Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasinormal modes of NUT-charged black branes in the AdS/CFT correspondence

We study the scalar, electromagnetic and gravitational perturbations of planar AdS4 black holes with NUT charge. In the context of the AdS/CFT correspondence, these solutions describe a thermal quantum field theory embedded in a Gödel-type Universe with closed time-like curves. For a given temperature and NUT charge, two different planar Taub–NUT solutions exist, but we show that only the one with a positive specific heat contributes to the Euclidean saddle point in the path integral. By using the Newman–Penrose formalism, we then derive the master equations satisfied by scalar, electromagnetic and gravitational perturbations in this background, and show that the corresponding equations are separable. Interestingly, the solutions pile up in the form of Landau levels, and hence are characterized by a single quantum number q. We determine the appropriate boundary conditions satisfied by the master variables and using these we compute the quasinormal modes of scalar and gravitational perturbations. On the other hand, electromagnetic perturbations depend on a free parameter whose determination is problematic. We find that all the scalar and gravitational QNM frequencies lie in the lower half of the complex plane, indicating that these Taub–NUT spacetimes are stable. We discuss the implications of these results in the light of the AdS/CFT correspondence.
CoMo: A Novel Comoving 3D Camera System

Motivated by the theoretical interest in reconstructing long 3D trajectories of individual birds in large flocks, we developed CoMo, a comoving camera system of two synchronized cameras coupled with rotational stages, which allows us to dynamically follow the motion of a target flock. With the rotation of the cameras, we overcome the limitations of standard static systems that restrict the duration of the collected data to the short interval of time in which targets are in the cameras common field of view, but, at the same time, we change, in time, the external parameters of the system, which have then to be calibrated frame by frame. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch, and roll in the system home configuration (rotational stage at an angle equal to 0°) and combining this static information with the time-dependent rotation due to the stages. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests that show a relative error of the order of 1%. The novelty of the work presented in this article is not only on the system itself but also on the approach that we use in the tests, which we show to be a very powerful tool in detecting and fixing calibration inaccuracies, and it, for this reason, may be relevant for a broad audience.
Summarising Historical Text in Modern Languages

We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language. This is a fundamentally important routine to historians and digital humanities researchers but has never been automated. We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese. Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms. We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task.
SGR 0755−2933: a new high-mass X-ray binary with the wrong name

The soft gamma-ray repeater candidate SGR 0755−2933 was discovered in 2016 by Swift/BAT, which detected a short (∼30 ms), powerful burst typical of magnetars. To understand the nature of the source, we present here an analysis of follow-up observations of the tentative soft-X-ray counterpart of the source obtained with Swift/XRT, NuSTAR, and Chandra. From our analysis we conclude that, based on the observed counterpart position and properties, SGR 0755−2933 is not a soft gamma-ray repeater but rather a new high-mass X-ray binary. We suggest it be referred to as 2SXPS J075542.5−293353. We therefore conclude that the available data do not allow us to confirm existence and identify the true soft-X-ray counterpart to the burst event. The presence of a soft counterpart is nevertheless essential to unambiguously associate the burst with a magnetar flare, and we conclude that the magnetar origin of the burst and a precise burst location remain uncertain and require further investigation.
xLumi: Payment Channel Protocol and Off-chain Payment in Blockchain Contract Systems

In this paper, we introduce Super Luminal (”xLumi”), a new payment channel protocol for blockchain systems. xLumi is a simple unidirectional payment channel that can be extended to a bidirectional payment channel or a complete network. xLumi guarantees the security of the payment channel’s funds by using a simple set of mathematical rules that can be easily implemented on any blockchain with the necessary infrastructure. We also give the detailed implementation methods of this idea using V Systems contract systems in this paper.
Transparency in Multi-Human Multi-Robot Interaction

—Transparency is a key factor in the performance of human-robot interaction. A transparent interface allows operators to be aware of the state of a robot and to assess the progress of the tasks at hand. When multi-robot systems are involved, transparency is a greater challenge, due to the larger number of variables affecting the behavior of the robots as a whole. Existing work studies transparency with single oper- ators and multiple robots. Studies on transparency that focus on multiple operators interacting with a multi-robot systems are limited. This paper ﬁlls this gap by presenting a novel human-swarm interface for multiple operators. Through this interface, we study which graphical elements are contributing to multi-operator transparency by comparing four “transparency modes”: (i) no transparency (no operator receives information from the robots), (ii) central transparency (the operators receive information only relevant to their personal task), (iii) peripheral transparency (the operators share information on each others’ tasks), and (iv) mixed transparency (both central and periph-eral). We report the results in terms of awareness, trust, and workload from a user study involving 18 participants engaged in a complex multi-robot task.
Online and Scalable Model Selection with Multi-Armed Bandits

