Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Domain Non-Orthogonal Transmission for Cellular Mobile Broadcasting: Basic Scheme, System Design, and Coverage Performance

Power domain non-orthogonal transmission is a promising technology for 5G wireless networks and beyond, as it can achieve higher spectrum efficiency than the orthogonal kind by multiplexing multiple users in the power domain. This article studies power domain non-orthogonal transmission for cellular mobile broadcasting to satisfy the increasing demands on multimedia communications in 5G and beyond. We first present two schemes for non-orthogonal transmission-based cellular mobile broadcasting: multi-rate and multi-service superposition transmissions, and then discuss their information-theoretical perspectives. Furthermore, we provide system designs for virtualized network architecture and physical layer processing, and discuss the key elements. We present a general superposition transmission framework to integrate three schemes developed by the 3GPP and to reduce the complexity of implementation, and then study constellation rotation to improve the BER performance of superposition transmission. Finally, we evaluate the SINR coverage performance of the presented schemes, followed by the main challenges and future research directions.
An ASP Methodology for Understanding Narratives about Stereotypical Activities

Abstract We describe an application of Answer Set Programming to the understanding of narratives about stereotypical activities, demonstrated via question answering. Substantial work in this direction was done by Erik Mueller, who modeled stereotypical activities as scripts. His systems were able to understand a good number of narratives, but could not process texts describing exceptional scenarios. We propose addressing this problem by using a theory of intentions developed by Blount, Gelfond, and Balduccini. We present a methodology in which we substitute scripts by activities (i.e., hierarchical plans associated with goals) and employ the concept of an intentional agent to reason about both normal and exceptional scenarios. We exemplify the application of this methodology by answering questions about a number of restaurant stories. This paper is under consideration for acceptance in TPLP.
The Neuroscience of Mathematical Cognition and Learning

The synergistic potential of cognitive neuroscience and education for efficient learning has attracted considerable interest from the general public, teachers, parents, academics and policymakers alike. This review is aimed at providing 1) an accessible and general overview of the research progress made in cognitive neuroscience research in understanding mathematical learning and cognition, and 2) understanding whether there is sufficient evidence to suggest that neuroscience can inform mathematics education at this point. We also highlight outstanding questions with implications for education that remain to be explored in cognitive neuroscience. The field of cognitive neuroscience is growing rapidly. The findings that we are describing in this review should be evaluated critically to guide research communities, governments and funding bodies to optimise resources and address questions that will provide practical directions for short- and long-term impact on the education of future generations. Le potentiel synergetique des neurosciences cognitives et de l’education pour l’efficacite de l’apprentissage suscite un vif interet de la part du grand public, des enseignants, des parents, des universitaires et des decideurs. Cet examen entend : 1) offrir un apercu general accessible des progres realises par la recherche en neurosciences cognitives dans la comprehension des processus d’apprentissage et de cognition en mathematiques ; et 2) determiner s’il existe des donnees suffisantes pour etayer la possibilite, a ce stade, d’une contribution des neurosciences a l’enseignement des mathematiques. Nous mettons egalement en lumiere certaines questions en suspens ayant des implications en termes d’education et restant a explorer dans les neurosciences cognitives, domaine connaissant un essor rapide. Les resultats que nous presentons dans cet examen doivent faire l’objet d’une evaluation critique afin de guider les communautes de chercheurs, les pouvoirs publics et les organismes de financement dans leurs efforts pour optimiser les ressources et repondre aux questions qui orienteront l’incidence a court et long termes sur l’education des generations futures.
Personal Identification by Gait Analysis ( August 2012 ) B

