random trees the subtree size topology arises in the context of algorithms for searching and sorting when applied to random input, resulting in a sequence of nested trees. For these we obtain a structural result based on a local version of exchangeability. This in turn leads to a central limit theorem, with possibly mixed asymptotic normality.
SeeChart: Enabling Accessible Visualizations Through Interactive Natural Language Interface For People with Visual Impairments

Web-based data visualizations have become very popular for exploring data and communicating insights. Newspapers, journals, and reports regularly publish visualizations to tell compelling stories with data. Unfortunately, most visualizations are inaccessible to readers with visual impairments. For many charts on the web, there are no accompanying alternative (alt) texts, and even if such texts exist they do not adequately describe important insights from charts. To address the problem, we first interviewed 15 blind users to understand their challenges and requirements for reading data visualizations. Based on the insights from these interviews, we developed SeeChart, an interactive tool that automatically deconstructs charts from web pages and then converts them to accessible visualizations for blind people by enabling them to hear the chart summary as well as to interact through data points using the keyboard. Our evaluation with 14 blind participants suggests the efficacy of SeeChart in understanding key insights from charts and fulfilling their information needs while reducing their required time and cognitive burden.
Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets

Identifying predictive factors for an outcome of interest via a multivariable analysis is often difficult when the data set is small. Combining data from different medical centers into a single (larger) database would alleviate this problem, but is in practice challenging due to regulatory and logistic problems. Federated Learning (FL) is a machine learning approach that aims to construct from local inferences in separate data centers what would have been inferred had the data sets been merged. It seeks to harvest the statistical power of larger data sets without actually creating them. The FL strategy is not always efficient and precise. Therefore, in this paper we refine and implement an alternative Bayesian Federated Inference (BFI) framework for multicenter data with the same aim as FL. The BFI framework is designed to cope with small data sets by inferring locally not only the optimal parameter values, but also additional features of the posterior parameter distribution, capturing information beyond what is used in FL. BFI has the additional benefit that a single inference cycle across the centers is sufficient, whereas FL needs multiple cycles. We quantify the performance of the proposed methodology on simulated and real life data.
The Isaac Newton Telescope Monitoring Survey of Local Group Dwarf Galaxies. VI. The Star Formation History and Dust Production in Andromeda IX

We present a photometric study of the resolved stellar populations in Andromeda IX (And IX), the closest satellite to M31, a metal-poor and low-mass dwarf spheroidal galaxy. We estimate a distance modulus of 24.56−0.15+0.05 mag based on the tip of the red giant branch. By probing the variability of asymptotic giant branch stars, we study the star formation history of And IX. We identified 50 long-period variables (LPVs) in And IX using the Isaac Newton Telescope in two filters, the Sloan i′ and Harris V. In this study, we selected LPVs within two half-light radii with amplitudes in the range of 0.2–2.20 mag. It is found that the peak of star formation reaches ∼8.2 ± 3.1 × 10−4 M ⊙ yr−1 at ≈6 Gyr ago. Our findings suggest an outside-in galaxy formation scenario for And IX with a quenching occurring 3.65−0.13+1.52 Gyr ago with a star formation rate (SFR) in the order of 2.0 × 10−4 M ⊙ yr−1 at redshift <0.5. We calculate the total stellar mass by integrating the SFR within two half-light radii ∼3.0 × 105 M ⊙. By employing spectral energy distribution fitting for the observed LPVs in And IX, we evaluate a mass-loss rate in the range of 10−7 ≤ Ṁ ≤ 10−5 M ⊙ yr−1. Finally, we show that the total mass deposition to the interstellar medium (ISM) is ∼2.4 × 10−4 M ⊙ yr−1 from the C- and O-rich types of dust-enshrouded LPVs. The ratio of the total mass returned to the ISM by LPVs to the total stellar mass is ∼8.0 × 10−10 yr−1, and so at this rate it would take ∼1 Gyr to reproduce this galaxy.
Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware

Logic-in-memory (LIM) describes the execution of logic gates within memristive crossbar structures, promising to improve performance and energy efficiency. Utilizing only binary values, LIM particularly excels in accelerating binary neural networks, shifting it in the focus of edge applications. Considering its potential, the impact of faults on BNNs accelerated with LIM still lacks investigation. In this paper, we propose faulty logic-in-memory (FLIM), a fault injection platform capable of executing full-fledged BNNs on LIM while injecting in-field faults. The results show that FLIM runs a single MNIST picture 66754× faster than the state of the art by offering a fine-grained fault injection methodology.
CERiL: Continuous Event-based Reinforcement Learning

This paper explores the potential of event cameras to enable continuous time reinforcement learning. We formalise this problem where a continuous stream of unsynchronised observations is used to produce a corresponding stream of output actions for the environment. This lack of synchronisation enables greatly enhanced reactivity. We present a method to train on event streams derived from standard RL environments, thereby solving the proposed continuous time RL problem. The CERiL algorithm uses specialised network layers which operate directly on an event stream, rather than aggregating events into quantised image frames. We show the advantages of event streams over less-frequent RGB images. The proposed system outperforms networks typically used in RL, even succeeding at tasks which cannot be solved traditionally. We also demonstrate the value of our CERiL approach over a standard SNN baseline using event streams.
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank

Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker. In this paper, we introduce our winning approach for the"Unbiased Learning to Rank"task in WSDM Cup 2023. We find that the provided data is severely biased so neural models trained directly with the top 10 results with click information are unsatisfactory. So we extract multiple heuristic-based features for multi-fields of the results, adjust the click labels, add true negatives, and re-weight the samples during model training. Since the propensities learned by existing ULTR methods are not decreasing w.r. t. positions, we also calibrate the propensities according to the click ratios and ensemble the models trained in two different ways. Our method won the 3rd prize with a DCG@10 score of 9.80, which is 1.1% worse than the 2nd and 25.3% higher than the 4th.
Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.
Quantum Fractionary Cosmology: K-Essence Theory

Using a particular form of the quantum K-essence scalar field, we show that in the quantum formalism, a fractional differential equation in the scalar field variable, for some epochs in the Friedmann–Lemaı^tre–Robertson–Walker (FLRW) model (radiation and inflation-like epochs, for example), appears naturally. In the classical analysis, the kinetic energy of scalar fields can falsify the standard matter in the sense that we obtain the time behavior for the scale factor in all scenarios of our Universe by using the Hamiltonian formalism, where the results are analogous to those obtained by an algebraic procedure in the Einstein field equations with standard matter. In the case of the quantum Wheeler–DeWitt (WDW) equation for the scalar field ϕ, a fractional differential equation of order β=2α2α−1 is obtained. This fractional equation belongs to different intervals, depending on the value of the barotropic parameter; that is to say, when ωX∈[0,1], the order belongs to the interval 1≤β≤2, and when ωX∈[−1,0), the order belongs to the interval 0
Fairness in Socio-technical Systems: a Case Study of Wikipedia

Problems broadly known as algorithmic bias frequently occur in the context of complex socio-technical systems (STS), where observed biases may not be directly attributable to a single automated decision algorithm. As a first investigation of fairness in STS, we focus on the case of Wikipedia. We systematically review 75 papers describing different types of bias in Wikipedia, which we classify and relate to established notions of harm from algorithmic fairness research. By analysing causal relationships between the observed phenomena, we demonstrate the complexity of the socio-technical processes causing harm. Finally, we identify the normative expectations of fairness associated with the different problems and discuss the applicability of existing criteria proposed for machine learning-driven decision systems.
Optical Networks and Interconnects

The rapid evolution of communication technologies such as 5G and beyond, rely on optical networks to support the challenging and ambitious requirements that include both capacity and reliability. This chapter begins by giving an overview of the evolution of optical access networks, focusing on Passive Optical Networks (PONs). The development of the different PON standards and requirements aiming at longer reach, higher client count and delivered bandwidth are presented. PON virtualization is also introduced as the flexibility enabler. Triggered by the increase of bandwidth supported by access and aggregation network segments, core networks have also evolved, as presented in the second part of the chapter. Scaling the physical infrastructure requires high investment and hence, operators are considering alternatives to optimize the use of the existing capacity. This chapter introduces different planning problems such as Routing and Spectrum Assignment problems, placement problems for regenerators and wavelength converters, and how to offer resilience to different failures. An overview of control and management is also provided. Moreover, motivated by the increasing importance of data storage and data processing, this chapter also addresses different aspects of optical data center interconnects. Data centers have become critical infrastructure to operate any service. They are also forced to take advantage of optical technology in order to keep up with the growing capacity demand and power consumption. This chapter gives an overview of different optical data center network architectures as well as some expected directions to improve the resource utilization and increase the network capacity.
Dielectric properties of fine‐grained barium titanate ceramics