Many online applications running on live traffic are powered by machine learning models, for which training, validation, and hyper-parameter tuning are conducted on historical data. However, it is common for models demonstrating strong performance in offline analysis to yield poorer performance when deployed online. This problem is a consequence of the difficulty of training on historical data in non-stationary environments. Moreover, the machine learning metrics used for model selection may not sufficiently correlate with real-world business metrics used to determine the success of the applications being tested. These problems are particularly prominent in the Real-Time Bidding (RTB) domain, in which ML models power bidding strategies, and a change in models will likely affect performance of the advertising campaigns. In this work, we present Automatic Model Selector (AMS), a system for scalable online selection of RTB bidding strategies based on real-world performance metrics. AMS employs Multi-Armed Bandits (MAB) to near-simultaneously run and evaluate multiple models against live traffic, allocating the most traffic to the best-performing models while decreasing traffic to those with poorer online performance, thereby minimizing the impact of inferior models on overall campaign performance. The reliance on offline data is avoided, instead making model selections on a case-by-case basis according to actionable business goals. AMS allows new models to be safely introduced into live campaigns as soon as they are developed, minimizing the risk to overall performance. In live-traffic tests on multiple ad campaigns, the AMS system proved highly effective at improving ad campaign performance.
Conditional Action and Imperfect Erasure of Qubits

We consider state changes in quantum theory due to “conditional action” and relate these to the discussion of entropy decrease due to interventions of “intelligent beings” and the principles of Szilard and Landauer/Bennett. The mathematical theory of conditional actions is a special case of the theory of “instruments”, which describes changes of state due to general measurements and will therefore be briefly outlined in the present paper. As a detailed example, we consider the imperfect erasure of a qubit that can also be viewed as a conditional action and will be realized by the coupling of a spin to another small spin system in its ground state.
Exploring Transitivity in Neural NLI Models through Veridicality

Despite the recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities for natural language understanding remains unclear. We explore this issue in the domain of natural language inference (NLI), focusing on the transitivity of inference relations, a fundamental property for systematically drawing inferences. A model capturing transitivity can compose basic inference patterns and draw new inferences. We introduce an analysis method using synthetic and naturalistic NLI datasets involving clause-embedding verbs to evaluate whether models can perform transitivity inferences composed of veridical inferences and arbitrary inference types. We find that current NLI models do not perform consistently well on transitivity inference tasks, suggesting that they lack the generalization capacity for drawing composite inferences from provided training examples. The data and code for our analysis are publicly available at https://github.com/verypluming/transitivity.
Deep Learning for Scene Classification: A Survey

Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision. The rise of large-scale datasets, which constitute the corresponding dense sampling of diverse real-world scenes, and the renaissance of deep learning techniques, which learn powerful feature representations directly from big raw data, have been bringing remarkable progress in the field of scene representation and classification. To help researchers master needed advances in this field, the goal of this paper is to provide a comprehensive survey of recent achievements in scene classification using deep learning. More than 200 major publications are included in this survey covering different aspects of scene classification, including challenges, benchmark datasets, taxonomy, and quantitative performance comparisons of the reviewed methods. In retrospect of what has been achieved so far, this paper is also concluded with a list of promising research opportunities.
DeepDT: Learning Geometry From Delaunay Triangulation for Surface Reconstruction

In this paper, a novel learning-based network, named DeepDT, is proposed to reconstruct the surface from Delaunay triangulation of point cloud. DeepDT learns to predict inside/outside labels of Delaunay tetrahedrons directly from a point cloud and corresponding Delaunay triangulation. The local geometry features are first extracted from the input point cloud and aggregated into a graph deriving from the Delaunay triangulation. Then a graph filtering is applied on the aggregated features in order to add structural regularization to the label prediction of tetrahedrons. Due to the complicated spatial relations between tetrahedrons and the triangles, it is impossible to directly generate ground truth labels of tetrahedrons from ground truth surface. Therefore, we propose a multi-label supervision strategy which votes for the label of a tetrahedron with labels of sampling locations inside it. The proposed DeepDT can maintain abundant geometry details without generating overly complex surfaces, especially for inner surfaces of open scenes. Meanwhile, the generalization ability and time consumption of the proposed method is acceptable and competitive compared with the state-of-the-art methods. Experiments demonstrate the superior performance of the proposed DeepDT.
Joint Forecasting of Features and Feature Motion for Dense Semantic Future Prediction

