Miracle-dz/UniFAR-FTUs huggingface dataset:
Mod2Dash: A Framework for Model-Driven Dashboards Generation

The construction of an interactive dashboard involves deciding on what information to present and how to display it and implementing those design decisions to create an operational dashboard. Traditionally, a dashboard's design is implied in the deployed dashboard rather captured explicitly as a digital artifact, preventing it from being backed up, version-controlled, and shared. Moreover, practitioners have to implement this implicit design manually by coding or configuring it on a dashboard platform. This paper proposes Mod2Dash, a software framework that enables practitioners to capture their dashboard designs as models and generate operational dashboards automatically from these models. The framework also provides a GUI-driven customization approach for practitioners to fine-tune the auto-generated dashboards and update their models. With these abilities, Mod2Dash enables practitioners to rapidly prototype and deploy dashboards for both operational and research purposes. We evaluated the framework's effectiveness in a case study on cyber security visualization for decision support. A proof-of-concept of Mod2Dash was employed to model and reconstruct 31 diverse real-world cyber security dashboards. A human-assisted comparison between the Mod2Dash-generated dashboards and the baseline dashboards shows a close matching, indicating the framework's effectiveness for real-world scenarios.
Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents

In this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: the OpenAI-ES evolutionary strategy and the Proximal Policy Optimization (PPO) reinforcement learning algorithm -- the most similar methods of the two families. We analyze how the methods differ with respect to: (i) general efficacy, (ii) ability to cope with sparse rewards, (iii) propensity/capacity to discover minimal solutions, (iv) dependency on reward shaping, and (v) ability to cope with variations of the environmental conditions. The analysis of the performance and of the behavioral strategies displayed by the agents trained with the two methods on benchmark problems enable us to demonstrate qualitative differences which were not identified in previous studies, to identify the relative weakness of the two methods, and to propose ways to ameliorate some of those weakness. We show that the characteristics of the reward function has a strong impact which vary qualitatively not only for the OpenAI-ES and the PPO but also for alternative reinforcement learning algorithms, thus demonstrating the importance of optimizing the characteristic of the reward function to the algorithm used.
A scalable deep learning approach for solving high-dimensional dynamic optimal transport

The dynamic formulation of optimal transport has attracted growing interests in scientific computing and machine learning, and its computation requires to solve a PDE-constrained optimization problem. The classical Eulerian discretization based approaches suffer from the curse of dimensionality, which arises from the approximation of high-dimensional velocity field. In this work, we propose a deep learning based method to solve the dynamic optimal transport in high dimensional space. Our method contains three main ingredients: a carefully designed representation of the velocity field, the discretization of the PDE constraint along the characteristics, and the computation of high dimensional integral by Monte Carlo method in each time step. Specifically, in the representation of the velocity field, we apply the classical nodal basis function in time and the deep neural networks in space domain with the H1-norm regularization. This technique promotes the regularity of the velocity field in both time and space such that the discretization along the characteristic remains to be stable during the training process. Extensive numerical examples have been conducted to test the proposed method. Compared to other solvers of optimal transport, our method could give more accurate results in high dimensional cases and has very good scalability with respect to dimension. Finally, we extend our method to more complicated cases such as crowd motion problem.
The initial conditions for young massive cluster formation in the Galactic Centre: convergence of large-scale gas flows

Young massive clusters (YMCs) are compact (≲1 pc), high-mass (>104 M⊙) stellar systems of significant scientific interest. Due to their rarity and rapid formation, we have very few examples of YMC progenitor gas clouds before star formation has begun. As a result, the initial conditions required for YMC formation are uncertain. We present high-resolution (0.13″, ∼1000 au) ALMA observations and Mopra single-dish data, showing that Galactic Centre dust ridge ‘Cloud d’ (G0.412+0.052, mass =7.6 × 104 M⊙, radius =3.2 pc) has the potential to become an Arches-like YMC (104 M⊙, r ∼ 1 pc), but is not yet forming stars. This would mean it is the youngest known pre-star forming massive cluster and therefore could be an ideal laboratory for studying the initial conditions of YMC formation. We find 96 sources in the dust continuum, with masses ≲3 M⊙ and radii of ∼103 au. The source masses and separations are more consistent with thermal rather than turbulent fragmentation. It is not possible to unambiguously determine the dynamical state of most of the sources, as the uncertainty on virial parameter estimates is large. We find evidence for large-scale (∼1 pc) converging gas flows, which could cause the cloud to grow rapidly, gaining 104 M⊙ within 105 yr. The highest density gas is found at the convergent point of the large-scale flows. We expect this cloud to form many high-mass stars, but find no high-mass starless cores. If the sources represent the initial conditions for star formation, the resulting IMF will be bottom-heavy.
A Soft-rigid Hybrid Actuator with Multi-direction Tunable Stiffness Property

