Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards General Function Approximation in Zero-Sum Markov Games

This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value function or the model is parameterized by general function classes. Provably efficient algorithms for both decoupled and {coordinated} settings are developed. In the {decoupled} setting where the agent controls a single player and plays against an arbitrary opponent, we propose a new model-free algorithm. The sample complexity is governed by the Minimax Eluder dimension -- a new dimension of the function class in Markov games. As a special case, this method improves the state-of-the-art algorithm by a $\sqrt{d}$ factor in the regret when the reward function and transition kernel are parameterized with $d$-dimensional linear features. In the {coordinated} setting where both players are controlled by the agent, we propose a model-based algorithm and a model-free algorithm. In the model-based algorithm, we prove that sample complexity can be bounded by a generalization of Witness rank to Markov games. The model-free algorithm enjoys a $\sqrt{K}$-regret upper bound where $K$ is the number of episodes.
Simulation-Based Optimization of High-Performance Wheel Loading

Having smart and autonomous earthmoving in mind, we explore high-performance wheel loading in a simulated environment. This paper introduces a wheel loader simulator that combines contacting 3D multibody dynamics with a hybrid continuum-particle terrain model, supporting realistic digging forces and soil displacements at real-time performance. A total of 270,000 simulations are run with different loading actions, pile slopes, and soil to analyze how they affect the loading performance. The results suggest that the preferred digging actions should preserve and exploit a steep pile slope. High digging speed favors high productivity, while energy-efficient loading requires a lower dig speed.
Imaging low-mass planets within the habitable zones of nearby stars with ground-based mid-infrared imaging

Giant exoplanets on 10-100 au orbits have been directly imaged around young stars. The peak of the thermal emission from these warm young planets is in the near-infrared (∼1-5 µm), whereas mature, temperate exoplanets (i.e., those within their stars’ habitable zones) radiate primarily in the mid-infrared (mid-IR: ∼10 µm). If the background noise in the mid-IR can be mitigated, then exoplanets with low masses–including rocky exoplanets–can potentially be imaged in very deep exposures. Here, we review the recent results of the Breakthrough Watch/New Earths in the Alpha Centauri Region (NEAR) program on the Very Large Telescope (VLT) in Chile. NEAR pioneered a ground-based mid-IR observing approach designed to push the capabilities for exoplanet imaging with a specific focus on the closest stellar system, α Centauri. NEAR combined several new optical technologies–including a mid-IR optimized coronagraph, adaptive optics system, and rapid chopping strategy to mitigate noise from the central star and thermal background within the habitable zone. We focus on the lessons of the VLT/NEAR campaign to improve future instrumentation specifically on strategies to improve noise mitigation through chopping. We also present the design and commissioning of the Large Binocular Telescope’s Exploratory Survey for Super-Earths Orbiting Nearby Stars (LESSONS), an experiment in the Northern hemisphere that is building on what was learned from NEAR to further push the sensitivity of mid-IR imaging. Finally, we briefly discuss some of the possibilities that mid-IR imaging will enable for exoplanet science.
Optimization of EMCCD operating parameters for the acquisition system of SPARC4

Abstract. We present the optimization method for the electron multiplying charge coupled devices of the acquisition system of the SPARC4 (OMASS4). The OMASS4 uses as figures of merit the signal-to-noise ratio (SNR) and the acquisition rate (AR) as a function of the operation mode of the CCDs. Three different modes of optimization are included in the OMASS4: (1) optimization of SNR only; (2) optimization of AR only; and (3) optimization of both SNR and AR simultaneously. The first two modes calculate an analytical maximization of the cost function, whereas the third mode uses the Bayesian optimization method to determine the optimum mode of operation. We apply the OMASS4 to find the optimum mode for observations obtained at the Pico dos Dias Observatory, Brazil and compare the delivered modes of operation and its performance with the ones adopted by the observer. If the OMASS4 had been used as a tool to optimize the CCDs in all of these nights, it would be possible to improve their efficiency in 97.17%, 65.08%, and 77.66% for the optimization modes 1, 2, and 3, respectively.
Effect of varying preheating temperatures in electron beam powder bed fusion: Part I Assessment of the effective powder cake thermal conductivity

