Miracle-dz/UniFAR-FTUs huggingface dataset:
Vulnerable road user detection and orientation estimation for context-aware automated driving

This thesis addresses the detection, segmentation and orientation estimation of persons in visual data. In particular, the aim of this work is to establish an accurate machine representation of the Vulnerable Road Users (VRU, e.g. pedestrians, cyclists) by using image-based cues to support context-aware automated driving. A robust detection of the VRU is achieved by applying efficient stereo-based proposals within region-based Convolutional Neural Networks. Various network and proposal configurations are compared on a newly introduced dataset focusing on the challenging detection of cyclists in urban areas. A pixel-wise segmentation of the detected VRU facilitates higher-level, semantic scene analysis (e.g. pose estimation, activity analysis). Accurate object segmentations are gained by combining statistical shape models with multiple visual data cues within an iterative framework using a Conditional Random Field formulation. Head and body part locations and orientations are jointly estimated from a set of orientation-specific detector responses. The applied Dynamic Bayesian Network model accounts for spatial and temporal anatomical constraints resulting in stable part localization and orientation estimates. The inferred orientations are used to anticipate the behavior of the VRU by modeling situational awareness within a context-based Switching Linear Dynamic System. Experiments show that such context-aware models lead to a significant improvement in VRU path prediction. Since data annotation and management are indispensable components for the development of complex machine learning applications, two software tools are proposed to support an efficient handling of sensor data and annotations.
A direct method to Frobenius norm-based matrix regression

Regression analysis has been widely used for face recognition. This paper mainly discuss the following regularized matrix regression problem: Given a set of k image matrices and an image matrix find such that where are also a set of representation coefficients, λ is the model parameter, and represents the Frobenius norm of matrix A. Yuan and Liao [S.F. Yuan, A.P. Liao, Least squares Hermitian solution of the complex matrix equation AXB + CXD = E with the least norm. J. Frankl. Inst. 351 (2014), pp. 4978–4997] introduced a new product for matrices and vectors, and solved the least squares Hermitian problem of complex matrix equation In this paper, we deeply investigate this product and its relative properties about matrix trace, norm, and determinant. We then provide a direct method to get the close form solution for solving the regularized image matrix regression problem in face recognition.
Visualization of Climate Factors for Sports Events and Activities–The Tokyo 2020 Olympic Games

Detailed climate information in an easily interpretable form is demanded by the general public, as well as by decision-makers on different planning levels. One example is the group of planners in the field of sports and tourism. A promising approach is the visualization of climate thresholds in a Climate-Tourism/Transfer-Information Scheme (CTIS) for the prevailing local climate conditions. The presented approach is adaptable to specific destinations and target activities and integrates meteorological, as well as recreational, touristic and specific activity-related parameters and thresholds. All parameters are simplified in terms of factors and combined in one individual graph, the CTIS diagram. The detailed information on local climate can be applied by non-experts like tourists intending to attend a sports event. They are able to prepare for different aspects of the local climate by, e.g., selecting appropriate clothing when planning their stay. The example of the Tokyo 2020 Olympic Games is presented and discussed. Results show that heat stress together with sultriness is likely to occur during the scheduled time of the Olympics, while cold stress will most probably not be relevant.
Photonic Multiply-Accumulate Operations for Neural Networks

It has long been known that photonic communication can alleviate the data movement bottlenecks that plague conventional microelectronic processors. More recently, there has also been interest in its capabilities to implement low precision linear operations, such as matrix multiplications, fast and efficiently. We characterize the performance of photonic and electronic hardware underlying neural network models using multiply-accumulate operations. First, we investigate the limits of analog electronic crossbar arrays and on-chip photonic linear computing systems. Photonic processors are shown to have advantages in the limit of large processor sizes (<inline-formula><tex-math notation="LaTeX">${>}\text{100}\; \mu$</tex-math></inline-formula>m), large vector sizes (<inline-formula><tex-math notation="LaTeX">$N > 500)$</tex-math></inline-formula>, and low noise precision (<inline-formula><tex-math notation="LaTeX">${\leq} 4$</tex-math></inline-formula> bits). We discuss several proposed tunable photonic MAC systems, and provide a concrete comparison between deep learning and photonic hardware using several empirically-validated device and system models. We show significant potential improvements over digital electronics in energy (<inline-formula><tex-math notation="LaTeX">${>}10^2$</tex-math></inline-formula>), speed (<inline-formula><tex-math notation="LaTeX">${>}10^3$</tex-math></inline-formula>), and compute density (<inline-formula><tex-math notation="LaTeX">${>}10^2$</tex-math></inline-formula>).
Learning progressions to support mathematical reasoning in the middle years: geometric reasoning

