Miracle-dz/UniFAR-FTUs huggingface dataset:
Pricing Multivariate European Equity Option Using Gaussians Mixture Distributions and EVT-Based Copulas

In this article, we present an approach which allows taking into account the effect of extreme values in the modeling of financial asset returns and in the valorisation of associated options. Specifically, the marginal distribution of asset returns is modelled by a mixture of two Gaussian distributions. Moreover, we model the joint dependence structure of the returns using a copula function, the extremal one, which is suitable for our financial data, particularly the extreme values copulas. Applications are made on the Atos and Dassault Systems actions of the CAC40 index. Monte Carlo method is used to compute the values of some equity options such as the call on maximum, the call on minimum, the digital option, and the spreads option with the basket (Atos, Dassault systems) as underlying.
One2Set: Generating Diverse Keyphrases as a Set

Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a target sequence during training. However, the keyphrases are inherently an unordered set rather than an ordered sequence. Imposing a predefined order will introduce wrong bias during training, which can highly penalize shifts in the order between keyphrases. In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases. To fit this paradigm, we propose a novel model that utilizes a fixed set of learned control codes as conditions to generate a set of keyphrases in parallel. To solve the problem that there is no correspondence between each prediction and target during training, we propose a K-step label assignment mechanism via bipartite matching, which greatly increases the diversity and reduces the repetition rate of generated keyphrases. The experimental results on multiple benchmarks demonstrate that our approach significantly outperforms the state-of-the-art methods.
SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification

Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the model's decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection. Results: Making sense of the transformer's self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts. Conclusion: Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes. Significance: Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings.
Quantitative Heegaard Floer cohomology and the Calabi invariant

Abstract We define a new family of spectral invariants associated to certain Lagrangian links in compact and connected surfaces of any genus. We show that our invariants recover the Calabi invariant of Hamiltonians in their limit. As applications, we resolve several open questions from topological surface dynamics and continuous symplectic topology: We show that the group of Hamiltonian homeomorphisms of any compact surface with (possibly empty) boundary is not simple; we extend the Calabi homomorphism to the group of hameomorphisms constructed by Oh and Müller, and we construct an infinite-dimensional family of quasi-morphisms on the group of area and orientation preserving homeomorphisms of the two-sphere. Our invariants are inspired by recent work of Polterovich and Shelukhin defining and applying spectral invariants, via orbifold Floer homology, for links composed of parallel circles in the two-sphere. A particular feature of our work is that it avoids the orbifold setting and relies instead on ‘classical’ Floer homology. This not only substantially simplifies the technical background but seems essential for some aspects (such as the application to constructing quasi-morphisms).
4D printing of mechanical metamaterials

Mechanical metamaterials owe their extraordinary properties and functionalities to their micro/nanoscale design of which shape, including both geometry and topology, is perhaps the most important aspect. 4D printing enables programmed, predictable, and precise change in the shape of mechanical metamaterials to achieve multi-functionality, adaptive properties, and the other types of desired behaviors that cannot be achieved using simple 3D printing. This paper presents an overview of 4D printing as applied to mechanical metamaterials. It starts by presenting a systematic definition of what 4D printing is and what shape aspects (e.g., geometry, topology) are relevant for the 4D printing of mechanical metamaterials. Instead of focusing on different printing processes and materials, the paper addresses the most fundamental aspects of the shapeshifting behaviors required for transforming a flat construct to a target 3D shape (i.e., 2D to 3D shapeshifting) or transforming a 3D shape to another 3D shape (i.e., 3D to 3D shapeshifting). In either case, we will discuss the rigid-body shape morphing (e.g., rigid origami) as well as deformable-body shapeshifting. The paper concludes with a discussion of the major challenges ahead of us for applying 4D printing to mechanical metamaterials and suggests several areas for future research.
Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model

3-D action recognition is referred to as the classification of action sequences which consist of 3-D skeleton joints. While many research works are devoted to 3-D action recognition, it mainly suffers from three problems: 1) highly complicated articulation; 2) a great amount of noise; and 3) low implementation efficiency. To tackle all these problems, we propose a real-time 3-D action-recognition framework by integrating the locally aggregated kinematic-guided skeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model. We first define the skeletonlet as a few combinations of joint offsets grouped in terms of the kinematic principle and then represent an action sequence using LAKS, which consists of a denoising phase and a locally aggregating phase. The denoising phase detects the noisy action data and adjusts it by replacing all the features within it with the features of the corresponding previous frame, while the locally aggregating phase sums the difference between an offset feature of the skeletonlet and its cluster center together over all the offset features of the sequence. Finally, the SHA model combines sparse representation with a hashing model, aiming at promoting the recognition accuracy while maintaining high efficiency. Experimental results on MSRAction3D, UTKinectAction3D, and Florence3DAction datasets demonstrate that the proposed method outperforms state-of-the-art methods in both recognition accuracy and implementation efficiency.
Polyhedron phase space using 2-groups: kappa-Poincare as a Poisson 2-group

