Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Localization and Mapping in Multipath Environments: Mapping and Reusing of Virtual Transmitters

Channel-SLAM is an algorithm which uses multipath propagation for positioning and treats multipath components (MPCs) as line-of-sight (LoS) signals originated from virtual transmitters (VTs). To use the information of the MPCs, Channel-SLAM estimates the receiver position and the position of the VTs simultaneously using simultaneous localization and mapping (SLAM) and does not require any prior information such as room-layout or a database for fingerprinting. This paper investigates mapping, where we derive a probabilistic map representation based on the receiver positions. Thus, if the receiver knows its current location, the information in the probabilistic map helps to estimate the trajectory of further receiver movement. Similar to SLAM approaches, we estimate, map and reuse VT positions in this paper. The algorithm is evaluated based on measurements in an indoor scenario with one fixed transmitter and a moving receiver. We show that indoor positioning is possible with only one transmitter when MPCs are used.
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds: Supplementary Material

This document provides ( A ) a supplementary background on Riemannian manifolds, ( B ) a proof that the distance between two points on a nested hypersphere manifold is independent w.r. t. the parameters { r D , . r d +1 } , ( C ) an approximation of the SPD distance for the mGP kernel, ( D ) the algorithm for trust region on Riemannian manifolds used for optimizing acquisition functions in HD-GaBO, ( E ) the equations of the benchmark functions used in the experiments of the main paper
Numerical analysis of the influence of an actively controlled spoiler on the handling of a sports car

Normal force distribution among tires significantly affects the performance of the vehicle, especially in terms of handling and safety. Consequently, evolving technologies for controlling and regulating normal loads have the potential of boosting the controllability of the vehicle. This paper illustrates the effectiveness of the actively controlled aerodynamic system to increase the performance and the safety margins of a sports car in cornering maneuvers; normal loads on the front and rear axles at high speed are controlled by changing the angle of attack of the front and rear spoilers in opposite directions to alter load distribution. The performance of the controlled system is analyzed through a nonlinear 14-degree-of-freedom vehicle model. Numerical simulations are carried out to understand how the control logic can affect oversteer/understeer response, improving both stability and performance of the vehicle. Robustness of the control systems toward the variation of friction coefficient is also assessed.
PENGEMBANGAN CRACKERS DENGAN PENAMBAHAN TEPUNG IKAN PATIN [Pangasius hypophthalmus] DAN TEPUNG WORTEL [Daucus carota L.]

ABSTRACT Diversification of food needs to be done to improve people's eating habits and overcome the main nutritional problems in Indonesia. Striped Fish and carrot flour is good foodstuffs to add to the crackers rich in nutrition especially protein and give a natural compelling color. To know the effect of the addition of striped Fish and carrot flour to the sensory qualities and protein content in crackers. Four different crackers were prepared using these formulation: 1) control; 2) the addition 25 g striped catfish and carrot flour; 3) the addition 50 g striped catfish and carrot flour; and 4) the addition 100 g striped catfish and carrot flour. The preference test was conducted in this research to assess the organoleptic parameters, and analyse the protein content. The sensory evaluation revealed that composite crackers made by adding striped Fish and carrot flour 0, 25g, 50g and 100g had significant differences (p<0.05) in colour, texture, odour and flavor of sensory parameters. Crackers made with 25g striped catfish and carrot flour is the most accepted by panelis with Score Card evaluation method 3,56 out of 5. This formulation had 16.19 percent protein content. Striped Fish and carrot with a correct formulation could improve the sensory quality and protein content of crackers, so that can be good ingredients to improve food products. ABSTRAK Diversifikasi pangan perlu dilakukan untuk memperbaiki kebiasaan makan masyarakat dan mengatasi masalah gizi utama di Indonesia. Tepung Ikan patin dan tepung wortel yang ditambahkan pada crackers diharapkan dapat meningkatkan zat gizi terutama protein dan memberikan warna yang menarik; Tujuan penelitian untuk mengetahui pengaruh penambahan tepung ikan patin dan wortel terhadap kualitas sensorik dan kadar protein crackers. Empat biskuit berbeda disiapkan menggunakan formulasi berikut: 1) kontrol; 2) penambahan tepung ikan patin dan tepung wortel masing-masing 25 g; 3) penambahan tepung ikan patin dan tepung wortel masing-masing 50 g; 4) penambahan tepung ikan patin dan tepung wortel masing-masing 100 g. Uji preferensi dilakukan dalam penelitian ini untuk menilai parameter organoleptik, dan kandungan protein. Evaluasi sensorik menunjukkan bahwa crackers dengan penambahan tepung ikan patin dan tepung wortel masing-masing 0, 25g, 50g dan 100g memiliki perbedaan yang signifikan (p <0,05) dalam parameter sensori warna, tekstur, aroma dan rasa. Crackers dibuat dengan penambahan tepung ikan patin dan tepung wortel masing-masing 25g adalah yang paling banyak disukai oleh panelis menggunakan evaluasi metode Score Card yaitu dengan nilai 3,56 dari 5. Formulasi ini memiliki kandungan protein sebesar 16,19 persen. Tepung ikan patin dan wortel dengan formulasi yang tepat dapat meningkatkan kualitas sensorik dan kandungan protein crackers sehingga bisa menjadi komponen bahan pangan yang baik untuk memperbaiki produk makanan.
A Geostatistical Approach to Predict the Average Annual Rainfall of Bangladesh

