Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction-Aware Trajectory Planning for Autonomous Vehicles with Analytic Integration of Neural Networks into Model Predictive Control

Autonomous vehicles (AVs) must share the driving space with other drivers and often employ conservative motion planning strategies to ensure safety. These conservative strategies can negatively impact AV's performance and significantly slow traffic throughput. Therefore, to avoid conservatism, we design an interaction-aware motion planner for the ego vehicle (AV) that interacts with surrounding vehicles to perform complex maneuvers in a locally optimal manner. Our planner uses a neural network-based interactive trajectory predictor and analytically integrates it with model predictive control (MPC). We solve the MPC optimization using the alternating direction method of multipliers (ADMM) and prove the algorithm's convergence. We provide an empirical study and compare our method with a baseline heuristic method.
Do the Defect Prediction Models Really Work?

—“ You may develop a potential prediction model, but how can I trust your model that it will beneﬁt my software? ”. Using a software defect prediction (SDP) model as a tool, we address this fundamental problem in machine learning research. This is a preliminary work targeted at providing an analysis of the developed binary SDP model in real-time working environments.
Rock Guitar Tablature Generation via Natural Language Processing

Deep learning has recently empowered and democratized generative modeling of images and text, with additional concurrent works exploring the possibility of generating more complex forms of data, such as audio. However, the high dimensionality, long-range dependencies, and lack of standardized datasets currently makes generative modeling of audio and music very challenging. We propose to model music as a series of discrete notes upon which we can use autoregressive natural language processing techniques for successful generative modeling. While previous works used similar pipelines on data such as sheet music and MIDI, we aim to extend such approaches to the under-studied medium of guitar tablature. Specifically, we develop the first work to our knowledge that models one specific genre as guitar tablature: heavy rock. Unlike other works in guitar tablature generation, we have a freely available public demo at https://huggingface.co/spaces/josuelmet/Metal_Music_Interpolator
An Approach to Stochastic Dynamic Games with Asymmetric Information and Hidden Actions

We consider in discrete time, a general class of sequential stochastic dynamic games with asymmetric information with the following features. The underlying system has Markovian dynamics controlled by the agents' joint actions. Each agent's instantaneous utility depends on the current system state and the agents' joint actions. At each time instant each agent makes a private noisy observation of the current system state and the agents' actions in the previous time instant. In addition, at each time instant all agents have a common noisy observation of the current system state and their actions in the previous time instant. Each agent's actions are part of his private information. The objective is to determine Bayesian Nash Equilibrium (BNE) strategy profiles that are based on a compressed version of the agents' information and can be sequentially computed; such BNE strategy profiles may not always exist. We present an approach/methodology that achieves the above-stated objective, along with an instance of a game where BNE strategy profiles with the above-mentioned characteristics exist. We show that the methodology also works for the case where the agents have no common observations.
UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems

We present an extensible user simulation toolkit to facilitate automatic evaluation of conversational recommender systems. It builds on an established agenda-based approach and extends it with several novel elements, including user satisfaction prediction, persona and context modeling, and conditional natural language generation. We showcase the toolkit with a pre-existing movie recommender system and demonstrate its ability to simulate dialogues that mimic real conversations, while requiring only a handful of manually annotated dialogues as training data.
Universality in low-dimensional BCS theory

It is a remarkable property of BCS theory that the ratio of the energy gap at zero temperature $\Xi$ and the critical temperature $T_c$ is (approximately) given by a universal constant, independent of the microscopic details of the fermionic interaction. This universality has rigorously been proven quite recently in three spatial dimensions and three different limiting regimes: weak coupling, low density, and high density. The goal of this short note is to extend the universal behavior to lower dimensions $d=1,2$ and give an exemplary proof in the weak coupling limit.
Randomization Test for the Specification of Interference Structure

This study considers testing the specification of spillover effects in causal inference. We focus on experimental settings in which the treatment assignment mechanism is known to researchers. We develop a new randomization test utilizing a hierarchical relationship between different exposures. Compared with existing approaches, our approach is essentially applicable to any null exposure specifications and produces powerful test statistics without a priori knowledge of the true interference structure. As empirical illustrations, we revisit two existing social network experiments: one on farmers' insurance adoption and the other on anti-conflict education programs.
Threat Models over Space and Time: A Case Study of E2EE Messaging Applications

