Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed-Point Implementation of a Proximal Newton Method for Embedded Model Predictive Control

Abstract Extending the success of model predictive control (MPC) technologies in embedded applications heavily depends on the capability of improving quadratic programming (QP) solvers. Improvements can be done in two directions: better algorithms that reduce the number of arithmetic operations required to compute a solution, and more efficient architectures in terms of speed, power consumption, memory occupancy and cost. This paper proposes an implementation with fixed-point arithmetic of a proximal Newton method to solve optimization problems arising in input-constrained MPC. The main advantages of the algorithm are its fast asymptotic convergence rate and its relatively low computational cost per iteration since it the solution of a small linear system is required. A detailed analysis on the effects of quantization errors is presented, showing the robustness of the algorithm with respect to finite-precision computations. A hardware implementation with specific optimizations to minimize computation times and memory footprint is also described, demonstrating the viability of low-cost, low-power controllers for high-bandwidth MPC applications. The algorithm is shown to be very effective for embedded MPC applications through a number of simulation experiments.
Driving Conditions Recognition Using Heart Rate Variability Indexes

This study presents a physiological recognition strategy based on HRV-parameter-based recognition strategy. The strategy consists of the following processes: 1) feature generation, 2) feature selection, 3) feature extraction, and 4) classifier construction for recognition. In the feature generation processes, the parameter-based strategy calculates features from five-minute HRV analysis results. In the feature selection process, the strategy adopts the best individual N (BIN) as the search strategy and the kernel-based class separability (KBCS) as the selection criterion. Sequentially, principal component analysis (PCA) and linear discriminant analysis (LDA) are adopted in the feature extraction process. Finally, a k-nearest neighbor (k-NN) algorithm is used for the recognition. The feasibility of the recognition strategy is verified by driving condition recognition. The simulation results demonstrate that the proposed strategy can achieve satisfactory recognition rates for recognizing driving conditions. The results show that the feature extraction process or feature selection process has respective physical meaning in the proposed strategies.
Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting

In existing forecasting research papers support vector regression with chaotic mapping function and evolutionary algorithms have shown their advantages in terms of forecasting accuracy improvement. However, for classical particle swarm optimization (PSO) algorithms, trapping in local optima results in an earlier standstill of the particles and lost activities, thus, its core drawback is that eventually it produces low forecasting accuracy. To continue exploring possible improvements of the PSO algorithm, such as expanding the search space, this paper applies quantum mechanics to empower each particle to possess quantum behavior, to enlarge its search space, then, to improve the forecasting accuracy. This investigation presents a support vector regression (SVR)-based load forecasting model which hybridizes the chaotic mapping function and quantum particle swarm optimization algorithm with a support vector regression model, namely the SVRCQPSO (support vector regression with chaotic quantum particle swarm optimization) model, to achieve more accurate forecasting performance. Experimental results indicate that the proposed SVRCQPSO model achieves more accurate forecasting results than other alternatives.
Hawkmoths evaluate scenting flowers with the tip of their proboscis

Pollination by insects is essential to many ecosystems. Previously, we have shown that floral scent is important to mediate pollen transfer between plants (Kessler et al., 2015). Yet, the mechanisms by which pollinators evaluate volatiles of single flowers remained unclear. Here, Nicotiana attenuata plants, in which floral volatiles have been genetically silenced and its hawkmoth pollinator, Manduca sexta, were used in semi-natural tent and wind-tunnel assays to explore the function of floral scent. We found that floral scent functions to increase the fitness of individual flowers not only by increasing detectability but also by enhancing the pollinator's foraging efforts. Combining proboscis choice tests with neurophysiological, anatomical and molecular analyses we show that this effect is governed by newly discovered olfactory neurons on the tip of the moth's proboscis. With the tip of their tongue, pollinators assess the advertisement of individual flowers, an ability essential for maintaining this important ecosystem service. DOI: http://dx.doi. org/10.7554/eLife.15039. 001
Development and preliminary evaluation of an actuated MRI-compatible robotic device for MRI-guided prostate intervention

This paper reports the design, development, and magnetic resonance imaging (MRI) compatibility evaluation of an actuated transrectal prostate robot for MRI-guided intervention. The robot employs an actuated needle guide with the goal of reducing interventional procedure times and increasing needle placement accuracy. The design of the robot, employing piezo-ceramic-motor actuated needle guide positioning and manual needle insertion, is reported. Results of a MRI compatibility study show no reduction of MRI image signal-to-noise-ratio (SNR) with the motors disabled and a 40% to 60% reduction in SNR with the motors enabled. The addition of radio-frequency (RF) shielding is shown to significantly reduce image SNR degradation due to the presence of the robotic device.
Subset kernel principal component analysis

