Miracle-dz/UniFAR-FTUs huggingface dataset:
A Knowledge Enhanced Learning and Semantic Composition Model for Multi-Claim Fact Checking

To inhibit the spread of rumorous information and its severe consequences, traditional fact checking aims at retrieving relevant evidence to verify the veracity of a given claim. Fact checking methods typically use knowledge graphs (KGs) as external repositories and develop reasoning mechanism to retrieve evidence for verifying the triple claim. However, existing methods only focus on verifying a single claim. As real-world rumorous information is more complex and a textual statement is often composed of multiple clauses (i.e. represented as multiple claims instead of a single one), multiclaim fact checking is not only necessary but more important for practical applications. Although previous methods for verifying a single triple can be applied repeatedly to verify multiple triples one by one, they ignore the contextual information implied in a multi-claim statement and could not learn the rich semantic information in the statement as a whole. In this paper, we propose an end-to-end knowledge enhanced learning and verification method for multi-claim fact checking. Our method consists of two modules, KG-based learning enhancement and multi-claim semantic composition. To fully utilize the contextual information, the KG-based learning enhancement module learns the dynamic context-specific representations via selectively aggregating relevant attributes of entities. To capture the compositional semantics of multiple triples, the multi-claim semantic composition module constructs the graph structure to model claim-level interactions, and integrates global and salient local semantics with multi-head attention. Experimental results on a real-world dataset and two benchmark datasets show the effectiveness of our method for multi-claim fact checking over KG.
Network Analysis of SFU Course Registrations

We investigate the dynamics of disease infection via shared classes at Simon Fraser University, a medium-sized school in Western Canada. Speciﬁcally, we use simulation to examine the impact of keeping classes above a certain size online, while those below that size are allowed to meet in person. We use simple models for infection and recovery, as well as multiple regimes for infectiousness and class size threshold. Graph theoretic properties of the student-class enrollment network are also computed, and one such statistic is used to model an important output of our simulations.
Semi-Supervised Joint Estimation of Word and Document Readability

Readability or difficulty estimation of words and documents has been investigated independently in the literature, often assuming the existence of extensive annotated resources for the other. Motivated by our analysis showing that there is a recursive relationship between word and document difficulty, we propose to jointly estimate word and document difficulty through a graph convolutional network (GCN) in a semi-supervised fashion. Our experimental results reveal that the GCN-based method can achieve higher accuracy than strong baselines, and stays robust even with a smaller amount of labeled data.
HodgeNet

Constrained by the limitations of learning toolkits engineered for other applications, such as those in image processing, many mesh-based learning algorithms employ data flows that would be atypical from the perspective of conventional geometry processing. As an alternative, we present a technique for learning from meshes built from standard geometry processing modules and operations. We show that low-order eigenvalue/eigenvector computation from operators parameterized using discrete exterior calculus is amenable to efficient approximate backpropagation, yielding spectral per-element or per-mesh features with similar formulas to classical descriptors like the heat/wave kernel signatures. Our model uses few parameters, generalizes to high-resolution meshes, and exhibits performance and time complexity on par with past work.
Emergence as the conversion of information: a unifying theory

Is reduction always a good scientific strategy? The existence of the special sciences above physics suggests not. Previous research has shown that dimensionality reduction (macroscales) can increase the dependency between elements of a system (a phenomenon called ‘causal emergence’). Here, we provide an umbrella mathematical framework for emergence based on information conversion. We show evidence that coarse-graining can convert information from one ‘type’ to another. We demonstrate this using the well-understood mutual information measure applied to Boolean networks. Using partial information decomposition, the mutual information can be decomposed into redundant, unique and synergistic information atoms. Then by introducing a novel measure of the synergy bias of a given decomposition, we are able to show that the synergy component of a Boolean network’s mutual information can increase at macroscales. This can occur even when there is no difference in the total mutual information between a macroscale and its underlying microscale, proving information conversion. We relate this broad framework to previous work, compare it to other theories, and argue it complexifies any notion of universal reduction in the sciences, since such reduction would likely lead to a loss of synergistic information in scientific models. This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.
Unsupervised Deep Manifold Attributed Graph Embedding

Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing methods concentrate on learning latent representation via reconstruction tasks, but cannot directly optimize representation and are prone to oversmoothing, thus limiting the applications on downstream tasks. To alleviate these issues, we propose a novel graph embedding framework named Deep Manifold Attributed Graph Embedding (DMAGE). A node-to-node geodesic similarity is proposed to compute the inter-node similarity between the data space and the latent space and then use Bergman divergence as loss function to minimize the difference between them. We then design a new network structure with fewer aggregation to alleviate the oversmoothing problem and incorporate graph structure augmentation to improve the representation's stability. Our proposed DMAGE surpasses state-of-the-art methods by a significant margin on three downstream tasks: unsupervised visualization, node clustering, and link prediction across four popular datasets.
The Hessian Screening Rule

Predictor screening rules, which discard predictors before fitting a model, have had considerable impact on the speed with which sparse regression problems, such as the lasso, can be solved. In this paper we present a new screening rule for solving the lasso path: the Hessian Screening Rule. The rule uses second-order information from the model to provide both effective screening, particularly in the case of high correlation, as well as accurate warm starts. The proposed rule outperforms all alternatives we study on simulated data sets with both low and high correlation for $\ell_1$-regularized least-squares (the lasso) and logistic regression. It also performs best in general on the real data sets that we examine.
LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach

A LightGBM model fed with target word lexical characteristics and features obtained from word frequency lists, psychometric data and bigram association measures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data Prediction. It obtained the best performance of all teams on two of the five eye-tracking measures to predict, allowing it to rank first on the official challenge criterion and to outperform all deep-learning based systems participating in the challenge.
WALLABY Pilot Survey: The Diversity of Ram Pressure Stripping of the Galactic H i Gas in the Hydra Cluster

This study uses H i image data from the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY) pilot survey with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, covering the Hydra cluster out to 2.5r 200. We present the projected phase–space distribution of H i-detected galaxies in Hydra, and identify that nearly two-thirds of the galaxies within 1.25r200 may be in the early stages of ram pressure stripping. More than half of these may be only weakly stripped, with the ratio of strippable H i (i.e., where the galactic restoring force is lower than the ram pressure in the disk) mass fraction (over total H i mass) distributed uniformly below 90%. Consequently, the H i mass is expected to decrease by only a few 0.1 dex after the currently strippable portion of H i in these systems has been stripped. A more detailed look at the subset of galaxies that are spatially resolved by WALLABY observations shows that, while it typically takes less than 200 Myr for ram pressure stripping to remove the currently strippable portion of H i, it may take more than 600 Myr to significantly change the total H i mass. Our results provide new clues to understanding the different rates of H i depletion and star formation quenching in cluster galaxies.
Security, Privacy and Trust: Cognitive Internet of Vehicles

The recent advancement of cloud technology offers unparallel strength to support intelligent computations and advanced services to assist with automated decisions to improve road transportation safety and comfort. Besides, the rise of machine intelligence propels the technological evolution of transportation systems one step further and leads to a new framework known as Cognitive Internet of Vehicles (C-IoV). The redefined cognitive technology in this framework promises significant enhancements and optimized network capacities compared with its predecessor framework, the Internet of Vehicles (IoV). CIoV offers additional security measures and introduces security and privacy concerns, such as evasion attacks, additional threats of data poisoning, and learning errors, which may likely lead to system failure and road user fatalities. Similar to many other public enterprise systems, transportation has a significant impact on the population. Therefore, it is crucial to understand the evolution and equally essential to identify potential security vulnerabilities and issues to offer mitigation towards success. This chapter offers discussions framing answers to the following two questions, 1) how and in what ways the penetration of the latest technologies are reshaping the transportation system? 2) whether the evolved system is capable of addressing the concerns of cybersecurity? This chapter, therefore, starts presenting the evolution of the transportation system followed by a quick overview of the evolved CIoV, highlighting the evolved cognitive design. Later it presents how a cognitive engine can overcome legacy security concerns and also be subjected to further potential security, privacy, and trust issues that this cloud-based evolved transportation system may encounter.
Balancing Constraints and Submodularity in Data Subset Selection

Deep learning has yielded extraordinary results in vision and natural language processing, but this achievement comes at a cost. Most deep learning models require enormous resources during training, both in terms of computation and in human labeling effort. In this paper, we show that one can achieve similar accuracy to traditional deep-learning models, while using less training data. Much of the previous work in this area relies on using uncertainty or some form of diversity to select subsets of a larger training set. Submodularity, a discrete analogue of convexity, has been exploited to model diversity in various settings including data subset selection. In contrast to prior methods, we propose a novel diversity driven objective function, and balancing constraints on class labels and decision boundaries using matroids. This allows us to use efﬁcient greedy algorithms with approximation guarantees for subset selection. We out-perform baselines on standard image classiﬁcation datasets such as CIFAR-10, CIFAR-100, and ImageNet. In addition, we also show that the proposed balancing constraints can play a key role in boosting the performance in long-tailed datasets such as CIFAR-100-LT.
Flow-Aware Forwarding in SDN Datacenters Using a Knapsack-PSO-Based Solution

