Miracle-dz/UniFAR-FTUs huggingface dataset:
Fair Coresets and Streaming Algorithms for Fair k-Means Clustering

We study fair clustering problems as proposed by Chierichetti et al. (NIPS 2017). Here, points have a sensitive attribute and all clusters in the solution are required to be balanced with respect to it (to counteract any form of data-inherent bias). Previous algorithms for fair clustering do not scale well. We show how to model and compute so-called coresets for fair clustering problems, which can be used to significantly reduce the input data size. We prove that the coresets are composable and show how to compute them in a streaming setting. Furthermore, we propose a variant of Lloyd's algorithm that computes fair clusterings and extend it to a fair k-means++ clustering algorithm. We implement these algorithms and provide empirical evidence that the combination of our approximation algorithms and the coreset construction yields a scalable algorithm for fair k-means clustering.
Explanations from Deep Reinforcement Learning Using Episodic Memories

Deep Reinforcement Learning (RL) involves the use of Deep Neural Networks (DNNs) to make sequential decisions in order to maximize reward. For many tasks the resulting sequence of actions produced by a Deep RL policy can be long and difficult to understand for humans. A crucial component of human explanations is selectivity, whereby only key decisions and causes are recounted. Imbuing Deep RL agents with such an ability would make their resulting policies easier to understand from a human perspective and generate a concise set of instructions to aid the learning of future agents. To this end we use a Deep RL agent with an episodic memory system to identify and recount key decisions during policy execution. We show that these decisions form a short, human readable explanation that can also be used to speed up the learning of naive Deep RL agents.
The natural structure of scattering amplitudes

In this thesis we present all the techniques needed for the functional reconstruction of univariate rational functions over Finite Fields and apply them to tree-level scattering amplitudes. In particular this procedure using the BCFW recursion as black-box algorithm is presented here for the first time. Furthermore, as a second novel result, we discuss some new aspects of the relation among BCFW tree-level recursion and the maximum-cut of multi-loop diagrams.
COARSE GRAINED SIMULATIONS AND TURBULENT MIXING

Mixing of materials by the small scales of turbulent motion is a critical element of many flow systems of interest in engineering, geophysics and astrophysics. Numerical simulation plays a crucial role and turbulent mixing predictability is a major concern. Small-scale resolution requirements focus typically on those of continuum fluid mechanics described by the NavierStokes equations; different requirements are involved depending on the regime considered and on the relative importance of coupled physics such as multi-species diffusion and combustion as determined by Knudsen, Reynolds, Schmidt, Damköhler, and other characteristic non-dimensional numbers. Direct numerical simulation (DNS) resolving all relevant physical space/time scales, is prohibitively expensive in the foreseeable future for most practical flows and regimes of interest at moderate-to-high Re. On the other end of the simulation spectrum are the Reynolds-Averaged Navier-Stokes (RANS) approaches – which focus on statistical moments for an ensemble of realizations and model the turbulent effects. In coarse grained simulation (CGS) large energy containing structures are resolved, smaller structures are spatially filtered out, and unresolved subgrid scale (SGS) effects are modeled; this includes classical LES strategies [1] with explicit closure SGS models, and implicit LES (ILES) [2], relying on SGS modeling implicitly provided by physics capturing numerical algorithms. The CGS strategy of separating resolved / unresolved physics constitutes the viable intermediate approach between DNS and RANS to address practical geometries and multiphysics. Complex turbulent flow applications unavoidably involve under-resolved simulations, and robustness of CGS predictions is a frequently unsettled issue. The validity of the scale separation assumptions in CGS needs to be carefully tested when potentially important SGS flow physics is involved, specifically, for turbulent wall bounded flows, and for turbulent material mixing – the focus area of the proposed mini-symposia. We examine our understanding of coarse-grained turbulent mixing, its theoretical basis, verification, validation, and predictability aspects, and progress addressing difficult open issues in complex non-equilibrium applications involving singleas well as multi-phase turbulent flow.
Broad bandwidth piezoelectric energy harvester by a flexible buckled bridge