Kernel principal component analysis (kernel PCA or KPCA) has been used widely for non-linear feature extraction, dimensionally reduction, and classification problems. However, KPCA is known to have high computational complexity, that is the eigenvalue decomposition of which size equals to the number of samples n. Moreover, in order to calculate projection of vector onto the subspace obtained by KPCA, we have to store all n samples and evaluate the kernel function n times. In order to overcome these problems, we propose subset KPCA that minimizes a residual error for all samples using limited number of them, and we provide its solution. Experimental results using synthetic and real data show that the proposed method gives almost the same result as KPCA even if the size of the problem is one-tenth of KPCA.
Maximum entropy based tone modeling for mandarin speech recognition

To explore the potential of prosody for Mandarin speech recognition, this paper addresses the tone modeling problem and its integration issue. This study adopts the maximum entropy approach to capture both acoustic and lexical characteristics of tones due to its flexibility in handling multiple interacting features. Moreover, considering the phoneme factor, besides a tone model, a phoneme dependent model is also constructed. With regard to the model integration, the presented models are integrated into the recognizer under the one-pass decoding framework, where they are used to prune the active word-final states during beam search. Experimental results on the HUB-4 evaluation material reveal the effectiveness of the presented models. They significantly improve the performance of speech recognition with 7.6% and 11.1% relative reduction of character error rate.
Portfolio Policies with Stock Options

We study the partial equilibrium portfolio optimization problem for a myopic CRRA investor who can trade options on individual stocks. Applying the parametric portfolio approach of Brandt, Santa-Clara, and Valkanov (forthcoming) to derivatives, we show that options characteristics (such as implied volatility and IV smile skew) convey information about the mispricing in the option portfolios. We take the data on all US-traded options to build characteristic-based factor portfolios of options. An investor uses them in addition to the market portfolio and Fama and French (1992) factors in her utility maximization. Surprisingly, portfolios based on the IV smile skew turn out to be less important than IV-based portfolios, and factor portfolios from call options are in general more interesting for an investor than the factors from puts. Market frictions in the form of stock shortsale constraints are compensated by the use of options, and having options with no stock shortsales allowed may be better than having only stocks with shortsales permitted. Monthly rebalancing leads to extreme transaction costs for an investor facing the full bid-ask spread, providing limits to arbitrage interpretation of the documented mispricing in the option portfolios.
Using mention accessibility to improve coreference resolution

Modern coreference resolution systems require linguistic and general knowledge typically sourced from costly, manually curated resources. Despite their intuitive appeal, results have been mixed. In this work, we instead implement fine-grained surface-level features motivated by cognitive theory. Our novel fine-grained feature specialisation approach significantly improves the performance of a strong baseline, achieving state-of-the-art results of 65.29 and 61.13% on CoNLL-2012 using gold and automatic preprocessing, with system extracted mentions.
Choosing a career in Papua New Guinea: a context of leadership barriers

Nationally significant leadership barriers in association with individual characteristics affecting career choice in Papua New Guinea (PNG) are investigated. A multinomial logistic regression model using leadership barrier variables of Limited Access to High School (LARS), Lack of People Skills (LOPS), and Lack of Vision (LOV) together with the individual characteristics of age, sex, education and experience are investigated. Each variable, except LOPS, are found to be statistically significant in predicting occupational choice in PNG. Occupational choices by males and females are indifferent once higher education levels have been achieved. This novel application of multinomial logistic regression to model the effect of leadership barriers on occupational choice contributes to the ongoing development of PNG and could be applied in developing economies.
An approach to resource-aware co-scheduling for CMPs

We develop real-time scheduling techniques for improving performance and energy for multiprogrammed workloads that scale non-uniformly with increasing thread counts. Multithreaded programs generally deliver higher throughput than single-threaded programs on chip multiprocessors, but performance gains from increasing threads decrease when there is contention for shared resources. We use analytic metrics to derive local search heuristics for creating efficient multiprogrammed, multithreaded workload schedules. Programs are allocated fewer cores than requested, and scheduled to space-share the CMP to improve global throughput. Our holistic approach attempts to co-schedule programs that complement each other with respect to shared resource consumption. We find application co-scheduling for performance and energy in a resource-aware manner achieves better results than solely targeting total throughput or concurrently co-scheduling all programs. Our schedulers improve overall energy delay (E*D) by a factor of 1.5 over time-multiplexed gang scheduling.
Exploring the Efficacy of The Word Within the Word for Gifted and Typically Developing Students