— The ability to maintain compliance during interaction with the human or environments while avoiding the undesired destabilization could be extremely important for further application in practicality for soft actuators. In this paper, a soft-rigid hybrid actuator with multi-direction tunable stiffness property was proposed. The multi-direction tunable stiffness property, which means that the stiffness of multiple directions can be decoupled for modulation, was achieved in two orthogonal directions, the bending direction (B direction) and the direction perpendicular to bending (PB direction). In the B direction, the stiffness was modulated through the antagonistic effect of the tendon-air hybrid driven; In the PB direction, the jamming effect brought by a novel structure, the bone-like structure (BLS), reinforces the PB- direction stiffness. Meanwhile, in this paper, the corresponding fabrication method to ensure airtightness was designed, and the working principle for the two mechanisms of the actuator was evaluated. Finally, a series of experiments have been conducted to characterize the performance of the actuator and analyze the stiffness variation in two orthogonal directions. According to the tests, the maximum ﬁngertip force reached 7.83N. And the experiments showed that stiffness in two directions can be tuned respectively. The B-direction stiffness can be tuned 1.5-4 times with a maximum stiffness of 1.24 N/mm. The PB- direction stiffness was enhanced about 4 times compared with the actuator without the mechanism, and it can be tuned decoupling with a range of 1.5 times.
Bidirectional Optimisation for Load Shaping within Coupled Microgrids

We address the problem of load shaping within a network of coupled microgrids (MGs) in a bilevel optimisation framework. To this end, we consider the charging/discharging rates of residential energy storage devices within each MG on the lower level and the power exchange among neighbouring MGs on the upper level as optimisation variables. We improve a previously developed model such that the maximal amount of exchanged power does not depend on the power demand, thus, increasing the flexibility within the network, and adapt the corresponding bidirectional optimisation scheme accordingly. For efficiency, standard distributed optimisation routines are used for the optimisation on the lower level; the power exchange problem on the upper level is replaced by parallelisable small-scale quadratic programmings. We prove global convergence of the optimisation scheme and illustrate the potential of the approach in a numerical case study based on real-world data.
Sensitive detection of local magnetic field changes with atomic interferometry by using superconducting Meissner effects

Sensitive detection of magnetic ﬁeld is one of the open problem in metrology. Here, we propose an Mach-Zehnder atomic interferometry to sensitively detect the very weak local magnetic ﬁeld, which is expelled by the superconductor (as the “testing magnet”) due to the Meissner effect. The induced magnetic ﬁeld gradient near the superconductor provides a centripetal acceleration of the atomic motion in the interferometry and thus can be detected by using the atomic interferences. Given gravity acceleration of the atoms have been measured at the accuracy of 10 − 12 g, the measured sensitivity of the expelled local ﬁeld could reach 10 − 14 T.
Radial projection theorems in finite spaces

Motivated by recent results on radial projections and applications to the celebrated Falconer distance problem, we radial projections in the setting of ﬁnite ﬁelds. More precisely, we extend results due to Mattila and Orponen (2016), Orponen (2018), and Liu (2020) to ﬁnite spaces. In some cases, our results are stronger than the corresponding results in the continuous setting. In particular, we solve the ﬁnite ﬁeld analog of a conjecture due to Liu and Orponen on the exceptional set of radial projections of a set of dimension between d − 2 and d − 1.
Wasserstein t-SNE

Scientific datasets often have hierarchical structure: for example, in surveys, individual participants (samples) might be grouped at a higher level (units) such as their geographical region. In these settings, the interest is often in exploring the structure on the unit level rather than on the sample level. Units can be compared based on the distance between their means, however this ignores the within-unit distribution of samples. Here we develop an approach for exploratory analysis of hierarchical datasets using the Wasserstein distance metric that takes into account the shapes of within-unit distributions. We use t-SNE to construct 2D embeddings of the units, based on the matrix of pairwise Wasserstein distances between them. The distance matrix can be efficiently computed by approximating each unit with a Gaussian distribution, but we also provide a scalable method to compute exact Wasserstein distances. We use synthetic data to demonstrate the effectiveness of our Wasserstein t-SNE, and apply it to data from the 2017 German parliamentary election, considering polling stations as samples and voting districts as units. The resulting embedding uncovers meaningful structure in the data.
Network change point localisation under local differential privacy