We present a novel dense semantic forecasting approach which is applicable to a variety of architectures and tasks. The approach consists of two modules. Feature-to-motion (F2M) module forecasts a dense deformation ﬁeld which warps past features into their future positions. Feature-to-feature (F2F) module regresses the future features directly and is therefore able to account for emergent scenery. The compound F2MF approach decouples effects of motion from the effects of novelty in a task-agnostic manner. We aim to apply F2MF forecasting to the most subsampled and the most abstract representation of a desired single-frame model. Our implementations take advantage of deformable convo-lutions and pairwise correlation coefﬁcients across neighbouring time instants. We perform experiments on three dense prediction tasks: semantic segmentation, instance-level segmentation, and panoptic segmentation. The results reveal state-of-the-art forecasting accuracy across all three modalities on the Cityscapes dataset.
Propagation and reconstruction of re-entry uncertainties using continuity equation and simplicial interpolation

This work proposes a continuum-based approach for the propagation of uncertainties in the initial conditions and parameters for the analysis and prediction of spacecraft re-entries. Using the continuity equation together with the re-entry dynamics, the joint probability distribution of the uncertainties is propagated in time for specific sampled points. At each time instant, the joint probability distribution function is then reconstructed from the scattered data using a gradient-enhanced linear interpolation based on a simplicial representation of the state space. Uncertainties in the initial conditions at re-entry and in the ballistic coefficient for three representative test cases are considered: a three-state and a six-state steep Earth re-entry and a six-state unguided lifting entry at Mars. The paper shows the comparison of the proposed method with Monte Carlo based techniques in terms of quality of the obtained marginal distributions and runtime as a function of the number of samples used.
Optimal Disclosure of Information to Privately Informed Agents

We study information design with multiple privately informed agents who interact in a game. Each agent's utility is linear in a real‐valued state. We show that there always exists an optimal mechanism that is laminar partitional and bound its “complexity. ” For each type profile, such a mechanism partitions the state space and recommends the same action profile within a partition element. Furthermore, the convex hulls of any two partition elements are such that either one contains the other or they have an empty intersection. We highlight the value of screening: the ratio of the optimal and the best payoff without screening can be equal to the number of types. Along the way, we shed light on the solutions to optimization problems over distributions subject to a mean‐preserving contraction constraint and additional side‐constraints, which might be of independent interest.
DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization

Indoor localization has been a hot area of research over the past two decades. Since its advent, it has been steadily utilizing the emerging technologies to improve accuracy, and machine learning has been at the heart of that. Machine learning has been increasingly used in fingerprint-based indoor localization to replace or emulate the radio map that is used to predict locations given a location signature. The prediction quality of a machine learning model primarily depends on how well the model was trained, which relies on the amount and quality of data used to train it. Data augmentation has been used to improve quality of the trained models by synthetically producing more training data, and several approaches were used in the literature that tackles the problem of lack of training data from different angles. In this paper, we propose DataLoc+, a data augmentation technique for room-level indoor localization that combines different approaches in a simple algorithm. We evaluate the technique by comparing it to the typical direct snapshot approach using data collected from a field experiment conducted in a hospital. Our evaluation shows that the model trained using the proposed technique achieves higher accuracy. We also show that the technique adapts to larger problems using a limited dataset while maintaining high accuracy.
The wide upper main sequence and main-sequence turnoff of the ∼ 800 Myr old star cluster NGC 1831★

We present the analysis of the colour-magnitude diagram (CMD) morphology of the ∼ 800 Myr old star cluster NGC 1831 in the Large Magellanic Cloud, exploiting deep, high-resolution photometry obtained using the Wide Field Camera 3 onboard the Hubble Space Telescope. We perform a simultaneous analysis of the wide upper main sequence and main sequence turn-off observed in the cluster, to verify whether these features are due to an extended star formation or a range of stellar rotation rates, or a combination of these two effects. Comparing the observed CMD with Monte Carlo simulations of synthetic stellar populations, we derive that the morphology of NGC 1831 can be fully explained in the context of the rotation velocity scenario, under the assumption of a bimodal distribution for the rotating stars, with ∼ 40% of stars being slow-rotators (Ω/Ωcrit < 0.5) and the remaining ∼ 60% being fast rotators (Ω/Ωcrit > 0.9). We derive the dynamical properties of the cluster, calculating the present cluster mass and escape velocity, and predicting their past evolution starting at an age of 10 Myr. We find that NGC 1831 has an escape velocity E4B2 = 18.4 km s −1 at an age of 10 Myr, above the previously suggested threshold of 15 km s−1 below which the cluster cannot retain the material needed to create second-generation stars. These results, combined with those obtained from the CMD morphology analysis, indicate that for the clusters whose morphology cannot be easily explained only in the context of the rotation velocity scenario, the threshold limit should be at least ∼ 20 km s−1.
Application Layer Modeling in Vehicle Networks: Cooperative Maneuver Use Case