One of the major barriers in adapting the existing EB-PBF process parameters to a new powder material system is controlling the preheating conditions such that every layer of powder results in enough partial sintering to create a coherent powder cake. To be able to understand the powder sintering process and adapt it to other materials, we must look at the degree of sintering and the effective thermal conductivity of the powder bed. An in-depth understanding of these characteristics will help tailor the preheating conditions and furthermore, make it easier to remove/de-powder intricate parts after build completion without compromising the advantages of the preheating phenomenon. This study evaluates the impact of preheating temperature on the insitu powder cake properties. Three different preheat temperatures, 650 °C, 690 °C and 730 °C, are employed to a Ti-6Al-4V powder cake and in each standalone build, unique powder-capture artefacts are fabricated to be able to analyze the in-situ powder cake properties using X-ray computed tomography. An empirically-derived model for thermal conductivity of the powder cake as a result of changing the preheating temperatures, was obtained. The results demonstrated that the effective thermal conductivity of the powder cake at a given preheating temperature strongly depends on the packing density, contact size ratio and coordination number. An increase in preheating temperature, lead to a linear increase in the packing density (from 58.42 ± 1.10 % to 61.87 ± 0.96 %), contact size ratio (from 0.45 ± 0.004 to 0.48 ± 0.003), coordination number (from 3.36 ± 0.05 to 3.58 ± 0.05) and the effective thermal conductivity (from 1.75 ± 0.07 W/m/K to 2.11 ± 0.07 W/m/K) of the powder cake. The findings in this work will be deployed to assess and to correlate the effects of different powder conductivity and Preheat Themes on the surface topography and geometric fidelity of components with simple and complex geometries.
Fine structure of current noise spectra in nanoelectromechanical resonators

We study frequency dependent noise of a suspended carbon nanotube quantum dot nanoelectromechanical resonator induced by electron-vibration coupling. By using rigorous Keldysh diagrammatic technique, we build a formal framework to connect the vibration properties and the electrical measurement. We find that the noise power spectrum has a narrow resonant peak at the frequency of vibrational modes. This fine structure feature disappears due to a coherent cancellation effect when tuning tunneling barriers to a symmetric point. We note that measuring the electrical current noise spectra provides an alternative and ultra-sensitive detection method for determining the damping and dephasing of the quantum vibration modes.
Simulation as Experiment: An Empirical Critique of Simulation Research on Recommender Systems

Simulation can enable the study of recommender system (RS) evolution while circumventing many of the issues of empirical longitudinal studies; simulations are comparatively easier to implement, are highly controlled, and pose no ethical risk to human participants. How simulation can best contribute to scientific insight about RS alongside qualitative and quantitative empirical approaches is an open question. Philosophers and researchers have long debated the epistemological nature of simulation compared to wholly theoretical or empirical methods. Simulation is often implicitly or explicitly conceptualized as occupying a middle ground between empirical and theoretical approaches, allowing researchers to realize the benefits of both. However, what is often ignored in such arguments is that without firm grounding in any single methodological tradition, simulation studies have no agreed upon scientific norms or standards, resulting in a patchwork of theoretical motivations, approaches, and implementations that are difficult to reconcile. In this position paper, we argue that simulation studies of RS are conceptually similar to empirical experimental approaches and therefore can be evaluated using the standards of empirical research methods. Using this empirical lens, we argue that the combination of high heterogeneity in approaches and low transparency in methods in simulation studies of RS has limited their interpretability, generalizability, and replicability. We contend that by adopting standards and practices common in empirical disciplines, simulation researchers can mitigate many of these weaknesses.
Combined Radar and Communications With Phase-Modulated Frequency Permutations

This paper focuses on the combined radar and communications problem and conducts a thorough analytical investigation on the effect of phase and frequency change on the communication and sensing functionality. First, we consider the classical stepped frequency radar waveform and modulate data using $M$ -ary phase shift keying (MPSK). Two important analytical tools in radar waveform design, namely the ambiguity function (AF) and the Fisher information matrix (FIM) are derived, based on which, we make the important conclusion that MPSK modulation has a negligible effect on radar local accuracy. Next, we extend the analysis to incorporate frequency permutations and propose a new signalling scheme in which the mapping between incoming data and waveforms is performed based on an efficient combinatorial transform called the Lehmer code. We also provide an efficient communications receiver based on a modified Hungarian algorithm. From the communications perspective, we consider the optimal maximum likelihood (ML) detector and derive the union bound and nearest neighbour approximation on the block error probability. From the radar sensing perspective, we discuss the broader structure of the waveform based on the AF derivation and quantify the radar local accuracy based on the FIM. Extensive numerical examples are provided to illustrate the accuracy of our results.
On the Quantum-like Contextuality of Ambiguous Phrases

Language is contextual as meanings of words are dependent on their contexts. Contextuality is, concomitantly, a well-defined concept in quantum mechanics where it is considered a major resource for quantum computations. We investigate whether natural language exhibits any of the quantum mechanics’ contextual features. We show that meaning combinations in ambiguous phrases can be modelled in the sheaf-theoretic framework for quantum contextuality, where they can become possibilistically contextual. Using the framework of Contextuality-by-Default (CbD), we explore the probabilistic variants of these and show that CbD-contextuality is also possible.
The Quest for the Missing Dust. I. Restoring Large-scale Emission in Herschel Maps of Local Group Galaxies