Threat modelling is foundational to secure systems engineering and should be done in consideration of the context within which systems operate. On the other hand, the continuous evolution of both the technical sophistication of threats and the system attack surface is an inescapable reality. In this work, we explore the extent to which real-world systems engineering reflects the changing threat context. To this end we examine the desktop clients of six widely used end-to-end-encrypted mobile messaging applications to understand the extent to which they adjusted their threat model over space (when enabling clients on new platforms, such as desktop clients) and time (as new threats emerged). We experimented with short-lived adversarial access against these desktop clients and analyzed the results with respect to two popular threat elicitation frameworks, STRIDE and LINDDUN. The results demonstrate that system designers need to both recognise the threats in the evolving context within which systems operate and, more importantly, to mitigate them by rescoping trust boundaries in a manner that those within the administrative boundary cannot violate security and privacy properties. Such a nuanced understanding of trust boundary scopes and their relationship with administrative boundaries allows for better administration of shared components, including securing them with safe defaults.
Sem@K: Is my knowledge graph embedding model semantic-aware?

Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities. However, the literature claims that the KGEM evaluation procedure would benefit from adding supplementary dimensions to assess. That is why, in this paper, we extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w.r. t. domain and range constraints. In particular, we consider a broad range of KGs and take their respective characteristics into account to propose different versions of Sem@K. We also perform an extensive study to qualify the abilities of KGEMs as measured by our metric. Our experiments show that Sem@K provides a new perspective on KGEM quality. Its joint analysis with rank-based metrics offers different conclusions on the predictive power of models. Regarding Sem@K, some KGEMs are inherently better than others, but this semantic superiority is not indicative of their performance w.r. t. rank-based metrics. In this work, we generalize conclusions about the relative performance of KGEMs w.r. t. rank-based and semantic-oriented metrics at the level of families of models. The joint analysis of the aforementioned metrics gives more insight into the peculiarities of each model. This work paves the way for a more comprehensive evaluation of KGEM adequacy for specific downstream tasks.
A Parametric Level Set Method with Convolutional Encoder-Decoder Network for Shape Optimization with Fluid Flow

In this article, we present a new data-driven shape optimization approach for implicit hydrofoil morphing via a polynomial perturbation of parametric level set representation. Without introducing any change in topology, the hydrofoil morphing is achieved by six shape design variables associated with the amplitude and shape of the perturbed displacements. The proposed approach has three to four times lower design variables than shape optimization via free-form deformation techniques and almost two orders lower design variables compared to topology optimization via traditional parametric level sets. Using the fixed Cartesian level set mesh, we also integrate deep convolutional encoder-decoder networks as a surrogate of high-fidelity Reynolds-averaged Navier-Stokes (RANS) simulations for learning the flow field around hydrofoil shapes. We show that an efficient shape representation via parametric level sets can enable online convolutional encoder-decoder application for the shape optimization of hydrofoils. The generalized flow field prediction of the convolutional encoder-decoder is demonstrated by a mean and minimum structural similarity index measure of 0.985 and 0.95, respectively, for predicted solutions compared to RANS predictions for out-of-training shapes. The convolutional encoder-decoder predictions are performed nearly five orders of magnitude faster compared to RANS. This enables a computationally tractable surrogate-based drag minimization of fifty different hydrofoils for two different design lift coefficients. Furthermore, the best local minimum obtained via the surrogate-based optimization lie in the neighbourhood of the RANS-predicted counterparts for both the design lift cases. The present findings show promise for the future shape optimization via parametric level sets with convolutional encoder-decoder over a broader spectrum of flow conditions and shapes.
Classification of Cross-cultural News Events

We present a methodology to support the analysis of culture from text such as news events and demonstrate its usefulness on categorising news events from different categories (society, business, health, recreation, science, shopping, sports, arts, computers, games and home) across different geographical locations (different places in 117 countries). We group countries based on the culture that they follow and then filter the news events based on their content category. The news events are automatically labelled with the help of Hofstede’s cultural dimensions. We present combinations of events across different categories and check the performances of different classification methods. We also presents experimental comparison of different number of features in order to find a suitable set to represent the culture.
The Fornax3D project: Environmental effects on the assembly of dynamically cold disks in Fornax cluster galaxies

