Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-Assisted Data Dissemination Scheduling in VANETs

In high-speed vehicular ad-hoc networks (VANETs), cooperative data dissemination is an effective solution to amend the limited connection time of communication links between roadside units (RSUs) and vehicles. Existing data dissemination strategies utilize efficient cooperation of vehicle-to-vehicle (V2V) and vehicle- to-infrastructure (V2I) communication links to maintain the data transmissions and thus improve the system performance. Recently, unmanned aerial vehicle (UAV) is widely utilized in communication systems, which has a high probability of line-of-sight (LoS) links with better channel quality and can be dynamically deployed. In this paper, we propose a novel UAV-assisted data dissemination scheduling strategy in VANETs. The recursive least squares (RLS) algorithm is utilized to predict the vehicle mobility with low complexity and high prediction accuracy. To enhance the transmission utilities of the UAVs, we further propose a maximum vehicle coverage (MVC) algorithm to schedule the two- dimensional (2D) movements of the UAVs during the process of data dissemination. Simulations in both urban and highway scenarios verify that the proposed UAV-assisted data dissemination strategy achieves a significant reduction of data dissemination delay and an improvement of system throughput.
Multicalibration: Calibration for the (Computationally-Identifiable) Masses

We develop and study multicalibration as a new measure of fairness in machine learning that aims to mitigate inadvertent or malicious discrimination that is introduced at training time (even from ground truth data). Multicalibration guarantees meaningful (calibrated) predictions for every sub-population that can be identiﬁed within a speciﬁed class of computations. The speciﬁed class can be quite rich; in particular, it can contain many overlapping subgroups of a protected group. We demonstrate that in many settings this strong notion of protection from discrimination is provably attainable and aligned with the goal of accurate predictions. Along the way, we present algorithms for learning a multicalibrated predictor, study the computational complexity of this task, and illustrate tight connections to the agnostic learning model.
Numerical coarsening using discontinuous shape functions

In this paper, an efficient and scalable approach for simulating inhomogeneous and non-linear elastic objects is introduced. Our numerical coarsening approach consists in optimizing non-conforming and matrix-valued shape functions to allow for predictive simulation of heterogeneous materials with non-linear constitutive laws even on coarse grids, thus saving orders of magnitude in computational time compared to traditional finite element computations. The set of local shape functions over coarse elements is carefully tailored in a preprocessing step to balance geometric continuity and local material stiffness. In particular, we do not impose continuity of our material-aware shape functions between neighboring elements to significantly reduce the fictitious numerical stiffness that conforming bases induce; however, we enforce crucial geometric and physical properties such as partition of unity and exact reproduction of representative fine displacements to eschew the use of discontinuous Galerkin methods. We demonstrate that we can simulate, with no parameter tuning, inhomogeneous and non-linear materials significantly better than previous approaches that traditionally try to homogenize the constitutive model instead.
Mecbanomyograpby Based Closed-Loop Functional Electrical Stimulation Cycling System

Functional Electrical Stimulation (FES) systems are successful in restoring motor function and supporting paralyzed users. Commercially available FES products are open loop, meaning that the system is unable to adapt to changing conditions with the user and their muscles which results in muscle fatigue and poor stimulation protocols. This is because it is difficult to close the loop between stimulation and monitoring of muscle contraction using adaptive stimulation. FES causes electrical artefacts which make it challenging to monitor muscle contractions with traditional methods such as electromyography (EMG). We look to overcome this limitation by combining FES with novel mechanomyographic (MMG) sensors to be able to monitor muscle activity during stimulation in real time. To provide a meaningful task we built an FES cycling rig with a software interface that enabled us to perform adaptive recording and stimulation, and then combine this with sensors to record forces applied to the pedals using force sensitive resistors (FSRs); crank angle position using a magnetic incremental encoder and inputs from the user using switches and a potentiometer. We illustrated this with a closed-loop stimulation algorithm that used the inputs from the sensors to control the output of a programmable RehaStim 1 FES stimulator (Hasomed) in real-time. This recumbent bicycle rig was used as a testing platform for FES cycling. The algorithm was designed to respond to a change in requested speed (RPM) from the user and change the stimulation power (% of maximum current mA) until this speed was achieved and then maintain it.
A Projected Gradient and Constraint Linearization Method for Nonlinear Model Predictive Control