1 Abstract—Human recognition at a distance is important in security systems. In this document, is described an efficient identification method which is directly inspired from [3]. The problems with the existing algorithms are the accuracy of motion extraction and computation time. The solution proposed here is to identify people by measuring the closest similarity between 2 distance signals. The results obtained with this method are very encouraging. The accuracy of this algorithm is 95% in outdoor conditions with a database containing 20 people and has a low computational cost. I. INTRODUCTION iometrics is a technology which is very used to identify people by their characteristics (fingerprints, face, iris…), especially in control access and also in the video surveillance. Human motion analysis enables to understand the behavior of persons. This process can determine how individuals move (walking or running) for example. In video surveillance, the association of biometrics and human motion analysis is an active research area. The human identification at a distance has been developed due to the need of person identification systems in sensitive places such as airports, banks. This new research domain aims to distinguish people by the way they walk. The advantage of this new technique is that we don't need to have a contact with the person like in fingerprint recognition. Although, face recognition does not need a contact, it can't be applied at a great distance. Gait analysis is the only way to identify people at a great distance [3]. However, is gait a reliable feature for identification? Some studies [4] [5] have shown that each individual has its own way of walking. Moreover, you can notice that you do identification by gait every day. Indeed, when you recognize someone who is walking at a distance, you analyze his appearance and the dynamics of his walking motion to identify him [2]. The recognition of people depends on the variation of their silhouette when they walk. In this paper, gait motion is supposed to be a succession of static body postures. We expect that the data contained in silhouettes are statistically sufficient to enable the identification. The recognition of a gait is performed by counting the number of silhouettes which correspond to a sequence of postures representing a person registered in a database. The comparison between silhouettes is performed using PCA (Principal Component Analysis). This technique consisting in analyzing data in another space is very
Online Convolutional Sparse Coding with Sample-Dependent Dictionary

Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing. However, existing methods have limited scalability. In this paper, instead of convolving with a dictionary shared by all samples, we propose the use of a sample-dependent dictionary in which filters are obtained as linear combinations of a small set of base filters learned from the data. This added flexibility allows a large number of sample-dependent patterns to be captured, while the resultant model can still be efficiently learned by online learning. Extensive experimental results show that the proposed method outperforms existing CSC algorithms with significantly reduced time and space requirements.
Throughput Analysis for Relay-Assisted Millimeter-Wave Wireless Networks

In this work, we analyze the throughput of random access multi-user relay-assisted millimeter-wave wireless networks, in which both the destination and the relay have multipacket reception capability. We consider a full-duplex network cooperative relay that stores the successfully received packets in a queue, for which we analyze the performance. Moreover, we study the effects on the network throughput of two different schemes, by which the source nodes transmit either a packet to both the destination and the relay in the same timeslot by using wider beams (broadcast scheme) or to only one of these two by using narrower beams (fully directional scheme). Numerical results show how the network throughput varies according to specific system parameters, such as positions and number of nodes. The analysis allows us also to understand the optimal transmission scheme for different network scenarios and shows that the choice to use transmissions with narrow beams does not always represent the best strategy, as wider beams provide a lower beamforming gain, but they allow to transmit simultaneously both at the relay and the destination.
Homocentric Hypersphere Feature Embedding for Person Re-Identification

Triplet loss and softmax loss are two widely used loss functions in Person Re-Identification (Person ReID). However, previous works that try to apply these two loss functions have measure inconsistency during training and testing stage and among different parts of the total loss function, which would cause inferior performance of models. To address this issue, we propose a novel homocentric hypersphere embedding scheme to decouple magnitude and orientation information for both feature and weight vectors, and reformulate the triplet loss and the softmax loss to their angular versions and combine them into an angular discriminative loss. We evaluate our proposed method extensively on the widely used Person ReID benchmarks. Our method demonstrates leading performance on all datasets.
Sexual selection and tail streamers in the barn swallow

The functional significance of elongated, narrow tips of the tail feathers of certain birds, so–called tail streamers, has recently been discussed from an aerodynamic point of view, and the effects of sexual selection on such traits have been questioned. We review our long–term field studies using observational and experimental approaches to investigate natural and sexual selection in the barn swallow, Hirundo rustica, which has sexually size–dimorphic outermost tail feathers. Experimental manipulation of the length of the outermost tail feathers has demonstrated sexual selection advantages of tail elongation and disadvantages of tail shortening, with opposite effects for natural selection in terms of foraging efficiency, haematocrit and survival. These findings are contrary to the prediction of a general deterioration from both shortening and elongation, if the tail trait was determined solely by its effects on aerodynamic efficiency and flight manoeuvrability. Patterns of sexual selection in manipulated birds conform with patterns in unmanipulated birds, and selection differentials for different components of sexual selection in manipulated birds are strongly positively correlated with differentials in unmanipulated birds. Age and sex differences in tail length, and geographical patterns of sexual size dimorphism, are also consistent with sexual selection theory, but inconsistent with a purely natural selection advantage of long outermost tail feathers in male barn swallows.
End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions

This work deals with SciTail, a natural entailment challenge derived from a multi-choice question answering problem. The premises and hypotheses in SciTail were generated with no awareness of each other, and did not specifically aim at the entailment task. This makes it more challenging than other entailment data sets and more directly useful to the end-task – question answering. We propose DEISTE (deep explorations of inter-sentence interactions for textual entailment) for this entailment task. Given word-to-word interactions between the premise-hypothesis pair (P, H), DEISTE consists of: (i) a parameter-dynamic convolution to make important words in P and H play a dominant role in learnt representations; and (ii) a position-aware attentive convolution to encode the representation and position information of the aligned word pairs. Experiments show that DEISTE gets ≈5% improvement over prior state of the art and that the pretrained DEISTE on SciTail generalizes well on RTE-5.
Trellis Coded Modulation for Non-Orthogonal Multiple Access Systems: Design, Challenges, and Opportunities

Capable of achieving massive connectivity and high reliability, the NOMA technique has been well recognized as a potential candidate for 5G mobile communications. Being a key technique of code-domain NOMA, codebook design has drawn great attention due to its potential to improve the coding gain of the system. Trellis coded modulation (TCM) techniques have been considered as an effective approach to optimize the coding gain through signal set expansion without causing bandwidth expansion. In this article, we propose a framework to apply the multi-dimensional TCM (MD-TCM) techniques in code-domain NOMA to jointly design the superposed codewords. Starting with a basic TCM-based NOMA system, we present the design criteria based on which joint multi-layer codeword design for code-domain NOMA is elaborated. Three TCM-based NOMA applications are extended: TCM-based NOMA for MIMO systems, TCM-based NOMA for cooperative relay systems, and TCM-based NOMA for vehicular systems. Open research issues in these scenarios are also discussed.
Opportunities , Challenges and Pathways of Nano-Medicines : A Concise Review Mini Review

The size of nano particles matters in the imaging. There is a need of nanoparticles of the particular size range for EPR (Enhanced Permeation and Retention) effect. The nanoparticles only of a specific range can diffuse through the endothelium of tumor tissues to experience the EPR effect. Generally the nanoparticles larger than 10 nm are preferred to avoid the first pass elimination in the kidneys but smaller than 150 nm to 200 nm are used to avoid clearing by the liver and spleen [8].
Novel Active Disturbance Rejection Control Based on Nested Linear Extended State Observers

In this paper, a Novel Active Disturbance Rejection Control (N-ADRC) strategy is proposed that replaces the Linear Extended State Observer (LESO) used in Conventional ADRC (C-ADRC) with a nested LESO. In the nested LESO, the inner-loop LESO actively estimates and eliminates the generalized disturbance. Increasing the bandwidth improves the estimation accuracy which may tolerate noise and conflict with H/W limitations and the sampling frequency of the system. Therefore, an alternative scenario is offered without increasing the bandwidth of the inner-loop LESO provided that the rate of change of the generalized disturbance estimation error is upper bounded. This was achieved by the placing of an outer-loop LESO in parallel with the inner one that estimates and eliminates the remaining generalized disturbance originating from the inner-loop LESO due to bandwidth limitations. The stability of LESO and nested LESO was investigated using Lyapunov stability analysis. Simulations on uncertain nonlinear single-input-single-output (SISO) system with time-varying exogenous disturbance revealed that the proposed nested LESO could successfully deal with a generalized disturbance in both noisy and noise-free environments, where the Integral Time Absolute Error (ITAE) of the tracking error for the nested LESO was reduced by 69.87% from that of the LESO.
A matrix-free approach to parallel and memory-efficient deformable image registration

