Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Calibration of a Multi-Channel Aerosol Sampler for Tropopause Region Studies from the CARIBIC Platform

An aircraft-based, multi-channel aerosol sampler for studies of the upper troposphere and lowermost stratosphere from the CARIBIC (Civil Aircraft for Regular Investigation of the Atmosphere Based on an Instrument Container) platform was designed and calibrated. The sampler operates with an impaction technique at a flow rate of 10.4 lpm and consists of sixteen sampling channels. Samples are collected in a time sequence. Each channel contains two sample types that are used for quantitative measurement of concentrations, using particle-induced X-ray emission (PIXE), and single particle analysis with electron microscopy. The minimum detection limits for PIXE analysis after 1.5 h sampling are, for example, 2.0, 0.14, and 0.02 ng/m3 STP (standard temperature and pressure) for sulfur, potassium, and nickel. Calibration included penetration studies of a cyclone arrangement used to define the upper size limit in the sampling to 2.0 μ m diameter and the collection efficiency of the impactor. Both components of the sampling system showed penetration and collection efficiency close to 100%, respectively, in the particle size range of interest. The impactor cut-off was found to be dependent on the ratio of the impactor upstream-to-downstream pressure for ratios well below the critical pressure drop (i.e., the pressure where the jet reaches sonic velocity) being 0.15 μ m and 0.08 μ m for ratios 0.41 and 0.2.
Cognitive network science for understanding online social cognitions: A brief review

Social media are digitalizing massive amounts of users' cognitions in terms of timelines and emotional content. Such Big Data opens unprecedented opportunities for investigating cognitive phenomena like perception, personality, and information diffusion but requires suitable interpretable frameworks. Since social media data come from users' minds, worthy candidates for this challenge are cognitive networks, models of cognition giving structure to mental conceptual associations. This work outlines how cognitive network science can open new, quantitative ways for understanding cognition through online media like: (i) reconstructing how users semantically and emotionally frame events with contextual knowledge unavailable to machine learning, (ii) investigating conceptual salience/prominence through knowledge structure in social discourse; (iii) studying users' personality traits like openness-to-experience, curiosity, and creativity through language in posts; (iv) bridging cognitive/emotional content and social dynamics via multilayer networks comparing the mindsets of influencers and followers. These advancements combine cognitive-, network- and computer science to understand cognitive mechanisms in both digital and real-world settings but come with limitations concerning representativeness, individual variability, and data integration. Such aspects are discussed along with the ethical implications of manipulating sociocognitive data. In the future, reading cognitions through networks and social media can expose cognitive biases amplified by online platforms and relevantly inform policy-making, education, and markets about complex cognitive trends.
Improved Regret Bound and Experience Replay in Regularized Policy Iteration

In this work, we study algorithms for learning in infinite-horizon undiscounted Markov decision processes (MDPs) with function approximation. We first show that the regret analysis of the Politex algorithm (a version of regularized policy iteration) can be sharpened from $O(T^{3/4})$ to $O(\sqrt{T})$ under nearly identical assumptions, and instantiate the bound with linear function approximation. Our result provides the first high-probability $O(\sqrt{T})$ regret bound for a computationally efficient algorithm in this setting. The exact implementation of Politex with neural network function approximation is inefficient in terms of memory and computation. Since our analysis suggests that we need to approximate the average of the action-value functions of past policies well, we propose a simple efficient implementation where we train a single Q-function on a replay buffer with past data. We show that this often leads to superior performance over other implementation choices, especially in terms of wall-clock time. Our work also provides a novel theoretical justification for using experience replay within policy iteration algorithms.
An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing

Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, by sharing the majority of hardware, signal processing modules and, in a typical case, the transmitted signal. The close cooperation of the communication and sensing functions can enable significant improvement of spectrum efficiency, reduction of device size, cost and power consumption, and improvement of performance of both functions. Advanced signal processing techniques are critical for making the integration efficient, from transmission signal design to receiver processing. This paper provides a comprehensive overview of the state-of-the-art on JCR systems from the signal processing perspective. A balanced coverage on both transmitter and receiver is provided for three types of JCR systems, namely, communication-centric, radar-centric, and joint design and optimization.
Optimising the mitigation of epidemic spreading through targeted adoption of contact tracing apps

The ongoing COVID-19 pandemic is the first epidemic in human history in which digital contact-tracing has been deployed at a global scale. Tracking and quarantining all the contacts of individuals who test positive to a virus can help slowing-down an epidemic, but the impact of contact-tracing is severely limited by the generally low adoption of contact-tracing apps in the population. We derive here an analytical expression for the effectiveness of contact-tracing app installation strategies in a SIR model on a given contact graph. We propose a decentralised heuristic to improve the effectiveness of contact tracing under fixed adoption rates, which targets a set of individuals to install contact-tracing apps, and can be easily implemented. Simulations on a large number of real-world contact networks confirm that this heuristic represents a feasible alternative to the current state of the art.
On a Networked SIS Epidemic Model With Cooperative and Antagonistic Opinion Dynamics