Projected Gradient Descent denotes a class of iterative methods for solving optimization programs. Its applicability to convex optimization programs has gained significant popularity for its intuitive implementation that involves only simple algebraic operations. In fact, if the projection onto the feasible set is easy to compute, then the method has low complexity. On the other hand, when the problem is nonconvex, e.g. because of nonlinear equality constraints, the projection becomes hard and thus impractical. In this paper, we propose a projected gradient method for Nonlinear Programs (NLPs) that only requires projections onto the linearization of the nonlinear constraints around the current iterate, similarly to Sequential Quadratic Programming (SQP). Although the projection is easier to compute, it makes the intermediate steps unfeasible for the original problem. As a result, the gradient method does not fall either into the projected gradient descent approaches, because the projection is not performed onto the original nonlinear manifold, or into the standard SQP, since second-order information is not used. For nonlinear smooth optimization problems, we analyze the similarities of the proposed method with SQP and assess its local and global convergence to a Karush-Kuhn-Tucker (KKT) point of the original problem. Further, we show that nonlinear Model Predictive Control (MPC) is a promising application of the proposed method, due to the sparsity of the resulting optimization problem. We illustrate the computational efficiency of the proposed method in a numerical example with box constraints on the control input and a quadratic terminal constraint on the state variable.
Event-triggered control for switched systems in the presence of actuator saturation

ABSTRACT In this paper, the event-triggered stabilisation problem for a class of switched systems with actuator saturation is investigated. Based on multiple Lyapunov function method and the average dwell time approach, a sufficient condition for exponential stability of such systems under event-triggered state-feedback controller is derived. To avoid Zeno behaviour, it is shown that there exists a positive lower bound for any two consecutive inter-event instants. Finally, two examples are presented to illustrate the effectiveness of the proposed method.
Three Body Problems in Evolutionary Game Dynamics: Convergence, Periodicity and Limit Cycles

We study the asymptotic behavior of replicator dynamics in settings of network interaction. We focus on three agent graphical games where each edge/game is either a 2x2 zero-sum or a 2x2 coordination game. Using tools from dynamical systems such as Lyapunov functions and invariant functions we establish that this simple family of games can exhibit an interesting range of behaviors such as global convergence, periodicity for all initial conditions as well as limit cycles. In contrast, we do not observe more complex behavior such as toroids or chaos while it is possible to reproduce them in slightly more complicated settings.
WordNet Embeddings

Semantic networks and semantic spaces have been two prominent approaches to represent lexical semantics. While a unified account of the lexical meaning relies on one being able to convert between these representations, in both directions, the conversion direction from semantic networks into semantic spaces started to attract more attention recently. In this paper we present a methodology for this conversion and assess it with a case study. When it is applied over WordNet, the performance of the resulting embeddings in a mainstream semantic similarity task is very good, substantially superior to the performance of word embeddings based on very large collections of texts like word2vec.
Evaluation of methods for taxonomic relation extraction from text

Modern information systems are changing the idea of “data processing” to the idea of “concept processing”, meaning that instead of processing words, such systems process semantic concepts which carry meaning and share contexts with other concepts. Ontology is commonly used as a structure that captures the knowledge about a certain area via providing concepts and relations between them. Traditionally, concept hierarchies have been built manually by knowledge engineers or domain experts. However, the manual construction of a concept hierarchy suffers from several limitations such as its coverage and the enormous costs of extension and maintenance. Furthermore, keeping up with a hand-crafted concept hierarchy along with the evolution of domain knowledge is an overwhelming task, being necessary to build concept hierarchies automatically. The (semi-)automatic support in ontology development is usually referred to as ontology learning. The ontology learning from texts is usually divided in steps, going from concepts identification, passing through hierarchy and non-hierarchy relations detection and, seldom, axiom extraction. It is reasonable to say that among these steps the current frontier is in the establishment of concept hierarchies, since this is the backbone of ontologies and, therefore, a good concept hierarchy is already a valuable resource for many ontology applications. A concept hierarchy is represented with a tree-structured form with specialization/generalization relations between concepts, in which lower-level concepts are more specific while higher-level are more general. The automatic construction of concept hierarchies from texts is a complex task and since the 1980 decade a large number of works have been proposing approaches to better extract relations between concepts. These different proposals have never been contrasted against each other on the same set of data and across different languages. Such comparison is important to see whether they are complementary or incremental, also we can see whether they present different tendencies towards recall and precision, i.e., some can be very precise but with very low recall and others can achieve better recall but low precision. Another aspect concerns to the variation of results for different languages. This thesis evaluates these different methods on the basis of hierarchy metrics such as density and depth, and evaluation metrics such as Recall and Precision. The evaluation is performed over the same corpora, which consist of English and Portuguese parallel and comparable texts. Both automatic and manual evaluations are presented. The output of seven methods are evaluated automatically and the output of four methods are evaluated manually. Results shed light over the comprehensive set of methods that are the state of the art according to the literature in the area.
Incentive Mechanism Design for Computation Offloading in Heterogeneous Fog Computing: A Contract-Based Approach