In this paper we tried to fit a predictive model for the average annual rainfall of Bangladesh through a geostatistical approach. From geostatistical point of view, we studied the spatial dependence pattern of average annual rainfall data (measured in mm) collected from 246 stations of Bangladesh. We have employed kriging or spatial interpolation for rainfall data. The data reveals a linear trend when investigated, so by fitting a linear model we tried to remove the trend and, then we used the trend-free data for further calculations. Four theoretical semivariogram models Exponential, Spherical, Gaussian and Matern were used to explain the spatial variation among the average annual rainfall. These models are chosen according to the pattern of empirical semivariogram. The prediction performance of Ordinary kriging with these four fitted models are then compared through ����-fold cross-validation and it is found that Ordinary Kriging performs better when the spatial dependency in average annual rainfall of Bangladesh is modeled through Gaussian semivariogram model.
A novel metal flow imaging using electrical capacitance tomography

The measurement of gas-liquid metal two phase flow is a challenging task due to the opaqueness and the high temperatures. For instance, during continuous casting of steel the distribution of argon gas and liquid steel in the submerged entry nozzle is of high interest, since it influences the quality of the produced steel. In this paper we present the results of a feasibility study for applying the electrical capacitance tomography (ECT) to detect the outer surface of a liquid metal stream. The results of this study are the basis for the development of a new contactless sensor which should be able to detect the outer shape of a liquid metal jet using ECT and the bubbles inside the jet at the same time with mutual inductance tomography.
Fabrication and assembling of a microfluidic optical stretcher polymeric chip combining femtosecond laser and micro injection molding technologies

Microfluidic optical stretchers are valuable optofluidic devices for studying single cell mechanical properties. These usually consist of a single microfluidic channel where cells, with dimensions ranging from 5 to 20 μm are trapped and manipulated through optical forces induced by two counter-propagating laser beams. Recently, monolithic optical stretchers have been directly fabricated in fused silica by femtosecond laser micromachining (FLM). Such a technology allows writing in a single step in the substrate volume both the microfluidic channel and the optical waveguides with a high degree of precision and flexibility. However, this method is very slow and cannot be applied to cheaper materials like polymers. Therefore, novel technological platforms are needed to boost the production of such devices on a mass scale. In this work, we propose integration of FLM with micro-injection moulding (μIM) as a novel route towards the cost-effective and flexible manufacturing of polymeric Lab-on-a-Chip (LOC) devices. In particular, we have fabricated and assembled a polymethylmethacrylate (PMMA) microfluidic optical stretcher by exploiting firstly FLM to manufacture a metallic mould prototype with reconfigurable inserts. Afterwards, such mould was employed for the production, through μIM, of the two PMMA thin plates composing the device. The microchannel with reservoirs and lodgings for the optical fibers delivering the laser radiation for cell trapping were reproduced on one plate, while the other included access holes to the channel. The device was assembled by direct fs-laser welding, ensuring sealing of the channel and avoiding thermal deformation and/or contamination.
Extraction of acoustic features based on auditory spike code and its application to music genre classification

A new method of extracting acoustic features based on auditory spike code is proposed. An auditory spike code represents the acoustic activities created by the signal, similar to sound encoding of the human auditory system. In the proposed method, an auditory spike code of the signal is computed using a 64-band Gammatone filterbank as the kernel functions. Then, for each spectral band, the sum and non-zero counts of the auditory spike code are determined, and the features corresponding to the population and occurrence rate of the acoustic activities for each band are computed. In addition, the distribution of the acoustic activities on a time axis is analysed based on the histogram of time intervals between the adjacent acoustic activities, and the features for expressing temporal properties of the signal are extracted. The reconstruction accuracy of the auditory spike code is also measured as the features. Different from most conventional features obtained by complex statistical modelling or learning, the features by the proposed method can directly show specific acoustic characteristics contained in the signal. These features are applied to a music genre classification, and it is confirmed that they provide a performance comparable to state-of-the-art features.
Electron spectrum of intercalated stage ordered layered structures: Periodic Anderson model approach