The Australian Curriculum: Mathematics calls for the concurrent development of mathematical skills and mathematical reasoning. What are the big ideas of mathematical reasoning and is it possible to map their learning trajectories? Using rich assessment tasks designed for middle-years students of mathematics, this symposium reports on the preliminary phase of a large national study designed to move beyond the hypothetical and to provide an evidence-based foundation for learning progressions in mathematical reasoning in three key areas of the curriculum: Algebraic Reasoning, Geometrical and Spatial Reasoning, and Statistical Reasoning.
Variation of the second person singular of the simple past tense in twitter: hiciste vs. hicistes "you did"

In this study, the ge ographical distribution of the -s added to the simple past tense of indicative in several high frequency verbs is analyzed, i.e. hicistes vs hiciste. For doing so, a geocorpus formed by geolocated tweets has been used. The data obtained suggest that the traditional explanation of addition by analogy with other verb tenses is still valid, especially in rural areas, although it is not the only factor. In urban areas of voseo, as is the case of the Rio de la Plata variety, there is no great difference in the ratio of -s added with the surrounding rural areas, as had been considered in previous studies.
Training Restricted Boltzmann Machine Using Gradient Fixing Based Algorithm

Most of the algorithms for training restricted Boltzmann machines (RBM) are based on Gibbs sampling. When the sampling algorithm is used to calculate the gradient, the sampling gradient is the approximate value of the true gradient and there is a big error between the sampling gradient and the true gradient, which seriously affects the training effect of the network. Aiming at this problem, this paper analysed the numerical error and orientation error between the approximate gradient and the true gradient. Their influence on the performance of network training is given then. An gradient fixing model was established. It was designed to adjust the numerical value and orientation of the approximate gradient and reduce the error. We also designed gradient fixing based Gibbs sampling training algorithm (GFGS) and gradient fixing based parallel tempering algorithm (GFPT), and the comparison experiment of the novel algorithms and the existing algorithms is given. It has been demonstrated that the new algorithms can effectively tackle the issue of gradient error, and can achieve higher training accuracy at a reasonable expense of computational runtime.
Aprendizado de Máquina para Previsão do Tempo de Execução de Aplicações Spark

Aplicações de big data têm associada uma série de problemas desafiadores envolvendo a alocação de recursos de hardware e software. Tipicamente, essas aplicações são conhecidas por serem computacionalmente custosas e por possuírem uma grande heterogeneidade e irregularidades na forma em que operam, tornando desafiadora a tarefa de estimar os tempos de conclusão de resultados. Embora esta seja uma tarefa difícil, ainda assim é possível buscar uma relação entre atributos extraídos da configuração do ambiente de execução e do conjunto de dados de entrada e o tempo total de execução de certas aplicações. Tal relação pode ser utilizada na predição do tempo de execução a partir apenas destes atributos. Este trabalho parte desta premissa e explora modelos de aprendizado de máquina para a previsão de tempos de execução de aplicações na plataforma Spark, popular na área de processamento de dados em larga escala. O trabalho investiga quatro modelos e diferentes atributos, além de comparar os resultados com o estado-da-arte atual. Todos os modelos são avaliados em diversos cenários e configurações, produzindo resultados significativamente melhores que o estado-da-arte em alguns casos.
LOW PROFILE, HIGH GAIN AND WIDEBAND CIRCULARLY POLARIZED ANTENNAS USING HEXAGONAL SHAPE PARASITIC PATCHES

This paper proposes low profile, high gain and wideband circularly polarized (CP) microstrip antennas (MSA), using gap coupled parasitic patches (PPs) on superstrate layer. Printed and suspended probe fed, CP MSAs are designed on a 1.59 mm thick FR4 substrate, and an array of closely spaced hexagonal PPs are printed on the bottom side of the 1.59 mm thick FR4 superstrate and placed at a height about λ0/8 above the ground plane, where λ0 is the free space wavelength, corresponding to the central frequency of the operating frequency band. The gap coupled hexagonal PPs are not only used to enhance the axial ratio bandwidth (AR BW) and gain of the antenna, but also used to reduce impedance and gain variation of the antenna over the operating frequency band. ‘Ant9’ is a suspended MSA with 7 hexagonal PPs. A prototype ‘Ant9’ is fabricated and tested, which provides a peak gain of 9 dBi, S11 < −10 dB, gain variation < 1 dB, and AR < 3 dB over 4.9 to 6.45 GHz frequency band. ARBW of 27.3% is achieved. The proposed ‘Ant9’ covers three frequency bands viz., 5.15 to 5.35 GHz, WLAN band, 5.725 to 5.875 GHz, ISM band, and 5.9 to 6.4 GHz, Satellite C band. The space fed antenna configuration reduces the cross polar radiation level (CPL) and increases the efficiency of the antenna. The measured results agree with the simulation ones. The overall size of ‘Ant9’ is 0. 96λ0 × 0. 96λ0 × 0. 136λ0.
The Stiffness-Threshold Conflict in Polymer Networks and a Resolution