We propose a mathematical model to study coupled epidemic and opinion dynamics in a network of communities. Our model captures SIS epidemic dynamics, whose evolution is dependent on the opinions of the communities toward the epidemic, and vice versa. In particular, we allow both cooperative and antagonistic interactions, representing similar and opposing perspectives on the severity of the epidemic, respectively. We propose an opinion-dependent reproduction number to characterize the mutual influence between epidemic spreading and opinion dissemination over the networks. Through stability analysis of the equilibria, we explore the impact of opinions on both epidemic outbreak and eradication, characterized by bounds on the opinion-dependent reproduction number. We also show how to eradicate epidemics by reshaping the opinions, offering researchers an approach for designing control strategies to reach target audiences to ensure effective epidemic suppression.
A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping

Evidential occupancy grid maps (OGMs) are a popular representation of the environment of automated vehicles. Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds. Geometric ISMs show a limited performance when estimating states in unobserved but inferable areas and have difficulties dealing with ambiguous input. Deep learning-based ISMs face the challenge of limited training data and they often cannot handle uncertainty quantification yet. We propose a deep learning-based framework for learning an OGM algorithm which is both capable of quantifying first- and second-order uncertainty and which does not rely on manually labeled data. Results on synthetic and on real-world data show superiority over other approaches. Source code and datasets are available at https://github.com/ika-rwth-aachen/EviLOG.
Online Learning for Unknown Partially Observable MDPs

Solving Partially Observable Markov Decision Processes (POMDPs) is hard. Learning optimal controllers for POMDPs when the model is unknown is harder. Online learning of optimal controllers for unknown POMDPs, which requires efficient learning using regret-minimizing algorithms that effectively tradeoff exploration and exploitation, is even harder, and no solution exists currently. In this paper, we consider infinite-horizon average-cost POMDPs with unknown transition model, though a known observation model. We propose a natural posterior sampling-based reinforcement learning algorithm (PSRL-POMDP) and show that it achieves a regret bound of $O(\log T)$, where $T$ is the time horizon, when the parameter set is finite. In the general case (continuous parameter set), we show that the algorithm achieves $O (T^{2/3})$ regret under two technical assumptions. To the best of our knowledge, this is the first online RL algorithm for POMDPs and has sub-linear regret.
Persistent Homology and Graphs Representation Learning

This article aims to study the topological invariant properties encoded in node graph representational embeddings by utilizing tools available in persistent homology. Specifically, given a node embedding representation algorithm, we consider the case when these embeddings are real-valued. By viewing these embeddings as scalar functions on a domain of interest, we can utilize the tools available in persistent homology to study the topological information encoded in these representations. Our construction effectively defines a unique persistence-based graph descriptor, on both the graph and node levels, for every node representation algorithm. To demonstrate the effectiveness of the proposed method, we study the topological descriptors induced by DeepWalk, Node2Vec and Diff2Vec.
Improving Approximate Optimal Transport Distances using Quantization

Optimal transport (OT) is a popular tool in machine learning to compare probability measures geometrically, but it comes with substantial computational burden. Linear programming algorithms for computing OT distances scale cubically in the size of the input, making OT impractical in the large-sample regime. We introduce a practical algorithm, which relies on a quantization step, to estimate OT distances between measures given cheap sample access. We also provide a variant of our algorithm to improve the performance of approximate solvers, focusing on those for entropy-regularized transport. We give theoretical guarantees on the benefits of this quantization step and display experiments showing that it behaves well in practice, providing a practical approximation algorithm that can be used as a drop-in replacement for existing OT estimators.
Approximate Privacy-Preserving Neighbourhood Estimations

Anonymous social networks present a number of new and challenging problems for existing Social Network Analysis techniques. Traditionally, existing methods for analysing graph structure, such as community detection, required global knowledge of the graph structure. That implies that a centralised entity must be given access to the edge list of each node in the graph. This is impossible for anonymous social networks and other settings where privacy is valued by its participants. In addition, using their graph structure inputs for learning tasks defeats the purpose of anonymity. In this work, we hypothesise that one can re-purpose the use of the HyperANF a.k. a HyperBall algorithm -- intended for approximate diameter estimation -- to the task of privacy-preserving community detection for friend recommending systems that learn from an anonymous representation of the social network graph structure with limited privacy impact. This is possible because the core data structure maintained by HyperBall is a HyperLogLog with a counter of the number of reachable neighbours from a given node. Exchanging this data structure in future decentralised learning deployments gives away no information about the neighbours of the node and therefore does preserve the privacy of the graph structure.
LES: Locally Exploitative Sampling for Robot Path Planning