With the rapid growth of different massive applications and parallel flow requests in Data Center Networks (DCNs), today’s providers are confronting challenges in flow forwarding decisions. Since Software Defined Networking (SDN) provides fine granular control, it can be intelligently programmed to distinguish between flow requirements. The present article proposes a knapsack model in which the link bandwidth and incoming flows are modeled as a knapsack capacity and items, respectively. Furthermore, each flow consists of two size and value aspects, acquired through flow size extraction and the type of service value assigned by the SDN controller decision. Indeed, the current work splits the incoming flow size range into Type of Service (ToS) decimal value numbers. The lower the flow size category, the higher the value dedicated to the flow. Particle Swarm Optimization (PSO) optimizes the knapsack problem and first forwards the selected-flows by KP-PSO, and the non-selected-flows second. To address the shortcomings of these methods in the event of dense parallel flow detection, the present study puts the link under the threshold of a 70% load by simultaneous requests. Experimental results indicate that the proposed method outperforms Sonum, Hedera, and ECMP in terms of flow completion time, packet loss rate, and goodput regarding flow size requirements.
Neuromimetic Control — A Linear Model Paradigm

Stylized models of the neurodynamics that under-pin sensory motor control in animals are proposed and studied. The voluntary motions of animals are typically initiated by high level intentions created in the primary cortex through a combination of perceptions of the current state of the environment along with memories of past reactions to similar states. Muscle movements are produced as a result of neural processes in which the parallel activity of large multiplicities of neurons generate signals that collectively lead to desired actions. Essential to coordinated muscle movement are intentionality, prediction, regions of the cortex dealing with misperceptions of sensory cues, and a significant level of resilience with respect to disruptions in the neural pathways through which signals must propagate. While linear models of feedback control systems have been well studied over many decades, this paper proposes and analyzes a class of models whose aims are to capture some of the essential features of neural control of movement. Whereas most linear models of feedback systems entail a state component whose dimension is higher than the number of inputs or outputs, the work that follows will treat models in which the numbers of input and output channels greatly exceed the state dimension. While we begin by considering continuous-time systems governed by differential equations, the aim will be to treat systems whose evolution involves classes of inputs that emulate neural spike trains. Within the proposed class of models, the paper will study resilience to channel dropouts, the ways in which noise and uncertainty can be mitigated by an appropriate notion of consensus among noisy inputs, and finally, by a simple model in which binary activations of a multiplicity of input channels produce a dynamic response that closely approximates the dynamics of a prescribed linear system whose inputs are continuous functions of time.
CPS Engineering: Gap Analysis and Perspectives

Virtualization of computing and networking, IT-OT convergence, cybersecurity and AI-based enhancement of autonomy are significantly increasing the complexity of CPS and CPSoS. New challenges have emerged to demonstrate that these systems are safe and secure. We emphasize the role of control and emerging fields therein, like symbolic control or set-based fault-tolerant and decentralized control, to address safety. We have chosen three open verification problems we deem central in cost-effective development and certification of safety critical CPSoS. We review some promising threads of research that could lead in the long term to a scalable and powerful verification strategy. Its main components are set-based and invariant-based design, contracts, adversarial testing, algorithmic geometry of dynamics, and probabilistic estimation derived from compositional massive testing. To explore these orientations in collaborative projects, and to promote them in certification arenas, we propose to continue and upgrade an open innovation drone-based use case that originated from a collaborative research project in aeronautic certification reformation
Scalable Metagrating for Efficient Ultrasonic Focusing

Acoustic focusing plays a pivotal role in a wide variety of applications ranging from medical science to nondestructive testing. Previous works have shown that acoustic metagratings can overcome the inherent efficiency limitations of gradient metasurfaces in beam steering. In this work, we propose a new design principle for acoustic metalenses, based on metagratings, to achieve efficient ultrasonic focusing. We achieve beam focusing by locally controlling the excitation of a single diffraction order with the use of adiabatically varying metagratings over the lens aperture. A set of metagratings is optimized by a semi-analytical approach using a genetic algorithm, enabling efficient anomalous reflection for a wide range of reflection angles. Numerical results reveal that our metalens can effectively focus impinging ultrasonic waves to a focal point of FWHM = 0. 364λ. The focusing performance of the metalens is demonstrated experimentally, validating our proposed approach.
Compressive Sampling Using a Pushframe Camera