A piezoelectric vibration energy harvester with a broad bandwidth (>15 Hz) is proposed based on a flexible self-buckled bridge. The polymer sheet, thin metal foil, and thinned piezoelectric ceramic film are bonded to constitute the flexible buckled bridge with the total thickness of 200 μm. Its good flexibility is benefit to obtain low resonant frequency ( 15 Hz) is proposed based on a flexible self-buckled bridge. The polymer sheet, thin metal foil, and thinned piezoelectric ceramic film are bonded to constitute the flexible buckled bridge with the total thickness of 200 μm. Its good flexibility is benefit to obtain low resonant frequency (<200 Hz). A “T” shaped proof mass is used to provide a concentrated force at the center of the bridge for buckling easily and decrease the resonant frequency. The natural buckled arc after the bonding process results in a nonlinear phenomenon at the excited acceleration of 0.25 g. The harvester is capable of achieving a maximum peak open-circuit voltage of 15.18 V and maintaining a bandwidth of 15 Hz (90–105 Hz) at an applied acceleration amplitude of 2.0 g. Meanwhile, the harvester generates the maximum effective power of 0.6 mW and the maximum effective power density of 6.9 mW/cm3 at 2.0 g and 105.3 Hz. The normalized power density reaches 0.36 mW cm−3.
Low-complexity Graph Sampling With Noise and Signal Reconstruction via Neumann Series

Graph sampling addresses the problem of selecting a node subset in a graph to collect samples, so that a $K$-bandlimited signal can be reconstructed with high fidelity. Assuming an independent and identically distributed (i.i.d.) noise model, minimizing the expected mean square error (MMSE) leads to the known A-optimality criterion for graph sampling, which is expensive to compute and difficult to optimize. In this paper, we propose an augmented objective based on Neumann series that well approximates the A-optimal criterion and is amenable to greedy optimization. Specifically, we show that a shifted A-optimal criterion can be equivalently written as a function of an ideal low-pass (LP) graph filter, which in turn can be approximated efficiently via fast graph Fourier transform (FGFT). Minimizing the new objective, we select nodes greedily without large matrix inversions using a matrix inverse lemma. Further, for the dynamic subset sampling case where node availability varies across time, we propose an extended sampling strategy that replaces offline samples one-by-one in the selected set. For signal reconstruction, we propose an accompanied biased signal recovery strategy that reuses the approximated filter from sampling. Experiments show that our reconstruction is more robust to large noise than the least squares (LS) solution, and our sampling strategy far outperforms several existing schemes.
FACULTY OF INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING DEGREE PROGRAMME IN WIRELESS COMMUNICATIONS ENGINEERING

The involvement of low power Internet of Things (IoT) devices in the Wireless Sensor Networks (WSN) allow enhanced autonomous monitoring capability in many application areas. Recently, the principles of edge computing paradigm have been used to cater onsite processing and managing actions in WSNs. However, WSNs deployed in remote sites require human involvement in data collection process since internet accessibility is still limited to population dense areas. Nowadays, researchers propose UAVs for monitoring applications where human involvement is required frequently. In this thesis work, we introduce an edge-based architecture which create end-to-end secure communication between IoT sensors in a remote WSN and central cloud via UAV, which assist the data collection, processing and managing procedures of the remote WSN. Since power is a limited resource, we propose Bluetooth Low Energy (BLE) as the communication media between UAV and sensors in the WSN, where BLE is considered as an ultra-low power radio access technology. To examine the performance of the system model, we have presented a simulation analysis considering three sensor nodes array types that can realize in the practical environment. The impact of BLE data rate, impact of speed of the UAV, impact of distance between adjacent sensors and impact of data generation rate of the sensor node have been analysed to examine the performance of system. Moreover, to observe the practical functionality of the proposed architecture, prototype implementation is presented using commercially available off-the-shelf devices. The prototype of the system is implemented assuming ideal environment.
Learning in Virtual Reality: A Quantitative Study on the Effects of Immersion and Interactivity on Procedural Training

This thesis is based on a graduation internship done at Ordina in Nieuwegein, and studies the effects of immersion and interactivity on procedural training. This was investigated by dividing 44 male and 20 female participants (Mage = 37.9, SDage = 12.4, Range = 21 to 62) over a 2 x 2 design, with interactivity and immersion as between subject variables. A quantitative analysis showed that participants that trained in the VR condition (high immersion, high interactivity), significantly outperformed the other conditions on the practical test, but did not differ significantly on the theoretical test. The higher performance on the practical test can be attributed to the small to medium positive effects of Interest, Involvement, Spatial Presence, and Realism caused by higher Immersion and Interactivity. The negative differences that were found on the theoretical test can be explained by the small detrimental effect of Spatial Presence, due to higher Interaction and Immersion. Future research is encouraged to replicate this study in a real-life setting, to see whether the knowledge gained while training with the VR application transfers to performance on the reallife task, and whether these found effects still hold.
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed meth-ods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deﬁcit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling

The challenge of DA is to use the source classifier to minimize the target classification error E(x,y)∼pt [Ly(Gy(Gf (x)), y)] when target labels are unavailable. Since the existence of domain shift, the source classifier cannot work properly on target samples. To better account for the shift between source and target distributions, density ratio w(x) = pt(x) ps(x) [5,7] can be used as a metric of transferability to measure the discrepancy between domains. Specifically, the shift will be eliminate if and only if w(x) = pt(x) ps(x) = 1. Then, the target classification error can be estimated by the source distribution ps as:
Improved dust acoustic solitary waves in two temperature dust fluids

A theoretical investigation is carried out for contribution of the higher-order nonlinearity to nonlinear dust-acoustic solitary waves (DASWs) in an unmagnetized two types of dust fluids (one cold and the other is hot) in the presence of Bolltzmannian ions and electrons. A KdV equation that contains the lowest-order nonlinearity and dispersion is derived from the lowest order of perturbation and a linear inhomogeneous (KdV-type) equation that accounts for the higher-order nonlinearity and dispersion is obtained. A stationary solution for equations resulting from higher-order perturbation theory has been found using the renormalization method. The effects of hot and cold dust charge grains are found to significantly change the higher-order properties (viz. the amplitude and width) of the DASWs.
Estimating spin diffusion length from spin pumping experiments

Although there has been enormous development in the field of spintronics, it is a challenge to interpret the experimental results and estimate the key parameters e.g., spin diffusion length. While designing functional devices, it creates a severe issue since an inaccurate estimation of one parameter also affects the estimation of other parameters concomitantly. The spin diffusion length of a giant spin-orbit material platinum (Pt) has been reported in literature in a wide range of 0.5 - 14 nm, and it is usually treated as a constant value independent of Pt's thickness. For an accurate estimation of spin diffusion length, noting that circuit theory has been tremendously successful in translating physical equations into circuit elements in organized form, we construct the spin-circuit representation of spin pumping. Thereby it is shown clearly that a thickness-dependent conductivity and spin diffusion length is necessary to simultaneously match the experimental results of effective spin mixing conductance and inverse spin Hall voltage due to spin pumping. Such thickness-dependent spin diffusion length is tantamount to Elliott-Yafet spin relaxation mechanism and it bodes well for transitional metals. It is also shown that this conclusion is not altered when there is a significant interfacial spin memory loss.
Real Analysis: Modern Techniques and Their Applications

This book covers the subject matter that is central to mathematical analysis: measure and integration theory, some point set topology, and rudiments of functional analysis. Also, a number of other topics are developed to illustrate the uses of this core material in important areas of mathematics and to introduce readers to more advanced techniques. Some of the material presented has never appeared outside of advanced monographs and research papers, or been readily available in comparative texts. About 460 exercises, at varying levels of difficulty, give readers practice in working with the ideas presented here.
Wavelength-tunable dissipative pulses from Yb-doped fiber laser with Sagnac filter

We experimentally demonstrate a wavelength-tunable passively mode-locked Yb-doped all-normal-dispersion fiber laser by exploiting a high-birefringence Sagnac fiber loop as a flexible filter. The wavelength of pulses is controlled by properly rotating the polarization controllers in a Sagnac loop. The spectrum of output pulses could be shifted by up to 15 nm at a repetition rate of 18 MHz. Output power of more than 43 mW at pulse durations below 11.4 ps is achieved at the central wavelength of 1035 nm. The pulse temporal frequency and output power change slightly over the tunable range.
Modeling, Simulation, and Results of Their Use in Railway Vehicle Dynamics Studies

This chapter focuses on problems related to building mathematical and numerical models of railway vehicle dynamics and then using these models in the process of vehicle dy‐ namics simulation. Finally, the results of such simulations devoted to selected dynamical problems are presented, highlighting the importance of powerful tools such as both the modeling and the simulation. The dynamical problems selected for the presentation con‐ cern railway vehicle stability and importance of kinematics accuracy for the description of the dynamics. These selected problems focus on the vehicle dynamics in a curved track, both in the circular and transition sections. Type of the chapter should be defined as the review paper, however, based on the authors’ own results in the main.
Parametric Design and Optimization of Magnetic Gears With Differential Evolution Method