Sampling-based algorithms solve the path planning problem by generating random samples in the searchspace and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-space. In contrast, this work proposes an optimization-based procedure that generates new samples so as to improve the cost-to-come value of vertices in a given neighborhood. The application of the proposed algorithm adds an exploitativebias to sampling and results in a faster convergence to the optimal solution compared to other state-of-the-art sampling techniques. This is demonstrated using benchmarking experiments performed for 7 DOF Panda and 14 DOF Baxter robots.
Carbon dust in the evolved born-again planetary nebulae A 30 and A 78

We present an infrared (IR) characterization of the born-again planetary nebulae (PNe) A 30 and A 78 using IR images and spectra. We demonstrate that the carbon-rich dust in A 30 and A 78 is spatially coincident with the H-poor ejecta and coexists with hot X-ray-emitting gas up to distances of 50′′ from the central stars (CSPNs). Dust forms immediately after the born-again event and survives for 1000 yr in the harsh environment around the CSPN as it is destroyed and pushed away by radiation pressure and dragged by hydrodynamical effects. Spitzer IRS spectral maps showed that the broad spectral features at 6.4 and 8.0 μm, attributed to amorphous carbon formed in H-deficient environments, are associated with the disrupted disk around their CSPN, providing an optimal environment for charge exchange reactions with the stellar wind that produces the soft X-ray emission of these sources. Nebular and dust properties are modeled for A 30 with cloudy taking into account different carbonaceous dust species. Our models predict dust temperatures in the 40–230 K range, five times lower than predicted by previous works. Gas and dust masses for the born-again ejecta in A 30 are estimated to beMgas = (4.41+0.55 −0.14)×10 −3M andMdust = (3.20+3.21 −2.06)×10 −3M , which can be used to estimate a total ejected mass and mass-loss rate for the born-again event of (7.61+3.76 −2.20) × 10 −3 M and ¤ M = [5 − 60] × 10−5 M yr−1, respectively. Taking into account the carbon trapped into dust grains, we estimate that the C/O mass ratio of the H-poor ejecta of A 30 is larger than 1, which favors the very late thermal pulse model over the alternate hypothesis of a nova-like event.
CausalX: Causal eXplanations and Block Multilinear Factor Analysis

By adhering to the dictum, “No causation without manipulation (treatment, intervention)”, cause and effect data analysis represents changes in observed data in terms of changes in the causal factors. When causal factors are not amenable for active manipulation in the real world due to current technological limitations or ethical considerations, a counterfactual approach performs an intervention on the model of data formation. In the case of object representation or activity (temporal object) representation, varying object parts is generally unfeasible whether they be spatial and/or temporal. Multilinear algebra, the algebra of higher order tensors, is a suitable and transparent framework for disentangling the causal factors of data formation. Learning a part-based intrinsic causal factor representations in a multilinear framework requires applying a set of interventions on a part-based multilinear model. We propose a unified multilinear model of wholes and parts. We derive a hierarchical block multilinear factorization, the $M$-mode Block SVD, that computes a disentangled representation of the causal factors by optimizing simultaneously across the entire object hierarchy. Given computational efficiency considerations, we introduce an incremental bottom-up computational alternative, the Incremental $M$-mode Block SVD, that employs the lower level abstractions, the part representations, to represent the higher level of abstractions, the parent wholes. This incremental computational approach may also be employed to update the causal model parameters when data becomes available incrementally. The resulting object representation is an interpretable combinatorial choice of intrinsic causal factor representations related to an object's recursive hierarchy of wholes and parts that renders object recognition robust to occlusion and reduces training data requirements.
Automatic Story Generation: Challenges and Attempts

Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. The best human-crafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of-the-art still struggle to produce, even using transformer architectures. In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions, and (4) generate creative language.
Structural and Absorption Characteristics of Mn-Ti Substituted Ba-Sr Hexaferrite Synthesized by Mechanical Alloying Route