Dielectric properties, lattice‐ and microstructure of ceramic BaTiO3 showing grain sizes of 0.3–100 μm were studied. At grain sizes <10 μm the width of ferroelectric 90° domains decreases proportionally to the square root of the grain diameter. The decreasing width of the domains can be theoretically explained by the equilibrium of elastic field energy and domain wall energy. The smaller the grains, the more the dielectric and the elastic constants are determined by the contribution of 90° domain walls. The permittivity below the Curie point shows a pronounced maximum er ≊5000 at grain sizes 0.8–1 μm. At grain sizes <0.7 μm the permittivity strongly decreases and the lattice gradually changes from tetragonal to pseudocubic.
koopmans: An Open-Source Package for Accurately and Efficiently Predicting Spectral Properties with Koopmans Functionals

Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional framework. These functionals impose a generalized piecewise linearity condition to the entire electronic manifold, ensuring that orbital energies match the corresponding electron removal/addition energy differences (in contrast to semilocal DFT, where a mismatch between the two lies at the heart of the band gap problem and, more generally, the unreliability of Kohn–Sham orbital energies). This strategy has proven to be very powerful, yielding molecular orbital energies and solid-state band structures with comparable accuracy to many-body perturbation theory but at greatly reduced computational cost while preserving a functional formulation. This paper reviews the theory of Koopmans functionals, discusses the algorithms necessary for their implementation, and introduces koopmans, an open-source package that contains all of the code and workflows needed to perform Koopmans functional calculations and obtain reliable spectral properties of molecules and materials.
On Graph-based Reentrancy-free Semantic Parsing

We propose a novel graph-based approach for semantic parsing that resolves two problems observed in the literature: (1) seq2seq models fail on compositional generalization tasks; (2) previous work using phrase structure parsers cannot cover all the semantic parses observed in treebanks. We prove that both MAP inference and latent tag anchoring (required for weakly-supervised learning) are NP-hard problems. We propose two optimization algorithms based on constraint smoothing and conditional gradient to approximately solve these inference problems. Experimentally, our approach delivers state-of-the-art results on GeoQuery, Scan, and Clevr, both for i.i. d. splits and for splits that test for compositional generalization.
Weighted Tur\'an theorems with applications to Ramsey-Tur\'an type of problems

We study extensions of Tur\'an Theorem in edge-weighted settings. A particular case of interest is when constraints on the weight of an edge come from the order of the largest clique containing it. These problems are motivated by Ramsey-Tur\'an type problems. Some of our proofs are based on the method of graph Lagrangians, while the other proofs use flag algebras. Using these results, we prove several new upper bounds on the Ramsey-Tur\'an density of cliques. Other applications of our results are in a recent paper of Balogh, Chen, McCourt and Murley.
Prioritized offline Goal-swapping Experience Replay

In goal-conditioned offline reinforcement learning, an agent learns from previously collected data to go to an arbitrary goal. Since the offline data only contains a finite number of trajectories, a main challenge is how to generate more data. Goal-swapping generates additional data by switching trajectory goals but while doing so produces a large number of invalid trajectories. To address this issue, we propose prioritized goal-swapping experience replay (PGSER). PGSER uses a pre-trained Q function to assign higher priority weights to goal swapped transitions that allow reaching the goal. In experiments, PGSER significantly improves over baselines in a wide range of benchmark tasks, including challenging previously unsuccessful dexterous in-hand manipulation tasks.
ONERA Three-Dimensional Icing Model