Network data are ubiquitous in our daily life, containing rich but often sensitive information. In this paper, we expand the current static analysis of privatised networks to a dynamic framework by considering a sequence of networks with potential change points. We investigate the fundamental limits in consistently localising change points under both node and edge privacy constraints, demonstrating interesting phase transition in terms of the signal-to-noise ratio condition, accompanied by polynomial-time algorithms. The private signal-to-noise ratio conditions quantify the costs of the privacy for change point localisation problems and exhibit a different scaling in the sparsity parameter compared to the non-private counterparts. Our algorithms are shown to be optimal under the edge LDP constraint up to log factors. Under node LDP constraint, a gap exists between our upper bound and lower bound and we leave it as an interesting open problem.
GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction Following

Our goal is to enable a robot to learn how to sequence its actions to perform tasks specified as natural language instructions, given successful demonstrations from a human partner. The ability to plan high-level tasks can be factored as (i) inferring specific goal predicates that characterize the task implied by a language instruction for a given world state and (ii) synthesizing a feasible goal-reaching action-sequence with such predicates. For the former, we leverage a neural network prediction model, while utilizing a symbolic planner for the latter. We introduce a novel neuro-symbolic model, GoalNet, for contextual and task dependent inference of goal predicates from human demonstrations and linguistic task descriptions. GoalNet combines (i) learning, where dense representations are acquired for language instruction and the world state that enables generalization to novel settings and (ii) planning, where the cause-effect modeling by the symbolic planner eschews irrelevant predicates facilitating multi-stage decision making in large domains. GoalNet demonstrates a significant improvement (51%) in the task completion rate in comparison to a state-of-the-art rule-based approach on a benchmark data set displaying linguistic variations, particularly for multi-stage instructions.
Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems

Abstract Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily solvable instances is available, which is often not the case in practical applications. This work extends the learning framework and implementation of a model-based approach for Answer Set Programming to overcome these limitations and address challenging problems, such as the Partner Units Problem. In particular, we incorporate a new conflict analysis algorithm in the Inductive Logic Programming system ILASP, redefine the learning task, and suggest a new example generation method to scale up the approach. The experiments conducted for different kinds of Partner Units Problem instances demonstrate the applicability of our approach and the computational benefits due to the first-order constraints learned.
Assessing the Limits of the Distributional Hypothesis in Semantic Spaces: Trait-based Relational Knowledge and the Impact of Co-occurrences

The increase in performance in NLP due to the prevalence of distributional models and deep learning has brought with it a reciprocal decrease in interpretability. This has spurred a focus on what neural networks learn about natural language with less of a focus on how. Some work has focused on the data used to develop data-driven models, but typically this line of work aims to highlight issues with the data, e.g. highlighting and offsetting harmful biases. This work contributes to the relatively untrodden path of what is required in data for models to capture meaningful representations of natural language. This is entails evaluating how well English and Spanish semantic spaces capture a particular type of relational knowledge, namely the traits associated with concepts (e.g. bananas-yellow), and exploring the role of co-occurrences in this context.
q-Munchausen Reinforcement Learning

The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the current stochastic policy. Though significant improvement has been shown with the Boltzmann softmax policy, when the Tsallis sparsemax policy is considered, the augmentation leads to a flat learning curve for almost every problem considered. We show that it is due to the mismatch between the conventional logarithm and the non-logarithmic (generalized) nature of Tsallis entropy. Drawing inspiration from the Tsallis statistics literature, we propose to correct the mismatch of M-RL with the help of $q$-logarithm/exponential functions. The proposed formulation leads to implicit Tsallis KL regularization under the maximum Tsallis entropy framework. We show such formulation of M-RL again achieves superior performance on benchmark problems and sheds light on more general M-RL with various entropic indices $q$.
A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding

Global Positioning Systems (GPS) have played a crucial role in various navigation applications. Nevertheless, localizing the perfect destination within the last few meters remains an important but unresolved problem. Limited by the GPS positioning accuracy, navigation systems always show users a vicinity of a destination, but not its exact location. Street view images (SVI) in maps as an immersive media technology have served as an aid to provide the physical environment for human last-meters wayfinding. However, due to the large diversity of geographic context and acquisition conditions, the captured SVI always contains various distracting objects (e.g., pedestrians and vehicles), which will distract human visual attention from efficiently finding the destination in the last few meters. To address this problem, we highlight the importance of reducing visual distraction in image-based wayfinding by proposing a saliency-guided image inpainting framework. It aims at redirecting human visual attention from distracting objects to destination-related objects for more efficient and accurate wayfinding in the last meters. Specifically, a context-aware distracting object detection method driven by deep salient object detection has been designed to extract distracting objects from three semantic levels in SVI. Then we employ a large-mask inpainting method with fast Fourier convolutions to remove the detected distracting objects. Experimental results with both qualitative and quantitative analysis show that our saliency-guided inpainting method can not only achieve great perceptual quality in street view images but also redirect the human's visual attention to focus more on static location-related objects than distracting ones. The human-based evaluation also justified the effectiveness of our method in improving the efficiency of locating the target destination.
Gradient-based Counterfactual Explanations using Tractable Probabilistic Models