Influence of intercalation on the electronic band structure of the layered nanohybrid compound of the GaSe-type with a stage ordering (three layers in the packet in the considered case) is studied in the modified version of the periodic Anderson model. Density of electron states for the intercalated system is calculated both in the impurity single-level approximation and the one with the level smearing out (of the Lorentzian-type) due to local electron correlations. Intercalated particles form an additional band (usually placed near the bottom of the main band) like the narrow impurity band (being far enough from the main band) or the more extended band hybridized with the main one (for the case of overlapping). The most pronounced transformation of the main band takes place in the vicinity of the impurity level. Changes in the total density of electron states due to the broadening of impurity levels and the increase of the intercalant concentration are analyzed.
Sustaining Social and Sociomathematical Norms with Prospective Elementary Teachers in a Mathematics Content Course

Abstract As students contribute to social and sociomathematical norms, they re-organize their own understandings. As such, the purpose of this article is to describe the ways in which prospective elementary teachers contributed to the norms that were established and re-established throughout an entire semester in an undergraduate mathematics content course. Findings illustrate that norms must be renegotiated between an instructor and prospective teachers when the mathematics content changes.
Beyond equilibrium : Re-evaluating physical modelling of fluvial systems to 1 represent climate changes 2

6 1 Geography and Geology, School of Environmental Sciences, Faculty of Science and Engineering, 7 University of Hull, Hull, HU6 7RX, UK. 8 2 Department of Civil and Environmental Engineering, Norwegian University of Science and 9 Technology, Trondheim, Norway. 10 3 Marine and Coastal Systems, Department Ecology and Sediment Dynamics, Deltares, 11 Boussinesqweg 1, Delft, the Netherlands. 12 4 Department of Geography, Loughborough University, Loughborough, LE11 3TU, UK. 13 5 Forschungszentrum Küste, Leibniz Universität Hannover/TU Braunschweig, Hannover, Germany. 14 6 Institut de Mecanique des Fluides de Toulouse (IMFT), Université de Toulouse, CNRS15 INPT-UPS, Toulouse, France. 16 7 Leichtweiß Institute for Hydraulic Engineering and Water Resources, Technische Universität 17 Braunschweig, Braunschweig, Germany 18 19 * Now at: Univ Rennes, CNRS, Géosciences Rennes UMR 6118, 35000 Rennes, France 20 + Now at: Antea Group Belgium, Roderveldlaan 1, 2600 Antwerp, Belgium 21
Introduction to Information Retrieval: Evaluation in information retrieval

We have seen in the preceding chapters many alternatives in designing an information retrieval (IR) system. How do we know which of these techniques are effective in which applications? Should we use stop lists? Should we stem? Should we use inverse document frequency weighting? IR has developed as a highly empirical discipline, requiring careful and thorough evaluation to demonstrate the superior performance of novel techniques on representative document collections. In this chapter, we begin with a discussion of measuring the effectiveness of IR systems (Section 8.1) and the test collections that are most often used for this purpose (Section 8.2). We then present the straightforward notion of relevant and nonrelevant documents and the formal evaluation methodology that has been developed for evaluating unranked retrieval results (Section 8.3). This includes explaining the kinds of evaluation measures that are standardly used for document retrieval and related tasks like text classification and why they are appropriate. We then extend these notions and develop further measures for evaluating ranked retrieval results (Section 8.4) and discuss developing reliable and informative test collections (Section 8.5). We then step back to introduce the notion of user utility, and how it is approximated by the use of document relevance (Section 8.6). The key utility measure is user happiness. Speed of response and the size of the index are factors in user happiness.
Evaluation of human-scale motion energy harvesting for wearable electronics

We explore the potential of human-scale motion energy harvesting toward enabling self-powered wearable electronic components to avoid the burden of battery replacement and charging in next-generation wireless applications. The focus in this work is piezoelectric transduction for converting human motion into electricity. Specifically, we explore three piezoelectric energy harvesting approaches experimentally and numerically: (1) Direct base excitation of a cantilevered bimorph configuration without/with a tip mass; (2) plucking of a bimorph cantilever using a flexible/nonlinear plectrum; and (3) direct force excitation of a curved unimorph. In all cases, electromechanical models are developed and experimental validations are also presented. Specifically a nonlinear plectrum model is implemented for the plucking energy harvester. Average power outputs are on the order 10-100 uW and can easily exceed mW in certain cases via design optimization.
Non-Monotonic Dependencies of Cloud Microphysics and Precipitation on Aerosol Loading in Deep Convective Clouds: A Case Study Using the WRF Model with Bin Microphysics