We construct a phase space for a three dimensional cellular complex with decorations on edges and faces using crossed modules (strict 2-groups) equipped with a (non-trivial) Poisson structure. We do not use the most general crossed module, but only the ones where the target map (t-map) is trivial. As a particular case, we recover that deformations of the Poincaré group can be exported to deformations of the Poincaré 2-group. The κ-deformation case provides a natural candidate for describing discrete geometries with curved edge decorations (but still flat face decorations). Our construction generalizes the classical phase space defined in [5] for a 3d triangulation in terms of an un-deformed Poincaré 2-group.
SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?

Social Networks' omnipresence and ease of use has revolutionized the generation and distribution of information in today's world. However, easy access to information does not equal an increased level of public knowledge. Unlike traditional media channels, social networks also facilitate faster and wider spread of disinformation and misinformation. Viral spread of false information has serious implications on the behaviors, attitudes and beliefs of the public, and ultimately can seriously endanger the democratic processes. Limiting false information's negative impact through early detection and control of extensive spread presents the main challenge facing researchers today. In this survey paper, we extensively analyze a wide range of different solutions for the early detection of fake news in the existing literature. More precisely, we examine Machine Learning (ML) models for the identification and classification of fake news, online fake news detection competitions, statistical outputs as well as the advantages and disadvantages of some of the available data sets. Finally, we evaluate the online web browsing tools available for detecting and mitigating fake news and present some open research challenges.
Soft Robotic Suits: State of the Art, Core Technologies, and Open Challenges

Wearable robots are undergoing a disruptive transition, from the rigid machines that populated the science-fiction world in the early 1980s to lightweight robotic apparel, hardly distinguishable from our daily clothes. In less than a decade of development, soft robotic suits have achieved important results in human motor assistance and augmentation. In this article, we start by giving a definition of soft robotic suits and proposing a taxonomy to classify existing systems. We then critically review the modes of actuation, the physical human–robot interface and the intention-detection strategies of state-of-the-art soft robotic suits, highlighting the advantages and limitations of different approaches. Finally, we discuss the impact of this new technology on human movements, for both augmenting human function and supporting motor impairments, and identify areas that are in need of further development.
HOME: Heatmap Output for future Motion Estimation

In this paper, we propose HOME, a framework tackling the motion forecasting problem with an image output representing the probability distribution of the agent's future location. This method allows for a simple architecture with classic convolution networks coupled with attention mechanism for agent interactions, and outputs an unconstrained 2D top-view representation of the agent's possible future. Based on this output, we design two methods to sample a finite set of agent's future locations. These methods allow us to control the optimization trade-off between miss rate and final displacement error for multiple modalities without having to retrain any part of the model. We apply our method to the Argoverse Motion Forecasting Benchmark and achieve 1st place on the online leaderboard.
Chemical abundances in the nuclear region of nearby galaxies from the Palomar Survey

We estimate chemical abundances and ionization parameters in the nuclear region of a sample of 143 galaxies from the Palomar Spectroscopic Survey, composed by Star-Forming Galaxies (87), Seyferts 2 (16) and LINERs (40) using the Hii-Chi-mistry code. We also study for each spectral type the correlation of the derived quantities with other different properties of the host galaxies, such as morphology, stellar mass, luminosity and mass of their Supermassive Black Holes. The results obtained for Star-Forming Galaxies are used to check the soundness of our methodology. Then, we replicate a similar study for our sample of AGN, distinguishing between Seyferts 2 and LINERs. We report a saturation of Oxygen abundances for the nuclear regions of SFG. The correlations between chemical abundances and their host galaxy properties for SFG are in good agreement with previous studies. We find that Seyferts 2 present slightly higher chemical abundances but this result must be reexamined in larger samples of Seyfert galaxies. In contrast, we obtain lower chemical abundances for LINERs than for SFG. We confirm these relatively lower abundances for another sample of infrared luminous LINERs in the same stellar mass range. Our analysis of AGNs (both LINERs and Seyferts) shows that their host galaxy properties are not correlated with our estimated chemical abundances.
Unsupervised Video Summarization with a Convolutional Attentive Adversarial Network