We apply a population-orbit superposition method to 16 galaxies in the Fornax cluster observed with MUSE/VLT in the context of the Fornax3D project. By fitting the luminosity distribution, stellar kinematics, and age and metallicity maps simultaneously, we obtained the internal stellar orbit distribution, as well as the age and metallicity distribution of stars on different orbits for each galaxy. Based on the model, we decompose each galaxy into a dynamically cold disk (orbital circularity $\lambda_z\ge0.8$) and a dynamically hot non-disk component (orbital circularity $\lambda_z<0.8$), and obtain the surface-brightness, age, and metallicity radial profiles of each component. The galaxy infall time into the cluster is strongly correlated with galaxy cold-disk age with older cold disks in ancient infallers. We quantify the infall time $t_{\rm infall}$ of each galaxy with its cold-disk age using a correlation calibrated with TNG50 cosmological simulations. For galaxies in the Fornax cluster, we found that the luminosity fraction of cold disk in galaxies with $t_{\rm infall}>8$ Gyr are a factor of $\sim 4$ lower than in more recent infallers while controlling for total stellar mass. Nine of the 16 galaxies have spatially extended cold disks, and most of them show positive or zero age gradients; stars in the inner disk are $\sim 2-5$ Gyr younger than that in the outer disk, in contrast to the expectation of inside-out growth. Our results indicate that the assembly of cold disks in galaxies is strongly affected by their infall into clusters, by either removal of gas in outer regions or even tidally stripping or heating part of the pre-existing disks. Star formation in outer disks can stop quickly after the galaxy falls into the cluster, while star formation in the inner disks can last for a few Gyrs more, building the positive age gradient measured in cold disks.
Digital Pre-Distorted One-Step Phase Retrieval Algorithm for Real-Time Hologram Generation for Holographic Displays

In a computer-generated holographic projection system, the image is reconstructed via the diffraction of light from a spatial light modulator. In this process, several factors could contribute to non-linearities between the reconstruction and the target image. This paper evaluates the non-linearity of the overall holographic projection system experimentally, using binary phase holograms computed using the one-step phase retrieval (OSPR) algorithm, and then applies a digital pre-distortion (DPD) method to correct for the non-linearity. Both a notable increase in reconstruction quality and a significant reduction in mean squared error were observed, proving the effectiveness of the proposed DPD-OSPR algorithm.
Pylon: Semantic Table Union Search in Data Lakes

The large size and fast growth of data repositories, such as data lakes, has spurred the need for data discovery to help analysts find related data. The problem has become challenging as (i) a user typically does not know what datasets exist in an enormous data repository; and (ii) there is usually a lack of a unified data model to capture the interrelationships between heterogeneous datasets from disparate sources. In this work, we address one important class of discovery needs: finding union-able tables. The task is to find tables in a data lake that can be unioned with a given query table. The challenge is to recognize union-able columns even if they are represented differently. In this paper, we propose a data-driven learning approach: specifically, an unsupervised representation learning and embedding retrieval task. Our key idea is to exploit self-supervised contrastive learning to learn an embedding model that takes into account the indexing/search data structure and produces embeddings close by for columns with semantically similar values while pushing apart columns with semantically dissimilar values. We then find union-able tables based on similarities between their constituent columns in embedding space. On a real-world data lake, we demonstrate that our best-performing model achieves significant improvements in precision ($16\% \uparrow$), recall ($17\% \uparrow $), and query response time (7x faster) compared to the state-of-the-art.
New universal partizan rulesets and a new universal dicotic partisan ruleset

Universal partizan ruleset is a ruleset in which every game value of partizan games can be appear as a position. So far, generalized konane and turning tiles have been proved to be universal partizan rulesets. In this paper, we introduce two rulesets go on lattice and beyond the door and prove that they are universal partizan rulesets by using game tree preserving reduction. Further, we consider dicotic version of beyond the door, and we prove the ruleset is a universal partisan dicotic ruleset.
Toward Accurate Measurement of Property-dependent Galaxy Clustering. II. Tests of the Smoothed Density-corrected V
 max Method

We present a smoothed density-corrected V max technique for building a random catalog for property-dependent galaxy clustering estimation. This approach is essentially based on the density-corrected V max method of Cole, with three improvements to the original method. To validate the improved method, we generate two sets of flux-limited samples from two independent mock catalogs with different k + e corrections. By comparing the two-point correlation functions, our results demonstrate that the random catalog created by the smoothed density-corrected V max approach provides a more accurate and precise measurement for both sets of mock samples than the commonly used V max and redshift shuffled methods. For the flux-limited samples and color-dependent subsamples, the accuracy of the projected correlation function is well constrained within 1% on the scale of 0.07–30 h −1 Mpc. The accuracy of the redshift-space correlation function is less than 2% as well. Currently, it is the only approach that holds promise for achieving the goal of high-accuracy clustering measures for next-generation surveys.
A NOEMA Molecular Line Scan of the Hubble Deep Field North: Improved Constraints on the CO Luminosity Functions and Cosmic Density of Molecular Gas