The synthesis and characterization of composition (Ba,Sr)O.6Fe2(1-x)(Mn,Ti)xO3 (x = 0; 0.25; and 0.5) compound by using solid state reaction have been performed. The raw materials were BaCO3, SrCO3, Fe2O3, MnCO3, and TiO2. The mixed powder was compacted at 5000 psi into pellets and sintered at 1050°C in the air at atmosphere pressure for 15 hour and furnace cooling. X-ray diffraction studies indicate expansion of hexagonal unit cell and compression of atomic density with substitution of Mn 2+ and Ti 4+ ions. The substitution increases density and intergrain connectivity in the microstructure of sintered samples. Effect of substitution upon magnetic properties revealed that total magnetization, remanence and coercivity changed with substitution due to preferential site occupancy of substituted Mn 2+ and Ti 4+ ions. Since the coercivity and total magnetization may be controlled by substitution while
The Power of D-hops in Matching Power-Law Graphs

This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law degree distribution. A set of correctly matched vertex-pairs is chosen at random and revealed as initial seeds. Our goal is to use the seeds to recover the remaining latent vertex correspondence between the two graphs. Departing from the existing approaches that focus on the use of high-degree seeds in $1$-hop neighborhoods, we develop an efficient algorithm that exploits the low-degree seeds in suitably-defined D-hop neighborhoods. Specifically, we first match a set of vertex-pairs with appropriate degrees (which we refer to as the first slice) based on the number of low-degree seeds in their D-hop neighborhoods. This approach significantly reduces the number of initial seeds needed to trigger a cascading process to match the rest of graphs. Under the Chung-Lu random graph model with n vertices, max degree Θ(√n), and the power-law exponent 2 4-β/3-β, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only Ω((log n)4-β) initial seeds. Our result achieves an exponential reduction in the seed size requirement, as the best previously known result requires n1/2+ε seeds (for any small constant ε>0). Performance evaluation with synthetic and real data further corroborates the improved performance of our algorithm.
Stochastic Aggregation in Graph Neural Networks

Graph neural networks (GNNs) manifest pathologies including over-smoothing and limited discriminating power as a result of suboptimally expressive aggregating mechanisms. We herein present a unifying framework for stochastic aggregation (STAG) in GNNs, where noise is (adaptively) injected into the aggregation process from the neighborhood to form node embeddings. We provide theoretical arguments that STAG models, with little overhead, remedy both of the aforementioned problems. In addition to fixed-noise models, we also propose probabilistic versions of STAG models and a variational inference framework to learn the noise posterior. We conduct illustrative experiments clearly targeting oversmoothing and multiset aggregation limitations. Furthermore, STAG enhances general performance of GNNs demonstrated by competitive performance in common citation and molecule graph benchmark datasets.
Performance Comparison for Scientific Computations on the Edge via Relative Performance

In a typical Internet-of-Things setting that involves scientific applications, a target computation can be evaluated in many different ways depending on the split of computations among various devices. On the one hand, different implementations (or algorithms)—equivalent from a mathematical perspective—might exhibit significant difference in terms of performance. On the other hand, some of the implementations are likely to show similar performance characteristics. In this paper, we focus on analysing the performance of a given set of algorithms by clustering them into performance classes. To this end, we use a measurement-based approach to evaluate and score algorithms based on pair-wise comparisons; we refer to this approach as "Relative performance analysis". Each comparison yields one of three outcomes: one algorithm can be "better", "worse", or "equivalent" to another; those algorithms evaluating to have "equivalent" performance are merged into the same performance class. We show that our clustering methodology facilitates algorithm selection with respect to more than one metric; for instance, from the subset of equivalently fast algorithms, one could then select an algorithm that consumes the least energy on a certain device.
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally

We study the statistical limits of Imitation Learning (IL) in episodic Markov Decision Processes (MDPs) with a state space S. We focus on the known-transition setting where the learner is provided a dataset of N length-H trajectories from a deterministic expert policy and knows the MDP transition. We establish an upper bound O(|S|H3/2/N) for the suboptimality using the Mimic-MD algorithm in Rajaraman et al. (2020) which we prove to be computationally efficient. In contrast, we show the minimax suboptimality grows as Ω(H/N) when |S| ≥ 3 while the unknown-transition setting suffers from a larger sharp rate Θ(|S|H2/N) (Rajaraman et al., 2020). The lower bound is established by proving a two-way reduction between IL and the value estimation problem of the unknown expert policy under any given reward function, as well as building connections with linear functional estimation with subsampled observations. We further show that under the additional assumption that the expert is optimal for the true reward function, there exists an efficient algorithm, which we term as Mimic-Mixture, that provably achieves suboptimality O(1/N) for arbitrary 3-state MDPs with rewards only at the terminal layer. In contrast, no algorithm can achieve suboptimality O( √ H/N) with high probability if the expert is not constrained to be optimal. Our work formally establishes the benefit of the expert optimal assumption in the known transition setting, while Rajaraman et al. (2020) showed it does not help when transitions are unknown.