An exploratory study of the efficacy of The Word Within the Word tested students’ abilities to recognize, use, and recall vocabulary. Ten middle school teachers and their 493 students participated. Five teachers used The Word Within the Word, and five used traditional vocabulary materials. Students completed an out-of-level sentence completion test and a test of prompted vocabulary recall. Analysis of sentence completion data revealed significant differences with moderate effect sizes, favoring students in The Word Within the Word sixth- and seventh-grade classes. Analysis of prompted vocabulary recall data revealed significant differences with moderate to large effect sizes at all grade levels, favoring The Word Within the Word classes. Results suggest that in this case, both gifted and typically developing students in classrooms using The Word Within the Word were more skilled in vocabulary recognition, use, and recall than students in classrooms using traditional methods of vocabulary instruction.
Interaction design of social software: Clarifying requirements through a culturally aware artifact

Taking part in a social software is a growing reality worldwide, while the interaction design of social software is still a challenging task due to the lack of third HCI paradigm artifacts, methods, tools and even theories for supporting designers. In this paper we present an artifact for the requirements clarification activity, and suggest some activities in order to guide designers to take cultural issues into account. We situate our discussions in a practical context related to the development of social software for professionals of the Special Education field.
Social support and stressful life events in portuguese multi-problem families

The identification of family risk factors and of contextual factors that protect and promote personal and social resources may contribute to break the cycles of vulnerability of multi-problem families. This study analyzes the psychosocial profile of families at risk in the Algarve (South of Portugal). The participants were 54 mothers, with a mean age of 41.9 years (SD = 6.93). The following instruments were applied: Arizona Social Support Interview Schedule (Barrera, 1980), Appendix to the Arizona Social Support Schedule (Lopez, Menendez, Sanchez, Hidalgo, Lorence, & Jimenez, 2006), Stressful and Risk Events Inventory (Hidalgo, Menendez, Sanchez, Lopez, Jimenez, & Lorence, 2005) and a sociodemographical interview. The results suggest that most stressful events are related to economic and labor problems. Furthermore, the participants reported stressful life events related with conflicts in the mother-child relationship, and the caring for a dependent relative. Mothers reported greater emotional needs than material or informative needs. Also, participants reported as their main source of support their family and friends, and experienced low support from professionals. The families’ needs regarding social support highlighted by the present data are discussed in relation to intervention programmes to be held by professionals.
Nucleation of quark matter bubbles in neutron stars.

The thermal nucleation of quark matter bubbles inside neutron stars is examined for various temperatures which the star may realistically encounter during its lifetime. It is found that for a bag constant less than a critical value a very large part of the star will be converted into the quark phase within a fraction of a second. Depending on the equation of state for neutron star matter and strange quark matter, all or some of the outer parts of the star may subsequently be converted by a slower burning or a detonation.
What are the characteristics of nature preschools in Norway, and how do they organize their daily activities?

ABSTRACT This study provides basic information about how Norwegian nature preschools differ from other Norwegian preschools and how they organize their daily activities. Fifty-six nature preschools and 52 other preschools were included in the study. A total of 106 headmasters and 98 pedagogical leaders filled out questionnaires about the characteristics of their preschool and the characteristics of their preschool’s outdoor activities. Two women and four men from nature preschools were interviewed about organizing a nature preschool. Most nature preschools are private and have less children and staff than other preschools. Nature preschools have more reference areas in nature and visit these areas more frequently than other preschools. Nature preschools spend a large amount of time in nature and have routines and rules that allow the children a significant amount of trust.
Localization of synaptotagmin-binding domains on syntaxin

Synaptotagmin, an abundant calcium- and phospholipid-binding protein of synaptic vesicles, has been proposed to regulate neurotransmitter release at the nerve terminal. To understand better the biochemical mechanism of neurotransmitter release, we have investigated the calcium- dependent and -independent protein-protein interactions between synaptotagmin I and syntaxin 1a, a subunit of the receptor for synaptic vesicles on the presynaptic plasma membrane. Soluble syntaxin 1a binds to synaptotagmin glutathione S-transferase (GST) fusion protein, and the binding was decreased in the presence of calcium. A synaptotagmin fragment containing the second C2 repeat (Syt3–5) had the same binding profile as the whole cytoplasmic domain; however, fragments containing the first C2 repeat (Syt1–3 and Syt2–3) showed calcium-dependent binding to syntaxin. In addition, the soluble full-length cytoplasmic domain of synaptotagmin binds to a syntaxin GST fusion protein in a calcium-dependent manner. Syntaxin domains required for calcium- dependent and -independent synaptotagmin-binding were localized using syntaxin deletion mutants. Amino acids 241–266 of the syntaxin C terminus were required for calcium-independent binding of synaptotagmin. The minimal domain required for calcium-dependent binding of synaptotagmin to syntaxin was localized to amino acids 220– 266. The syntaxin domains required for synaptotagmin binding overlap with the domains for vesicle-associated membrane protein (or VAMP) and alpha-soluble N-ethyl-maleimide-sensitive fusion protein attachment protein (or alphaSNAP) interactions. The data suggest both calcium- dependent and -independent roles of synaptotagmin in regulating synaptic vesicle release and/or recycling.
The Fast Johnson--Lindenstrauss Transform and Approximate Nearest Neighbors