Stiffness and fatigue threshold are important material parameters in load-carrying applications. However, it is impossible to achieve both high stiffness and high threshold for single-network elastomers and single-network hydrogels. As the polymer chain length increases, the stiffness reduces, but the threshold increases. Here we show that this stiffness-threshold conflict is resolved in double-network hydrogels, where the stiffness depends on the short-chain network, but the threshold depends on the longchain network. Experimental data in the literature have shown the stiffness of the hydrogels is inversely proportional to chain length of the short-chain network. In this paper we measure the threshold of PAAm-PAMPS hydrogels with five different chain lengths of the long-chain network. We find that the threshold is proportional to 1/2 power of the chain length of the long-chain network. The resolution of the conflict enables the design of elastomers and hydrogels to achieve both high stiffness and high threshold.
A High-Performance Common-Mode Noise Absorption Circuit Based on Phase Modification Technique

In this article, a high-performance common-mode noise absorption circuit (CMNAC) is proposed for solving electromagnetic interference (EMI) or radio-frequency interference (RFI) problems in high-speed differential digital systems. Instead of reflecting common-mode noises by conventional common-mode filters (CMFs), the common-mode noises can be absorbed in the proposed circuit. In addition, the phase modification technique is utilized to enhance the common-mode noise absorption rate and the differential-mode eye diagram in this CMNAC. In the common-mode half circuit (CMHC), the phase inversion of the subnetwork adds a destructive combination of the stopband energy, resulting in higher stopband attenuation. In the differential-mode half circuit (DMHC), the phase linearization leads to flat group delay, reducing the distortion in high-speed digital signal transmission in the lumped circuit. Using a standard integrated passive device (IPD) process, this CMNAC is implemented for the demonstration. The circuit occupies an area of only 1.138 mm $\times0.822$ mm. From the measured results, the proposed circuit exhibits the common-mode suppression level larger than 15 dB from 4 to 20 GHz and the absorption rate over 90% from 3.8 to 17.4 GHz with a fractional bandwidth 128%. In addition, the measured eye diagram shows that the proposed circuit can support high-speed transmission up to 10 Gb/s.
Decomposition of a Multiscale Entropy Tensor for Sleep Stage Identification in Preterm Infants

Established sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate’s cerebral maturation. Tensor algebra is a powerful tool to analyze multidimensional data and has proven successful in many applications. In this paper, a novel unsupervised algorithm to identify neonatal sleep stages based on the decomposition of a multiscale entropy tensor is presented. The method relies on the difference in electroencephalography(EEG) complexity between the neonatal sleep stages and is evaluated on a dataset of 97 EEG recordings. An average sensitivity, specificity, accuracy and area under the receiver operating characteristic curve of 0.80, 0.79, 0.79 and 0.87 was obtained if the rank of the tensor decomposition is selected based on the age of the infant.
Fast measurements of entanglement over a kilometric distance to test superluminal models of Quantum Mechanics: final results

Quantum mechanics is a non local theory. To recover the locality principle some physicists suggested that the correlations between entangled particles could be established by communications propagating with a velocity vt > c in a preferred frame. In the previous DICE conference we reported the preliminary results of a high sensitivity experiment to test the superluminal models. Here we report the final results. No breaking of the quantum predictions has been found. Then, we infer that the velocity of the quantum communications has to be higher than a lower bound of the order of some millions of times the light velocity.
Cross-Phase Modulation Mitigation in Phase-Sensitive Amplifier Links

We experimentally investigate a wavelength-division multiplexing (WDM) system with a single span of 80 km standard single-mode fiber (SSMF) and a phase-sensitive amplifier (PSA) as a pre-amplifier. Using two channels spaced 25 GHz apart with single-polarization 10-Gbaud quadrature phase-shift keying (QPSK) signal, we show quantitatively that a PSA can mitigate self-phase modulation (SPM) and cross-phase modulation (XPM). With three single-polarization 10-Gbaud QPSK channels spaced 12.5 GHz apart, we verify that PSA can mitigate XPM and also determine an allowed span loss increase of 9.5 dB for the center channel with correlated edge channels for the PSA compared to the phase-insensitive amplifier (PIA) due to the combined improvement from low-noise amplification and nonlinearity mitigation.
Pairwise Error Probability Analysis of Dual Hop Relaying Network over Time Selective Nakagami-m Fading Channel with Imperfect CSI and Node Mobility

Received: 4 March 2019 Accepted: 28 May 2019 We investigate the end-to-end performance of the selective decode and forward (S-DF) relaying network over time selective independent but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channel considering practical communication constraints such as mobile nodes and imperfect channel state information (CSI). The per block average pairwise error probability (PEP) is investigated for both optimal and equal power allocation scenarios. Simulation results have been presented for various values of fading severity parameter and channel variance. The end-to-end performance is examined for various node mobility scenarios and for various antenna configurations at the source, relay and destination nodes. Simulation results confirm the accuracy of the analytical results.
Cryptographic Tools for Privacy Preservation and Verifiable Randomness