Because the galaxies of the Local Group have such large angular sizes, much of their diffuse, large-angular-scale emission is filtered out by the Herschel data reduction process. In this work, we restore this previously missed dust in Herschel observations of the Large Magellanic Cloud, Small Magellanic Cloud, M31, and M33. We do this by combining Herschel data (including new reductions for the Magellanic Clouds), in Fourier space, with lower-resolution data from all-sky surveys (Planck, IRAS, and COBE) that did not miss the extended emission. With these new maps, we find that a significant amount of emission was missing from uncorrected Herschel data of these galaxies: over 20% in some bands. Our new photometry also resolves the disagreement between fluxes reported from older HERITAGE Magellanic Cloud Herschel reductions and fluxes reported from other telescopes. More emission is restored in shorter-wavelength bands, especially in the galaxies’ peripheries, making these regions 20%–40% bluer than before. We also find that the Herschel-PACS instrument response conflicts with the all-sky data, over the 20′–90′ angular scales to which they are both sensitive, by up to 31%. By binning our new data based on hydrogen column density, we are able to detect emission from dust at low interstellar medium densities (at ΣH < 1 M ⊙ pc−2 in some cases), and are able to detect emission at much lower densities (a factor of 2.2 lower on average, and more than a factor of 7 lower in several cases) than was possible with uncorrected data.
Recognizing Emotions evoked by Movies using Multitask Learning

Understanding the emotional impact of movies has become important for affective movie analysis, ranking, and indexing. Methods for recognizing evoked emotions are usually trained on human annotated data. Concretely, viewers watch video clips and have to manually annotate the emotions they experienced while watching the videos. Then, the common practice is to aggregate the different annotations, by computing average scores or majority voting, and train and test models on these aggregated annotations. With this procedure a single aggregated evoked emotion annotation is obtained per each video. However, emotions experienced while watching a video are subjective: different individuals might experience different emotions. In this paper, we model the emotions evoked by videos in a different manner: instead of modeling the aggregated value we jointly model the emotions experienced by each viewer and the aggregated value using a multi-task learning approach. Concretely, we propose two deep learning architectures: a Single-Task (ST) architecture and a Multi-Task (MT) architecture. Our results show that the MT approach can more accurately model each viewer and the aggregated annotation when compared to methods that are directly trained on the aggregated annotations. Furthermore, our approach outperforms the current state-of-the-art results on the COGNIMUSE benchmark.
ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation

We describe an unsupervised domain adaptation method for image content shift caused by viewpoint changes for a semantic segmentation task. Most existing methods perform domain alignment in a shared space and assume that the mapping from the aligned space to the output is transferable. However, the novel content induced by viewpoint changes may nullify such a space for effective alignments, thus resulting in negative adaptation. Our method works without aligning any statistics of the images between the two domains. Instead, it utilizes a view transformation network trained only on color images to hallucinate the semantic images for the target. Despite the lack of supervision, the view transformation network can still generalize to semantic images thanks to the inductive bias introduced by the attention mechanism. Furthermore, to resolve ambiguities in converting the semantic images to semantic labels, we treat the view transformation network as a functional representation of an unknown mapping implied by the color images and propose functional label hallucination to generate pseudo-labels in the target domain. Our method surpasses baselines built on state-of-the-art correspondence estimation and view synthesis methods. Moreover, it outperforms the state-of-the-art unsupervised domain adaptation methods that utilize self-training and adversarial domain alignment. Our code and dataset will be made publicly available.
Hot Extended Galaxy Halos around Local L* Galaxies from Sunyaev–Zeldovich Measurements

Most of the baryons in L* galaxies are unaccounted for and are predicted to lie in hot gaseous halos (T ∼ 106.5 K) that may extend beyond R 200. A hot gaseous halo will produce a thermal Sunyaev–Zeldovich signal that is proportional to the product of the gas mass and the mass-weighted temperature. To best detect this signal, we used a Needlet Independent Linear Combination all-sky Planck map that we produced from the most recent Planck data release, also incorporating WMAP data. The sample is 12 L* spiral galaxies with distances of 3−10 Mpc, which are spatially resolved so that contamination from the optical galaxy can be excluded. One galaxy, NGC 891, has a particularly strong SZ signal, and when excluding it, the stack of 11 galaxies is detected at about 4σ (declining with radius) and is extended to at least 250 kpc (≈R 200) at >99% confidence. The gas mass within a spherical volume to a radius of 250 kpc is 9.8 ± 2.8 × 1010 M ⊙, for T avg = 3 × 106 K. This is about 30% of the predicted baryon content of the average galaxy (3.1 × 1011 M ⊙), and about equal to the mass of stars, disk gas, and warm halo gas. The remaining missing baryons (≈1.4 × 1011 M ⊙, 40%–50% of the total baryon content) are likely to be hot and extend to the 400–500 kpc volume, if not beyond. The result is higher than predictions, but within the uncertainties.
Topological Similarity Index and Loss Function for Blood Vessel Segmentation