We present measurements of the CO luminosity functions (LFs) and the evolution of the cosmic molecular gas density out to z ∼ 6 based on an 8.5 arcmin2 spectral scan survey at 3 mm of the iconic Hubble Deep Field North (HDF-N) observed with the NOrthern Extended Millimeter Array (NOEMA). We use matched filtering to search for line emission from galaxies and determine their redshift probability distributions exploiting the extensive multiwavelength data for the HDF-N. We identify the seven highest-fidelity sources as CO emitters at 1 < z < 6, including the well-known submillimeter galaxy HDF 850.1 at z = 5.18. Four high-fidelity 3 mm continuum sources are found to be radio galaxies at z ≤ 1, plus HDF 850.1. We constrain the CO LFs in the HDF-N out to z ∼ 6, including a first measurement of the CO(5–4) LF at 〈z〉 = 5.0. The relatively large area and depth of the NOEMA HDF-N survey extends the existing LFs at 1 < z < 4 above the knee, yielding a somewhat lower density by 0.15–0.4 dex at the overlap region for the CO(2–1) and CO(3–2) transitions, attributed to cosmic variance. We perform a joint analysis of the CO LFs in the HDF-N and Hubble Ultra Deep Field from ASPECS, finding that they can be well described by a single Schechter function. The evolution of the cosmic molecular gas density from a joint analysis is in good agreement with earlier determinations. This implies that the impact of cosmic field-to-field variance on the measurements is consistent with previous estimates, adding to the challenges for simulations that model galaxies from first principles.
Evaluations of euler-euler simulations of bubble columns based on numerical tracer experiments

Abstract This paper considers the question of how to evaluate Euler–Euler simulations of macro-mixing in two-phase bubble columns. Here, the idea is to perform tracer experiments (jump responses) both experimentally and also numerically. In both cases the resulting data is then analysed on the basis of the convection–dispersion model. Given that the integral quantities gas hold-up, axial dispersion coefficient (i.e., Bodenstein number) and mean residence time show sensitivity with respect to the physical phenomena, the applied turbulence models (i.e., k – ɛ and SST) and the boundary conditions, respectively to their numerical consequences, this approach provides a means to evaluate the quality of sophisticated mathematical models and their numerical realization.
Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO With Hardware Impairments

We consider the problem of channel estimation in a multiple-input-multiple-output (MIMO) full-duplex (FD) wireless communication system assisted by a reconfigurable intelligent surface (RIS) with hardware impairments (HI) occurring at the transceivers and RIS elements. We propose an unbiased channel estimator that requires knowledge of only the first and second order statistics of the HI, for which we derive closed form expressions. The proposed estimator reduces to the maximum likelihood estimator (MLE) in the case of ideal hardware. We also describe FD and HD orthogonal pilot schemes that minimize the mean square error of the MLE in the case of ideal hardware. We verify the performance of the estimator under varying conditions of transceiver and RIS HI via numerical simulations.
The evolution of vortices in vertical shear. III: Baroclinic vortices

The evolution of baroclinic tropical‐eye lone‐like vortices in environmental vertical shear on an f‐plane is investigated. Idealized numerical calculations are performed using a primitive‐equation model. The vertical structure of the initial vortex is varied by changing the strength of the upper‐level tangential wind. The baroclinic vortices develop a vertical tilt and a cyclonic rotation of the mid‐level centre about the surface centre occurs. In most of the calculations the upper‐level part of the vortex is advected away from the surface centre. The motion of the upper‐level part of the vortex can be attributed to advection by the flow associated with large‐scale asymmetries in the potential vorticity and to the vertically penetrating flow associated with the potential vorticity of the lower portion of the tilted vortex. As in the case of initially barotropic vortices the cyclonic rotation is found to depend on the parameters in the Rossby penetration depth. The height at which the environmental flow is equal to the Speed of vortex motion is found to be higher for vortices with stronger flow at upper levels. This is consistent with observations of tropical cyclones.
Weighted Domination and Colouring in Random Graphs

In the ﬁrst part of this paper, we consider weighted domination in the case where the vertices of the complete graph on n vertices are equipped with independent and identically distributed (i.i.d.) weights. We use the probabilistic iteration to determine suﬃcient conditions for maximizing the weighted domination probability. In the second part, we study a weighted generalization of the chromatic number and estimate the minimum number of colours needed to satisfy the constraints when the weights themselves are random. We show that the “extra” cost incurred for weighted colouring is small if the weights have suﬃciently large moments. We also consider inhomogenous random graphs where the edge probabilities are not necessarily all same and obtain bounds for the chromatic number in terms of its averaged edge probabilities.