Fog computing is a promising solution for new emerging applications requiring intensive computation resources and low latency. Devices at the edge of network can share idle resources and collaboratively accomplish the computing tasks in fog computing. Thus, task publishers have heterogeneous options when offloading computing tasks considering the quality of transmission links, energy consumption and other hardware constraints of fog nodes. To incentivize these devices to participate in computation offloading, effective incentive mechanisms are needed. In this paper, utilizing the framework of contract theory, we formulate the negotiation between task ublisher and fog nodes as an optimization problem. The optimal contract is the Nash equilibrium solution achieved by task publisher and fog nodes. Simulation results show that an optimal contract can maximize the utility of task publisher meanwhile guarantee the individual rationality and incentive compatibility of fog nodes. Therefore, edge devices can be incentivized effectively to involve in the computation offloading.
Observations on tooth development and implantation in the upper pharyngeal jaws in Astatotilapia elegans (Teleostei, Cichlidae)

Prominent stages in the development of teeth, associated with the upper pharyngeal jaws in early postembryonic stages of the mouth brooding cichlid A statotilapia elegans were studied on semithin sections in relation to changes in the underlying endoskeletal parts and to the formation of the dentigerous bone. Because the pattern of tooth implantation on infrapharyngobranchial III‐IV is constant, at least in early postembryonic stages, it is possible to trace the life history of a given tooth by tracing its homologue throughout the ontogenetic series. A probable causal relationship exists between tooth development and erosion of the underlying cartilage. Fully developed, though unerupted teeth, differentiate annular bone of attachment, which, depending on its position, is formed either outside the cartilage or within the previously induced erosion cavities. Attachment bone of adjacent teeth fuses to build up the dentigerous bone, which, as a result, may be situated within the area previously occupied by cartilage. As soon as the tooth has built up its bone of attachment, it may erupt. The collagenous matrix between tooth and attachment bone persists and gives rise to the movable connection between both. Differentiation of teeth on infrapharyngobranchial III‐IV, together with enlargement of the dentigerous bone, proceeds from the lateral and the rostral border, where new germs constantly form. The appearance of new germs on infrapharyngobranchial II is more unpredictable.
Blockchain-Based IoT-Cloud Authorization and Delegation

In a Smart City scenario, the authors envisioned an IoT-Cloud framework for the management of boards and resources scattered over a geographic area. It can also become a tool to let device owners contribute freely to the infrastructure. In comparison to datacenter-oriented Cloud middleware, the administrator and the owner of the infrastructure are not one and the same. This translates into the requirement to support delegation-enabled authorization. In this paper, the authors investigate an authorization and delegation model for the IoT-Cloud based on blockchain technology. In particular, the scheme is implemented in the form of smart contracts over the Ethereum platform. Indeed, this approach represents an enhancement, over a function previously designed in a centralized fashion, by enabling the user to audit authorization operations and inspect how access control is actually performed, without blindly trusting the Cloud as a proxy for access to resources.
Activity representation by SURF-based templates

Abstract This paper presents a method to recognise actions, which are overlapping and multi-dimensionalities. A spatio-temporal representation is illustrated on local interest points to compute global features. Motion history image (MHI) is computed and motion overwriting the motion overwriting problem of the MHI. The main contribution of this paper is that it demonstrates a higher discriminative ability of various complex actions when compared to the other MHI-based approaches. It selects local interest feature points to capture motion information using Speeded-Up Robust Features (SURF). These key interest points are exploited to compute gradient-based optical flow into four channels. RANSAC is exploited to remove outliers. It incorporates frame-subtracted accumulated image so that we can mask out points that are not required. Afterwards, feature vectors are computed based on moments. Actions are recognised by employing a nearest neighbour classifier and leave-one-out cross-validation partitioning scheme. The proposed method provides satisfactory recognition rates over several other approaches for some challenging actions in outdoor scenes.
Convolutional Gated Recurrent Units for Medical Relation Classification