Counterfactual examples are an appealing class of post-hoc explanations for machine learning models. Given input x of class y 1 , its counterfactual is a contrastive example x (cid:48) of another class y 0 . Current approaches primarily solve this task by a complex optimization: deﬁne an objective function based on the loss of the counterfactual outcome y 0 with hard or soft constraints, then optimize this function as a black-box. This “deep learning” approach, however, is rather slow, sometimes tricky, and may result in unrealistic counterfactual examples. In this work, we propose a novel approach to deal with these problems using only two gradient com-putations based on tractable probabilistic models. First, we compute an unconstrained counterfactual u of x to induce the counterfactual outcome y 0 . Then, we adapt u to higher density regions, resulting in x (cid:48) . Empirical evidence demonstrates the dominant advantages of our approach.
Everywhere regularity results for a polyconvex functional in finite elasticity

Here we develop a regularity theory for a polyconvex functional in $2\times2-$dimensional compressible finite elasticity. In particular, we consider energy minimizers/stationary points of the functional $I(u)=\int\limits_{\Omega}{\frac{1}{2}|\nabla u|^2+\rho(\det\nabla u)\;dx},$ where $\Omega\subset\mathbb{R}^2$ is open and bounded, $u\in W^{1,2}(\Omega,\mathbb{R}^2)$ and $\rho:\mathbb{R}\rightarrow\mathbb{R}_0^+$ smooth and convex with $\rho(s)=0$ for all $s\le0$ and $\rho$ becomes affine when $s$ exceeds some value $s_0>0. $ Additionally, we may impose boundary conditions. The first result we show is that every stationary point needs to be locally H\"older-continuous. Secondly, we prove that if $\|\rho'\|_{L^\infty(\mathbb{R})}<1$ s.t. the integrand is still uniformly convex, then all stationary points have to be in $W_{loc}^{2,2}. $ Next, a higher-order regularity result is shown. Indeed, we show that all stationary points that are additionally of class $W_{loc}^{2,2}$ and whose Jacobian is suitably H\"older-continuous are of class $C_{loc}^{\infty}. $ As a consequence, these results show that in the case when $\|\rho'\|_{L^\infty(\mathbb{R})}<1$ all stationary points have to be smooth.
VRCockpit: Mitigating Simulator Sickness in VR Games Using Multiple Egocentric 2D View Frames

Virtual reality head-mounted displays (VR HMDs) have become a popular platform for gaming. However, simulator sickness (SS) is still an impediment to VR’s wider adoption, particularly in gaming. It can induce strong discomfort and impair players’ immersion, performance, and enjoyment. Researchers have explored techniques to mitigate SS. While these techniques have been shown to help lessen SS, they may not be applicable to games because they cannot be easily integrated into various types of games without impacting gameplay, immersion, and performance. In this research, we introduce a new SS mitigation technique, VRCockpit. VRCockpit is a visual technique that surrounds the player with four 2D views, one for each cardinal direction, that show 2D copies of the areas of the 3D environment around the player. To study its effectiveness, we conducted two different experiments, one with a car racing game, followed by a first-person shooter game. Our results show that VRCockpit has the potential to mitigate SS and still allows players to have the same level of immersion and gameplay performance.
Software Updates Strategies: A Quantitative Evaluation Against Advanced Persistent Threats

Software updates reduce the opportunity for exploitation. However, since updates can also introduce breaking changes, enterprises face the problem of balancing the need to secure software with updates with the need to support operations. We propose a methodology to quantitatively investigate the effectiveness of software updates strategies against attacks of Advanced Persistent Threats (APTs). We consider strategies where the vendor updates are the only limiting factors to cases in which enterprises delay updates from 1 to 7 months based on SANS data. Our manually curated dataset of APT attacks covers 86 APTs and 350 campaigns from 2008 to 2020. It includes information about attack vectors, exploited vulnerabilities (e.g., 0-days versus public vulnerabilities), and affected software and versions. Contrary to common belief, most APT campaigns employed publicly known vulnerabilities. If an enterprise could theoretically update as soon as an update is released, it would face lower odds of being compromised than those waiting one (4.9x) or three (9.1x) months. However, if attacked, it could still be compromised from 14% to 33% of the times. As in practice enterprises must do regression testing before applying an update, our major finding is that one could perform 12% of all possible updates restricting oneself only to versions fixing publicly known vulnerabilities without significant changes to the odds of being compromised compared to a company that updates for all versions.