The recently described pushframe imager, a parallelized single pixel camera capturing with a pushbroom-like motion, is intrinsically suited to both remote-sensing and compressive sampling. It optically applies a 2D mask to the imaged scene, before performing light integration along a single spatial axis, but previous work has not made use of the architecture's potential for taking measurements sparsely. In this paper we develop a strongly performing static binarized noiselet compressive sampling mask design, tailored to pushframe hardware, allowing both a single exposure per motion time-step, and retention of 2D correlations in the scene. Results from simulated and real-world captures are presented, with performance shown to be similar to that of immobile — and hence inappropriate for satellite use — whole-scene imagers. A particular feature of our sampling approach is that the degree of compression can be varied without altering the pattern, and we demonstrate the utility of this for efficiently storing and transmitting multi-spectral images.
Real-time safety assessment of trajectories for autonomous driving

Autonomous vehicles (AVs) must always have a safe motion to guarantee that they are not causing any accidents. In an AV system, the motion of the vehicle is represented as a trajectory. A trajectory planning component is responsible to compute such a trajectory at run-time, taking into account the perception information about the environment, the dynamics of the vehicles, the predicted future states of other road users and a number of safety aspects. Due to the enormous amount of information to be considered, trajectory planning algorithms are complex, which makes it non-trivial to guarantee the safety of all planned trajectories. In this way, it is necessary to have an extra component to assess the safety of the planned trajectories at run-time. Such trajectory safety assessment component gives a diverse observation on the safety of AV trajectories and ensures that the AV only follows safe trajectories. We use the term trajectory checker to refer to the trajectory safety assessment component. The trajectory checker must evaluate planned trajectories against various safety rules, taking into account a large number of possibilities, including the worst-case behavior of other traffic participants. This must be done while guaranteeing hard real-time performance since the safety assessment is carried out while the vehicle is moving and in constant interaction with the environment. In this paper, we present a prototype of the trajectory checker we have developed at IVEX. We show how our approach works smoothly and accomplishes real-time constraints embedded in an Infineon Aurix TC397B automotive platform. Finally, we measure the performance of our trajectory checker prototype against a set of NCAPS-inspired scenarios.
On dependent generalized sensitivity indices and asymptotic distributions

Matieyendou Lamboni University of Guyane and UMR Espace-Dev-228 (University of Guyane, University of Réunion, IRD, University of Montpellier), French Guiana, France e-mail: matieyendou.lamboni@gmail.com, matieyendou.lamboni@univ-guyane.fr Abstract: In this paper, we propose a novel methodology for better performing uncertainty and sensitivity analysis for complex mathematical models under constraints and/or with dependent input variables, including correlated variables. Our approach allows for assessing the single, overall and interactions effects of any subset of input variables, that account for the dependencies structures inferred by the constraints. Using the variance as importance measure among others, we define the main-effect and total sensitivity indices of input(s) with the former index less than the latter. We also derive the consistent estimators and asymptotic distributions of such indices by distinguishing the case of the multivariate and/or functional outputs, including spatio-temporal models and dynamic models.
Smoothing fast iterative hard thresholding algorithm for $\ell_0$ regularized nonsmooth convex regression problem

We investigate a class of constrained sparse regression problem with cardinality penalty, where the feasible set is defined by box constraint, and the loss function is convex, but not necessarily smooth. First, we put forward a smoothing fast iterative hard thresholding (SFIHT) algorithm for solving such optimization problems, which combines smoothing approximations, extrapolation techniques and iterative hard thresholding methods. The extrapolation coefficients can be chosen to satisfy $\sup_k \beta_k=1$ in the proposed algorithm. We discuss the convergence behavior of the algorithm with different extrapolation coefficients, and give sufficient conditions to ensure that any accumulation point of the iterates is a local minimizer of the original cardinality penalized problem. In particular, for a class of fixed extrapolation coefficients, we discuss several different update rules of the smoothing parameter and obtain the convergence rate of $O(\ln k/k)$ on the loss and objective function values. Second, we consider the case in which the loss function is Lipschitz continuously differentiable, and develop a fast iterative hard thresholding (FIHT) algorithm to solve it. We prove that the iterates of FIHT converge to a local minimizer of the problem that satisfies a desirable lower bound property. Moreover, we show that the convergence rate of loss and objective function values are $o(k^{-2})$. Finally, some numerical examples are presented to illustrate the theoretical results.