With the explosive growth of video data, video summarization, which attempts to seek the minimum subset of frames while still conveying the main story, has become one of the hottest topics. Nowadays, substantial achievements have been made by supervised learning techniques, especially after the emergence of deep learning. However, it is extremely expensive and difficult to collect human annotation for large-scale video datasets. To address this problem, we propose a convolutional attentive adversarial network (CAAN), whose key idea is to build a deep summarizer in an unsupervised way. Upon the generative adversarial network, our overall framework consists of a generator and a discriminator. The former predicts importance scores for all frames of a video while the latter tries to distinguish the score-weighted frame features from original frame features. Specifically, the generator employs a fully convolutional sequence network to extract global representation of a video, and an attention-based network to output normalized importance scores. To learn the parameters, our objective function is composed of three loss functions, which can guide the frame-level importance score prediction collaboratively. To validate this proposed method, we have conducted extensive experiments on two public benchmarks SumMe and TVSum. The results show the superiority of our proposed method against other state-of-the-art unsupervised approaches. Our method even outperforms some published supervised approaches.
Classifying Trusted Hardware via Unidirectional Communication

It is well known that Byzantine fault tolerant (BFT) consensus cannot be solved in the classic asynchronous message passing model when one-third or more of the processes may be faulty. Since many modern applications require higher fault tolerance, this bound has been circumvented by introducing non-equivocation mechanisms that prevent Byzantine processes from sending conflicting messages to other processes. The use of trusted hardware is a way to implement non-equivocation. Several different trusted hardware modules have been considered in the literature. In this paper, we study whether all trusted hardware modules are equivalent in the power that they provide to a system. We show that while they do all prevent equivocation, we can partition trusted hardware modules into two different power classes; those that employ shared memory primitives, and those that do not. We separate these classes using a new notion we call unidirectionality, which describes a useful guarantee on the ability of processes to prevent network partitions. We show that shared-memory based hardware primitives provide unidirectionality, while others do not.
Reduction in Circulating Current With Improved Secondary Side Modulation in Isolated Current-Fed Half Bridge AC–DC Converter

Current-fed half bridge converter with bidirectional switches on ac side and a full bridge converter on dc side of a high frequency transformer is an optimal topology for single stage galvanically isolated ac–dc converter for onboard vehicle charging application. The ac side switches are actively commutated to achieve zero current switching (ZCS) using single phase shift modulation (SPSM) and discontinuous current phase shift modulation (DCPSM). Furthermore, zero voltage turn-on (ZVS) is achieved for dc side switches. Compared with SPSM, DCPSM maintains a constant peak current in the converter throughout the grid cycle of ac mains voltage. However, constant peak current contributes to a high circulating current near the zero crossings of ac mains voltage and also at light load conditions. This article proposes an improved discontinuous current phase shift modulation to increase the efficiency of the converter across different loading conditions. A dual control variable is adopted to actively reduce the circulating current while maintaining soft switching of both ac and dc side switches across the grid cycle of ac mains voltage. A 1.5-kW laboratory prototype has been developed to experimentally validate the analysis, design, and improvement in performance for different loading conditions.
Weakly-supervised Cross-view 3D Human Pose Estimation

Although monocular 3D human pose estimation methods have made signiﬁcant progress, it’s far from being solved due to the inherent depth ambiguity. Instead, exploiting multi-view information is a practical way to achieve absolute 3D human pose estimation. In this paper, we propose a simple yet effective pipeline for weakly-supervised cross-view 3D human pose estimation. By only using two camera views, our method can achieve state-of-the-art performance in a weakly-supervised manner, requiring no 3D ground truth but only 2D annotations. Speciﬁcally, our method contains two steps: triangulation and reﬁnement. First, given the 2D keypoints that can be obtained through any classic 2D detection methods, triangulation is performed across two views to lift the 2D keypoints into coarse 3D poses. Then, a novel cross-view U-shaped graph convolutional network (CV-UGCN), which can explore the spatial conﬁgurations and cross-view correlations, is designed to reﬁne the coarse 3D poses. In particular, the reﬁnement progress is achieved through weakly-supervised learning, in which geometric and structure-aware consistency checks are performed. We evaluate our method on the standard benchmark dataset, Human3.6M. The Mean Per Joint Position Error on the benchmark dataset is 27.4 mm, which outperforms the state-of-the-arts remarkably (27.4 mm vs 30.2 mm).
Trade-off Energy and Spectral Efficiency in 5G Massive MIMO System