We present a novel computational approach to fast and memory-efficient deformable image registration. In the variational registration model, the computation of the objective function derivatives is the computationally most expensive operation, both in terms of runtime and memory requirements. In order to target this bottleneck, we analyze the matrix structure of gradient and Hessian computations for the case of the normalized gradient fields distance measure and curvature regularization. Based on this analysis, we derive equivalent matrix-free closed-form expressions for derivative computations, eliminating the need for storing intermediate results and the costs of sparse matrix arithmetic. This has further benefits: (1) matrix computations can be fully parallelized, (2) memory complexity for derivative computation is reduced from linear to constant, and (3) overall computation times are substantially reduced. In comparison with an optimized matrix-based reference implementation, the CPU implementation achieves speedup factors between 3.1 and 9.7, and we are able to handle substantially higher resolutions. Using a GPU implementation, we achieve an additional speedup factor of up to 9.2. Furthermore, we evaluated the approach on real-world medical datasets. On ten publicly available lung CT images from the DIR-Lab 4DCT dataset, we achieve the best mean landmark error of 0.93 mm compared to other submissions on the DIR-Lab website, with an average runtime of only 9.23 s. Complete non-rigid registration of full-size 3D thorax-abdomen CT volumes from oncological follow-up is achieved in 12.6 s. The experimental results show that the proposed matrix-free algorithm enables the use of variational registration models also in applications which were previously impractical due to memory or runtime restrictions.
IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility

Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform the state of the art work by improving the efficiency in recruiting participants by 30 percent.
Analytical and Experimental Investigation of a Silicon Photonic Two-Stage Mach–Zehnder Delay Interferometer-Type Polarization Beam Splitter

We report the analytical and experimental results of a silicon photonic polarization beam splitter (PBS) consisting of two-stage Mach–Zehnder delay interferometers (MZDIs). The delay length of each MZDI was designed to achieve constructive and destructive interferences for the TE and TM modes simultaneously. First, we have fabricated and estimated a single-stage MZDI-type PBS. Owing to the difference of the in-phase or out-of-phase conditions between TE and TM modes in case of the designed delay length of the MZDI, the center wavelengths of the transmissivity for both the constructive and destructive interference are different for the TE and TM modes. Thus, the polarization extinction ratio (PER) was as small as 5 dB within the C-band. To improve the PER, a two-stage MZDI structure was introduced. In addition, the length of the multimode interferometer type optical splitter was determined to have an optimal splitting ratio of 50:50 for the TM mode for the shortest wavelength within the operating wavelength range. The center wavelength of the MZDIs was shifted to achieve the widest possible bandwidth with a large PER. We have fabricated the silicon photonic PBS to achieve a PER larger than 15 dB. The measurements showed good agreement with the simulations.
Big Data Quantum Support Vector Clustering

Clustering is a complex process in finding the relevant hidden patterns in unlabeled datasets, broadly known as unsupervised learning. Support vector clustering algorithm is a well-known clustering algorithm based on support vector machines and Gaussian kernels. In this paper, we have investigated the support vector clustering algorithm in quantum paradigm. We have developed a quantum algorithm which is based on quantum support vector machine and the quantum kernel (Gaussian kernel and polynomial kernel) formulation. The investigation exhibits approximately exponential speed up in the quantum version with respect to the classical counterpart.
Internet of Things Framework for Home Care Systems

The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency.
From single-user architectural design to PAC*: a generic software architecture model for CSCW

This article nqmts our tiection on softsvam amhiteetm modelling for multi-user systems (or groupware). Fi~ w introduce t.k notion of software architecture and make explieit the design steps that most sofl-wam designm in HCI tend to blend in a fizzy way. Building on general concepts and pmetiee fmm main stream soikm engineering, we then present a comparative analysis of the most signifhnt architecture models developed for singleand multi-user systems. We close with the pmentation d PAC*, a new arehitectuml fmmwodc for modelling and designing the sdsvare amhitectm of multi-user systems. PAC* is a motivated combination of existing anAiteetuml models seleeted for the complementarily of their “good properties”. These include operational heuristics such as rules for deriving agents in accordance to the task model or criteria for reasoning about replication as well as properties such as support for style heterogeneity, portability, and reusability.