We introduce a new low-distortion embedding of $\ell_2^d$ into $\ell_p^{O(\log n)}$ ($p=1,2$) called the fast Johnson-Lindenstrauss transform (FJLT). The FJLT is faster than standard random projections and just as easy to implement. It is based upon the preconditioning of a sparse projection matrix with a randomized Fourier transform. Sparse random projections are unsuitable for low-distortion embeddings. We overcome this handicap by exploiting the “Heisenberg principle” of the Fourier transform, i.e., its local-global duality. The FJLT can be used to speed up search algorithms based on low-distortion embeddings in $\ell_1$ and $\ell_2$. We consider the case of approximate nearest neighbors in $\ell_2^d$. We provide a faster algorithm using classical projections, which we then speed up further by plugging in the FJLT. We also give a faster algorithm for searching over the hypercube.
The Feature Extraction Based on Nonlinear Partial Orthogonal Analysis with B-Spline Function for Signal of Vibrational Sensors

The new method of feature extraction based on nonlinear partial orthogonal analysis with B-spline function for signal of vibrational sensors is proposed in the paper. Firstly, the nonlinear signal is mapped into linear space; then, the signal is analyzed in the linear space; finally, a set of eigenvalue vectors are obtained with nonlinear partial orthogonal analysis. The vectors reflect a pattern of the signal steadily. The experiment for vibrational signals of normal and abnormal rolling bearings shows that the pattern of every signal is extracted with the method steadily and effectively.
An Improved MAC Protocol for Wireless Sensor Networks

Statistic high efficient-distributed energy-aware node activation protocol (SHE-DEANA) mentioned in this paper is improved from DEANA protocol, which based on the time division multiple access (TDMA) principle in reserving energy and raising efficiency. In the proposed protocol a sensor utilizes its assigned slot only when it is sending or receiving information, otherwise its receiver and transmitter are turned into stand-by mode to avoid unnecessary neighbor listening and power consumption. The sensor nodes are divided into several sub-areas and the presented protocol adapt to the networks which are moderate in density and light in burden. Every node should be assigned different transmission slot so as to meet the needs of data communication. Simulation indicates that using this scheme can reduce power consumption (up to 96%)
Fighting Spam by Breaking the Econonmy of Advertisment by Unsolicited Emails

Unsolicited email (spam) is still a problem for users of the email service. Even though current email anti-spam solutions filter most spam emails, some spam emails still are delivered to the inbox of users. A special class of spam emails advertises websites, e.g., online dating sites or online pharmacies. The success rate of this kind of advertisement is rather low, however, as sending an email does only involve minimal costs, even a very low success rate results in enough revenue such that this kind of advertisement pays off. The anti-spam approach presented in this paper aims on increasing the costs for websites that are advertised by spam emails and on lowering the revenues from spam. Costs can be increased for a website by increasing traffic. Revenues can be decreased by making the website slow responding, hence some business gets lost. To increase costs and decreased revenues a decentralized peer-to-peer coordination mechanism is used to have mail clients to agree on a start date and time for an anti-spam campaign. During a campaign, all clients that received spam emails advertings a website send an opt-out request to this website. A huge number of opt-out requests results in increased traffic to this website and will likely result in a slower responsibility of the website. The coordination mechanism presented in this paper is based on a peer-to-peer mechanisms and a so-called paranoid trust model to avoid manipulation by spammers. An implementation for the Thunderbird email client exist. The anti-spam approach presented in this paper breaks the economy of spam, hence makes advertisement by unsolicited emails unattractive.
Study on Roller-Walker — Improvement of Locomotive Efficiency of Quadruped Robots by Passive Wheels

Abstract Roller-Walker is a leg–wheel hybrid mobile robot using a passive wheel equipped on the tip of each leg. The passive wheel can be transformed into sole mode by rotating the ankle roll joint when Roller-Walker walks on a rough terrain. This paper discusses the energy efficiency of locomotion in wheeled mode. We define a leg trajectory to produce forward straight propulsion, and discuss the relationships between the parameters of the leg trajectory and energy efficiency of the propulsion using a dynamics simulator. We find optimum parameter sets where optimization criterion is specific resistance. The results indicate that faster locomotion achieves higher energy efficiency. We then carry out hardware experiments and empirically derive the experimental specific resistance. We show that wheeled locomotion has an 8-times higher energy efficiency than the ordinary crawl gait. Finally, we compare the specific resistance of Roller-Walker with other walking robots described in the literature.