In recent years, network function virtualization and a software defined focus has allowed networks to become flexible and extensible in ways not possible previously. Although network modeling tools such as NS-2, NS-3, and OMNet++ have been extended with modules and code to support the absolute latest wireless protocols and medium access standards - there has been a growing gap in simulation of layers above medium access which recent 5G use cases are designed to support. In this thesis, I extend research into the topic of application layer modeling in wireless networks, with focus on upcoming deployments of enhanced vehicle services. In particular, I begin in discussing the evolution of vehicle network standards and use cases, along with the most recent initiative `Project 5GCar`; funded by the European Commission. I go narrower, from the macro trend of vehicular network standards evolution, to compare the state of the art in simulation stacks, designed to support application models in vehicle networks. Within this comparison, simulation environments OVNIS, Veins, iTetris, and VSimRTI are compared for their capabilities. Finally, to measure the qualitative performance of application layer modeling in vehicle networks, I take the cooperative maneuver use case, presented under Project 5GCar; to design an autonomous merge algorithm - completing the steps required to program and model the application in vehicles using the VSimRTI simulation stack.
Scaling the Convex Barrier with Sparse Dual Algorithms

Tight and efficient neural network bounding is crucial to the scaling of neural network verification systems. Many efficient bounding algorithms have been presented recently, but they are often too loose to verify more challenging properties. This is due to the weakness of the employed relaxation, which is usually a linear program of size linear in the number of neurons. While a tighter linear relaxation for piecewise-linear activations exists, it comes at the cost of exponentially many constraints and currently lacks an efficient customized solver. We alleviate this deficiency by presenting two novel dual algorithms: one operates a subgradient method on a small active set of dual variables, the other exploits the sparsity of Frank-Wolfe type optimizers to incur only a linear memory cost. Both methods recover the strengths of the new relaxation: tightness and a linear separation oracle. At the same time, they share the benefits of previous dual approaches for weaker relaxations: massive parallelism, GPU implementation, low cost per iteration and valid bounds at any time. As a consequence, we can obtain better bounds than off-the-shelf solvers in only a fraction of their running time, attaining significant formal verification speed-ups.
INFLUENCE OF UPSTREAM VERSUS DOWNSTREAM HEAT LOSSON THE STRUCTURE AND STABILITY OF PLANAR PREMIXED BURNER-STABILIZED FLAMES

The asymptotic structure and diffusional-thermal instability of a planar premixed burner-stabilized flame suffering either upstream or downstream volumetric heat loss with a rate described by a linear dependence on temperature is studied using activation-energy asymptotics. A brief review of the results for a flame without volumetric heat loss is presented. The results are compared to those obtained recently for a flame suffering combined upstream/downstream volumetric heat loss. For the structure, the analysis predicts that the expressions for the burning rate and the specific heat loss rate to the burner are not identical, unlike that shown for the freely propagating flame with volumetric heat loss. This is in agreement with earlier investigations of a flame suffering the three separate heat loss modes studied using thin-flame theory. Also in agreement with these earlier studies, the analysis predicts that the downstream heat loss mode is larger in magnitude than the upstream heat loss mode. For the diffusional-thermal instability, the analysis demonstrates that for the linear stability model considered, the effects of upstream and downstream heat loss appear in additive form. For fixed mass flux and volumetric heat loss intensity, it is found that the downstream heat loss mode is more effective than the upstream heat loss mode in promoting flame instability. These results are similar to those of the freely propagating flame with volumetric heat loss. A flame diagram dependent on the thermochemical data and volumetric heat loss mode is produced, which illustrates the stable flame region and the predicted extinction limits for low to medium values of the mass flux. It is found that at high mass flux no extinguishment point may be identified.