Aerosol-cloud-precipitation interactions in deep convective clouds are investigated through numerical simulations of a heavy precipitation event over South Korea on 15–16 July 2017. The Weather Research and Forecasting model with a bin microphysics scheme is used, and various aerosol number concentrations in the range N0 = 50–12,800 cm−3 are considered. Precipitation amount changes non-monotonically with increasing aerosol loading, with a maximum near a moderate aerosol loading (N0 = 800 cm−3). Up to this optimal value, an increase in aerosol number concentration results in a greater quantity of small droplets formed by nucleation, increasing the number of ice crystals. Ice crystals grow into snow particles through deposition and riming, leading to enhanced melting and precipitation. Beyond the optimal value, a greater aerosol loading enhances generation of ice crystals while the overall growth of ice hydrometeors through deposition stagnates. Subsequently, the riming rate decreases because of the smaller size of snow particles and supercooled drops, leading to a decrease in ice melting and a slight suppression of precipitation. As aerosol loading increases, cold pool and low-level convergence strengthen monotonically, but cloud development is more strongly affected by latent heating and convection within the system that is non-monotonically reinforced.
Under review as a conference paper at ICLR 2016 Train Connectivity Prune Connections Train Weights Cluster the Weights Generate Code Book

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce “deep compression”, a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9× to 13×; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35×, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49× from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. This also makes it possible to fit neural networks into mobile apps given size limit.
A particle filter motion prediction algorithm based on an autoregressive model for real-time MRI-guided radiotherapy of lung cancer

This work introduces a 2D motion prediction algorithm for lung tumors applied to dynamic MR images that combines intrafractional tumor deformations. It is developed in the context of MR-linac treatments and evaluates uniform and patient-specific margins about the gross tumor volume to optimize the tumor coverage. Seven early stage non-small cell lung cancer patients were imaged in treatment position with a 1.5 T magnetic resonance using a balanced steady-state free precession sequence for one minute at a rate of four images per second and were instructed to breathe normally. The particle filter, in combination with the autoregressive model, is used to sequentially track and predict the tumor position 250 ms in the future from the current image. In addition, the autocontour extracted from a previous study (Bourque et al 2016 Med. Phys. 43 5161–9) is projected to the predicted position and various margins are evaluated. Averaged over all patients, the root mean square errors are (1.3 ± 0.5) mm and (2.0 ± 0.8) mm with and without prediction, respectively, and the difference in centroid position is (1.1 ± 0.4) mm with the prediction. The addition of the prediction algorithm leads to inferior errors for all cases. With such predictor, enlarging the propagated contour by a uniform 2 mm margin leads to a minimum recall of 97% over the entire patient population. Considering patient-specific margins based on 2 σ of the Gaussian distribution around the mean error, the margins vary from 0.8 to 3.0 mm in anterior–posterior direction and from 1.2 to 3.2 mm in the superior–inferior direction. This study concludes that the advantage of such prediction algorithm highly depends on the tumor motion characteristics. For both uniform and patient-specific margins evaluation, smaller treatment margins could be used when combined to an accurate tracking and motion prediction algorithm. These results could offer guidance for future MR-guided lung tumor treatments.
Cardiac alternans reduction by chaotic attractors in T ± ϵ feedback control

To achieve control in dynamical systems, one usually avoids the generation of chaos. However, an effective control of an unstable dynamical system can also be practically achieved by deliberately inducing chaos in the system if the induced chaotic attractors are confined in a very small region in phase space. Using nonlinear dynamic analysis of the cardiac restitution model, we reveal that the mechanism of the recently proposed T ± ϵ control for cardiac alternans suppression is due to the confined chaotic attractors so generated. These results are confirmed experimentally by detailed phase-space portrait and transition dynamics measurements in whole heart experiments.
Study on Adiabatic Temperature Rise Reflecting Hydration Degree of Concrete

The thermal model and the relevant parameters of concrete are the most important issues to study the space-time characteristics of temperature field, which are also the theoretical foundation of temperature control and crack prevention for the mass concrete structures. In this research, the improved adiabatic temperature rise test is carried out, and the temperature variation of fly ash concrete is analyzed. Furthermore, a thermal model of concrete considering the hydration degree is established based on the existing achievements. Meanwhile, the thermal conductivity and specific heat of concrete are measured via three approaches: by treating the parameters as constant values, by computing the parameters as variables of the degree of hydration, and by back-analyzing the parameters through BP neural network. Finally, the thermal parameters determined by different methodologies are substituted into the thermal model, respectively, and the finite element analysis of the concrete specimen is performed. By comparing simulated temperatures with various measured results, it can be found that the numerical analysis results of parameters calculated by BP neural network are closest to the measured values in the whole curing ages. Therefore, BP neural network method is an effective way to calculate the thermal parameters, and BP inversion algorithm provides a new way for accurately study the temperature profile of mass concrete structures.