A three-dimensional icing model has been developed at ONERA to calculate ice accretion shapes for aerodynamic components that can not be predicted using conventional two-dimensional codes. It is described, emphasizing the original parts with respect to the two-dimensional existing models. The model includes Euler inviscid flow calculation. Droplet trajectories are calculated in a three-dimensional grid. The remesh on the leading edge is adapted to follow aerodynamics singularities. The boundary layer is calculated using a mixing length formulation to model the wall roughness influence on convective heat transfer. Runback paths are integrated. The heat balance is calculated in a grid created along the runback paths. The domain of validity of the three-dimensional icing code is described; compared with the two-dimensional model this domain is wider, especially for high speeds. The three-dimensional model is shown to simulate well a uniform ice deposit on a three-dimensional rotor blade tip. Then, a comparison of the three- and two-dimensional codes on an infinite swept wing shows that the corrected two-dimensional code predicts the catch efficiency but not the ice shape. Finally, it is shown that the continuum flux hypothesis prevents the three-dimensional model from simulating correctly the "lobster tail" ice shape (nonuniform ice deposit).
Investigating the Superorbital Modulations in 4U 1909 + 07, IGR J16418-4532, and IGR J16479-4514 with Swift XRT, BAT, and NuSTAR Observations

A puzzling variety of superorbital modulations has been discovered in several supergiant high mass X-ray binaries (sgHMXBs). To investigate the mechanisms driving these superorbital modulations, we have analyzed long-term Neil Gehrels Swift Observatory (Swift) Burst Alert Telescope (BAT) observations of three sgHMXBs: 4U 1909 + 07, IGR J16418–4532, and IGR J16479–4514, and constructed their dynamic power spectra and superorbital intensity profiles. These Swift BAT observations are complemented by pointed Swift X-ray Telescope (XRT) and Nuclear Spectroscopic Telescope Array (NuSTAR) observations performed near the predicted maximum and minimum phase of a single superorbital cycle for each of these sources. The BAT dynamic power spectra show changes in the strength of the superorbital modulation on timescales of years, with either the peak at the fundamental frequency and/or the second harmonic present at different times for all three sources. The pointed Swift XRT and NuSTAR observations show no significant differences between the pulse profiles and spectral parameters at the superorbital maximum and minimum phase. This is likely due to the fact the superorbital modulation had weakened significantly during the times when the NuSTAR observations were carried out for all three sources. The results from the Swift XRT, BAT, and NuSTAR analysis indicate the possible presence of multiple corotating interaction regions (CIRs) in the stellar winds of the supergiant stars, although a structured stellar wind from the supergiant star due to tidal oscillations cannot be ruled out.
SPICES: a 1.5-m space coronagraph for spectro-polarimetric characterization of cold exoplanets

The study of the physico-chemical properties of wide-separated exoplanets (> 1 AU) is a major goal of high-contrast imaging techniques. SPICES (Spectro-Polarimetric Imaging and Characterization of Exoplanetary Systems) is a project of space coronagraph dedicated to the spectro-polarimetric analysis of gas and ice giant planets, super-Earths and circumstellar disks in visible light at a spectral resolution of about 40. After recalling the science cases of the mission, we describe the optical design and the critical subsystems of the instrument. We then discuss the SPICES performance that we derived from numerical simulations.
Assessing enactment of content regulation policies: A post hoc crowd-sourced audit of election misinformation on YouTube

With the 2022 US midterm elections approaching, conspiratorial claims about the 2020 presidential elections continue to threaten users’ trust in the electoral process. To regulate election misinformation, YouTube introduced policies to remove such content from its searches and recommendations. In this paper, we conduct a 9-day crowd-sourced audit on YouTube to assess the extent of enactment of such policies. We recruited 99 users who installed a browser extension that enabled us to collect up-next recommendation trails and search results for 45 videos and 88 search queries about the 2020 elections. We find that YouTube’s search results, irrespective of search query bias, contain more videos that oppose rather than support election misinformation. However, watching misinformative election videos still lead users to a small number of misinformative videos in the up-next trails. Our results imply that while YouTube largely seems successful in regulating election misinformation, there is still room for improvement.
Deep Anomaly Detection under Labeling Budget Constraints

Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection. In this paper, we determine a set of theoretical conditions under which anomaly scores generalize from labeled queries to unlabeled data. Motivated by these results, we propose a data labeling strategy with optimal data coverage under labeling budget constraints. In addition, we propose a new learning framework for semi-supervised AD. Extensive experiments on image, tabular, and video data sets show that our approach results in state-of-the-art semi-supervised AD performance under labeling budget constraints.