Our society revolves around communication. The Internet is the biggest, cheapest and fastest digital communication channel used nowadays. Due to the continuous increase of daily communication among people worldwide, more and more data might be stolen, misused or tampered. We require to protect our communications and data by achieving privacy and confidentiality. Despite the two terms, "privacy" and "confidentiality",are often used as synonymous, in cryptography they are modelled in very different ways. Intuitively, cryptography can be seen as a tool-box in which every scheme, protocol or primitive is a tool that can be used to solve specific problems and provide specific communication security guarantees such as confidentiality. Privacy is instead not easy to describe and capture since it often depends on "which" information is available, "how" are these data used and/or "who" has access to our data. This licentiate thesis raises research questions and proposes solutions related to: the possibility of defining encryption schemes that provide both strong security and privacy guarantees; the importance of designing cryptographic protocols that are compliant with real-life privacy-laws or regulations; and the necessity of defining a post-quantum mechanism to achieve the verifiability of randomness. In more details, the thesis achievements are: (a) defining a new class of encryption schemes, by weakening the correctness property, that achieves Differential Privacy (DP), i.e., a mathematically sound definition of privacy; (b) formalizing a security model for a subset of articles in the European General Data Protection Regulation (GDPR), designing and implementing a cryptographic protocol based on the proposed GDPR-oriented security model, and; (c) proposing a methodology to compile a post-quantum interactive protocol for proving the correct computation of a pseudorandom function into a non-interactive one, yielding a post-quantum mechanism for verifiable randomness.
Effect of fairness on channel choice of the mobile phone supply chain

This paper considers a one-service operator, one-handset manufacturer mobile phone supply chain (MPSC), and examines how the nature and level of both the handset manufacturer’s fairness concerns and the operator’s fairness concerns affect the decision of pricing, subsidizing, and channel choice between an unlocked channel and a bundled channel. Our analysis shows that in most circumstances, the MPSC will choose the bundled channel as long as the price elasticity is higher; however, this property is invalid under certain conditions. Our analysis further shows that the introduction of fairness may have signiﬁcant effects on channel choice decisions. First, the manufacturer’s fairness may hinder the MPSC from choosing the bundled channel when the channel power between the operator and the manufacturer is sufﬁciently asymmetric. Second, when the manufacturer is almost neck-and-neck with the operator, the manufacturer’s fairness concerns may be conducive to the deployment of the bundled channel. Third, the bundled channel would never be a reasonable choice for both the operator and the MPSC when the manufacturer has a relatively powerful status, in particular under the situation when the manufacturer has strong fairness concerns. Fourth, the operator’s fairness concerns are always detrimental to the bundled cooperation which is different from the effect of the manufacturer’s fairness concerns, and the bundled channel would never be a reasonable choice for the MPSC when the operator is extremely powerful.
A Simplified API for the Creation of Bots for Real Time Strategy Games

Artificial Intelligence research in the past few years has been increasingly focusing on games, with Real Time Strategy games being of particular interest. However, one of the main tools used in the creation of agents in these environments has quite a steep learning curve for entry into development, leading to some potential entry barriers that new AI developers have to overcome to get into the field. This project aims to investigate ways in which these entry barriers can be lowered. Particular interest is taken in the potential future application of techniques found in Visual Programming Languages. With this view, a library of tools to help new AI developers is created and tested before being given to members of the Bot Development community to provide their technical assessment feedback, and has been given to new developers with no previous experience with the subject area for their evaluation on how effective these tools were in easing the process of starting out with development. This report describes the research, the planning and testing of this library, and the conclusions drawn from the subsequent evaluation of the tools.
Loci and locality of informational effects on phonetic implementation

Abstract Recent evidence suggests that the phonetic realization of linguistic units is sensitive to informational context. For example, the duration of a word is shorter when it is probable given the following word. Word-specific phonetic variation is unexpected according to modular/feedforward models. We consider various challenges to identifying the loci of informational effects on phonetic implementation – do they arise in production, perception, memory, or some combination? Section 2 addresses a theoretical issue: what are the right measure(s) of predictability/informativity? An urgent direction for future work is to understand what kinds of context matter and why. Section 3 reviews second-mention reduction and other non-local discourse effects, which strongly suggest a production locus (rather than arising in speech perception or memory). Important future directions include modeling discourse/topic in corpus studies, and experimentally assessing the role of nonlocal context in perception and memory. Section 4 addresses the role of computational modeling. We call for integrated, implemented end-to-end models which include speech perception, lexical representation, and speech production components.