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have become the mainstream architectures for relation classification. We propose a unified architecture, which exploits the advantages of CNN and RNN simultaneously, to identify medical relations in clinical records, with only word embedding features. Our model learns phrase-level features through a CNN layer, and these feature representations are directly fed into a bidirectional gated recurrent unit (GRU) layer to capture long-term feature dependencies. We evaluate our model on two clinical datasets, and experiments demonstrate that our model performs better than previous single-model methods on both datasets.
Clinical Characteristics of Liver Metastasis in Nivolumab-treated Patients with Non-small Cell Lung Cancer

Background: Recent studies have revealed that liver metastasis is associated with poor outcomes after treatment using immune checkpoint inhibitors, although the cause remains unclear. Patients and Methods: We retrospectively identified 201 patients at three Japanese Centers who received nivolumab for advanced non-small cell lung cancer between December 2015 and July 2016. The patients' baseline clinical characteristics and subsequent outcomes were compared according to liver metastasis status. Results: Liver metastasis was associated with inferior progression-free survival (PFS) and a lower response rate. Additionally, liver metastasis was significantly associated with younger age, poorer Eastern Cooperative Oncology Group performance status (ECOG PS), and more metastatic sites. Multivariate analyses revealed that poor PFS was independently associated with poor baseline ECOG PS (p=0.039) and high number of metastatic sites (p=0.007), although liver metastasis (p=0.2) was not. Conclusion: Baseline clinical characteristics were a strong predictor of outcome in nivolumab-treated patients with liver metastasis.
Reward Learning over Weeks Versus Minutes Increases the Neural Representation of Value in the Human Brain

Over the past few decades, neuroscience research has illuminated the neural mechanisms supporting learning from reward feedback. Learning paradigms are increasingly being extended to study mood and psychiatric disorders as well as addiction. However, one potentially critical characteristic that this research ignores is the effect of time on learning: human feedback learning paradigms are usually conducted in a single rapidly paced session, whereas learning experiences in ecologically relevant circumstances and in animal research are almost always separated by longer periods of time. In our experiments, we examined reward learning in short condensed sessions distributed across weeks versus learning completed in a single “massed” session in male and female participants. As expected, we found that after equal amounts of training, accuracy was matched between the spaced and massed conditions. However, in a 3-week follow-up, we found that participants exhibited significantly greater memory for the value of spaced-trained stimuli. Supporting a role for short-term memory in massed learning, we found a significant positive correlation between initial learning and working memory capacity. Neurally, we found that patterns of activity in the medial temporal lobe and prefrontal cortex showed stronger discrimination of spaced- versus massed-trained reward values. Further, patterns in the striatum discriminated between spaced- and massed-trained stimuli overall. Our results indicate that single-session learning tasks engage partially distinct learning mechanisms from distributed training. Our studies begin to address a large gap in our knowledge of human learning from reinforcement, with potential implications for our understanding of mood disorders and addiction. SIGNIFICANCE STATEMENT Humans and animals learn to associate predictive value with stimuli and actions, and these values then guide future behavior. Such reinforcement-based learning often happens over long time periods, in contrast to most studies of reward-based learning in humans. In experiments that tested the effect of spacing on learning, we found that associations learned in a single massed session were correlated with short-term memory and significantly decayed over time, whereas associations learned in short massed sessions over weeks were well maintained. Additionally, patterns of activity in the medial temporal lobe and prefrontal cortex discriminated the values of stimuli learned over weeks but not minutes. These results highlight the importance of studying learning over time, with potential applications to drug addiction and psychiatry.
Stratified Sampling of Projected Spherical Caps

We present a method for uniformly sampling points inside the projection of a spherical cap onto a plane through the sphere's center. To achieve this, we devise two novel area‐preserving mappings from the unit square to this projection, which is often an ellipse but generally has a more complex shape. Our maps allow for low‐variance rendering of direct illumination from finite and infinite (e.g. sun‐like) spherical light sources by sampling their projected solid angle in a stratified manner. We discuss the practical implementation of our maps and show significant quality improvement over traditional uniform spherical cap sampling in a production renderer.