Blood vessel segmentation is one of the most studied topics in computer vision, due to its relevance in daily clinical practice. Despite the evolution the field has been facing, especially after the dawn of deep learning, important challenges are still not solved. One of them concerns the consistency of the topological properties of the vascular trees, given that the best performing methodologies do not directly penalize mistakes such as broken segments and end up producing predictions with disconnected trees. This is particularly relevant in graph-like structures, such as blood vessel trees, given that it puts at risk the characterization steps that follow the segmentation task. In this paper, we propose a similarity index which captures the topological consistency of the predicted segmentations having as reference the ground truth. We also design a novel loss function based on the morphological closing operator and show how it allows to learn deep neural network models which produce more topologically coherent masks. Our experiments target well known retinal benchmarks and a coronary angiogram database.
Reconciling revealed and stated measures for willingness to pay in recreation by building a probability model.

The consumers' willingness to pay plays an important role in economic theory and in setting policy. For a market, this function can often be estimated from observed behavior -- preferences are revealed. However, economists would like to measure consumers' willingness to pay for some goods where this can only be measured through stated valuation. Confirmed convergence of valuations based on stated preferences as compared to valuations based on revealed preferences is rare, and it is important to establish circumstances under which one can expect such convergence. By building a simple probabilistic model for the consumers' likelihood of travel, we provide an approach that should make comparing stated and revealed preferences easier in cases where the preference is tied to travel or some other behavior whose cost can be measured. We implemented this approach in a pilot study and found an estimate of willingness to pay for visiting an environmentally enhanced recreational site based on actual travel in good agreement with an estimate based on a survey using stated preferences. To use the probabilistic model we used population statistics to adjust for the relevant duration and thus compare stated and revealed responses.
Text Classification and Clustering with Annealing Soft Nearest Neighbor Loss

We define disentanglement as how far class-different data points from each other are, relative to the distances among class-similar data points. When maximizing disentanglement during representation learning, we obtain a transformed feature representation where the class memberships of the data points are preserved. If the class memberships of the data points are preserved, we would have a feature representation space in which a nearest neighbour classifier or a clustering algorithm would perform well. We take advantage of this method to learn better natural language representation, and employ it on text classification and text clustering tasks. Through disentanglement, we obtain text representations with better-defined clusters and improve text classification performance. Our approach had a test classification accuracy of as high as 90.11% and test clustering accuracy of 88% on the AG News dataset, outperforming our baseline models -- without any other training tricks or regularization.
Ride comfort assessment for automated vehicles utilizing a road surface model and Monte Carlo simulations

The growing number of automated vehicles (AVs) necessitates good ride comfort for passengers. This research investigates currently available ride comfort methods and evaluates their performance with a validated simulation framework. The methodology developed encompasses a high‐precision road surface model and uses Monte Carlo simulations to compile accurate and representative virtual chassis acceleration data. By utilizing a threshold method and standard ISO 2631 ride comfort guidelines, results are compared to classifications based on empirical International Roughness Index data. A case study conducted in Austria specifies that ISO 2631 comfort estimates are most similar to International Roughness Index classifications and that the thresholding procedure detects preventable situations and over‐ or underestimated ride comfort. Thus, this methodology can help to better understand requirements for AVs' comfort, as well as justifying the importance of developing a sophisticated performance metric.
Automating Test Case Identification in Java Open Source Projects on GitHub

Software testing is one of the very important Quality Assurance (QA) components. A lot of researchers deal with the testing process in terms of tester motivation and how tests should or should not be written. However, it is not known from the recommendations how the tests are written in real projects. In this paper, the following was investigated: (i) the denotation of the word"test"in different natural languages; (ii) whether the number of occurrences of the word"test"correlates with the number of test cases; and (iii) what testing frameworks are mostly used. The analysis was performed on 38 GitHub open source repositories thoroughly selected from the set of 4.3M GitHub projects. We analyzed 20,340 test cases in 803 classes manually and 170k classes using an automated approach. The results show that: (i) there exists a weak correlation (r = 0.655) between the number of occurrences of the word"test"and the number of test cases in a class; (ii) the proposed algorithm using static file analysis correctly detected 97% of test cases; (iii) 15% of the analyzed classes used main() function whose represent regular Java programs that test the production code without using any third-party framework. The identification of such tests is very complex due to implementation diversity. The results may be leveraged to more quickly identify and locate test cases in a repository, to understand practices in customized testing solutions, and to mine tests to improve program comprehension in the future.