A massive multiple input multiple-output system is very important to optimize the trade-off energy efficiency and spectral efficiency in fifth-generation cellular networks. The challenges for the next generation depend on increasing the high data traffic in the wireless communication system for both EE and SE. In this paper, the trade off energy efficiency and spectral efficiency based on the first derivative of transmit antennas and transmit power in a downlink massive MIMO system has been investigated. The trade off EE-SE by using a multiobjective optimization problem to decrease transmit power has been analyzed. The EE and SE based on constraint maximum transmit power allocation and a number of antennas by computing the first derivative of transmit power to maximize the trade-off energy efficiency and spectral efficiency has been improved. From the simulation results, the optimum trade-off between EE and SE can be obtained based on the first derivative by selecting the optimal antennas with a low cost of transmit power. Therefore, based on an optimal optimization problem is flexible to make trade-offs between EE-SE for distinct preferences
Nonlinear Lie-Type Derivations of finitary Incidence Algebras and Related Topics

This is a continuation of our earlier works [29, 51, 52] with respect to (non-)linear Lie-type derivations of finitary incidence algebras. Let X be a pre-ordered set, R be a 2-torsionfree and (n − 1)torsionfree commutative ring with identity, where n ≥ 2 is an integer. Let FI(X,R) be the finitary incidence algebra of X over R. In this paper, a complete clarification is obtained for the structure of nonlinear Lie-type derivations of FI(X,R). We introduce a new class of derivations on FI(X,R) named inner-like derivations, and prove that each nonlinear Lie n-derivation on FI(X,R) is the sum of an inner-like derivation, a transitive induced derivation and a quasi-additive induced Lie n-derivation. Furthermore, if X is finite, we show that a quasi-additive induced Lie n-derivation can be expressed as the sum of an additive induced Lie derivation and a central-valued map annihilating all (n−1)-th commutators. We also provide a sufficient and necessary condition such that every nonlinear Lie n-derivation of FI(X,R) is of proper form. Some related topics for further research are proposed in the last section of this article.
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. This is done by controlling multiple unmanned aerial vehicles (UAVs) in real time while avoiding inter-UAV collisions. To this end, we propose a novel multiagent deep reinforcement learning (MADRL) framework, coined a graph attention exchange network (GAXNet). In GAXNet, each UAV constructs an attention graph locally measuring the level of attention to its neighboring UAVs, while exchanging the attention weights with other UAVs so as to reduce the attention mismatch between them. Simulation results corroborates that GAXNet achieves up to 4.5x higher rewards during training. At execution, without incurring inter-UAV collisions, G2ANet improves reliability of air-to-ground network in terms of latency and error rate.
De-identification of Privacy-related Entities in Job Postings

De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data. It has been well-studied within the medical domain. The need for de-identification technology is increasing, as privacy-preserving data handling is in high demand in many domains. In this paper, we focus on job postings. We present JobStack, a new corpus for de-identification of personal data in job vacancies on Stackoverflow. We introduce baselines, comparing Long-Short Term Memory (LSTM) and Transformer models. To improve these baselines, we experiment with BERT representations, and distantly related auxiliary data via multi-task learning. Our results show that auxiliary data helps to improve de-identification performance. While BERT representations improve performance, surprisingly “vanilla” BERT turned out to be more effective than BERT trained on Stackoverflow-related data.
A new computational framework for log-concave density estimation

In Statistics, log-concave density estimation is a central problem within the field of nonparametric inference under shape constraints. Despite great progress in recent years on the statistical theory of the canonical estimator, namely the log-concave maximum likelihood estimator, adoption of this method has been hampered by the complexities of the non-smooth convex optimization problem that underpins its computation. We provide enhanced understanding of the structural properties of this optimization problem, which motivates the proposal of new algorithms, based on both randomized and Nesterov smoothing, combined with an appropriate integral discretization of increasing accuracy. We prove that these methods enjoy, both with high probability and in expectation, a convergence rate of order $1/T$ up to logarithmic factors on the objective function scale, where $T$ denotes the number of iterations. The benefits of our new computational framework are demonstrated on both synthetic and real data, and our implementation is available in a github repository \texttt{LogConcComp} (Log-Concave Computation).