Recently, magnetic gears have drawn wide attention due to their inherent merits, such as noncontact operation, high torque density, and intrinsic overload protection. In this paper, an analytical calculation based on magnet magnetomotive force permeance theory is adopted to analyze the modulation effect in a magnetic gear. After that, the effects of some key parameters on magnetic gear performance are investigated, including the air-slot-opening-to-pole-pitch ratio <inline-formula><tex-math notation="LaTeX">$c_0$</tex-math></inline-formula>, the magnet-arc-to-permanent-magnet-pole-pitch ratio <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>, and the permanent magnet thickness <inline-formula><tex-math notation="LaTeX">$h_m$</tex-math></inline-formula>. In particular, three types of steel pole pieces are considered: radial slots, parallel teeth, and parallel slots. A global optimization design is then carried out by using the differential evolution algorithm, and some useful suggestions are derived from the optimal Pareto range. Finally, the determination of outer rotor yoke thickness is discussed considering the iron saturation, and the iron loss distribution is investigated. This paper will be helpful for the design and optimization of a coaxial magnetic gear.
Qualitative investigation of the dynamics of a leading edge control surfaces for MAV applications

Conventional control surfaces mounted on wing trailing edges actuated with commercially available servos have not been able to achieve sufficient control authority and rapidity to keep small MAVs flying straight and level in turbulent flow. Non-conventional leading edge control surfaces are investigated as an alternative actuation solution with the potential to enhance control authority and rapidity. In this study, flow visualization of leading edge control surface revealed that higher deflection rates delayed flow separation and this is expected to enhance control forces. Higher actuation rates produced dominant leading edge vortices and hence a transient lift enhancement over the airfoil. Lift spikes from high rate actuations could be exploited to compensate for the high frequency perturbations from gusts.
Extension of vertex cover and independent set in some classes of graphs and generalizations

We consider extension variants of the classical graph problems Vertex Cover and Independent Set. Given a graph $G=(V,E)$ and a vertex set $U \subseteq V$, it is asked if there exists a minimal vertex cover (resp.\ maximal independent set) $S$ with $U\subseteq S$ (resp.\ $U \supseteq S$). Possibly contradicting intuition, these problems tend to be NP-hard, even in graph classes where the classical problem can be solved in polynomial time. Yet, we exhibit some graph classes where the extension variant remains polynomial-time solvable. We also study the parameterized complexity of these problems, with parameter $|U|$, as well as the optimality of simple exact algorithms under the Exponential-Time Hypothesis. All these complexity considerations are also carried out in very restricted scenarios, be it degree or topological restrictions (bipartite, planar or chordal graphs). This also motivates presenting some explicit branching algorithms for degree-bounded instances. We further discuss the price of extension, measuring the distance of $U$ to the closest set that can be extended, which results in natural optimization problems related to extension problems for which we discuss polynomial-time approximability.
Influence of fine particulate matter on the variation of surface morphologies of conductors subjected to positive DC voltages

A study on the effect of fine particulate matter on the surface morphologies of conductors subjected to high positive dc voltages has been undertaken. A global distribution pattern of particulate matter was observed in the shape of parallel chains consisting of particle agglomerations. Based on a coupling model of electric and force fields, the dynamic progress of particles near conductor surfaces was investigated and the effects of forces exerted on the particles were analyzed. The experimental observations agreed well with calculated results. It was found that the interactions between particles played a critical role in the formation of agglomerations and parallel chains. The surface roughness and the corona discharge intensity of the conductors increased significantly as a result of the variation of surface morphologies.A study on the effect of fine particulate matter on the surface morphologies of conductors subjected to high positive dc voltages has been undertaken. A global distribution pattern of particulate matter was observed in the shape of parallel chains consisting of particle agglomerations. Based on a coupling model of electric and force fields, the dynamic progress of particles near conductor surfaces was investigated and the effects of forces exerted on the particles were analyzed. The experimental observations agreed well with calculated results. It was found that the interactions between particles played a critical role in the formation of agglomerations and parallel chains. The surface roughness and the corona discharge intensity of the conductors increased significantly as a result of the variation of surface morphologies.
Statistica Sinica Preprint No : SS-2016-0434 R 2

This study proposes an empirical martingale simulation (EMS) and an empirical P -martingale simulation (EPMS) price estimators for multi-asset financial derivatives. Under mild assumptions on the payoff functions, the strong consistency and asymptotic normality of the proposed estimators are established. Several simulation scenarios are conducted to investigate the performance of the proposed price estimators under multivariate geometric Brownian motion, multivariate GARCH models, multivariate jump-diffusion models and multivariate stochastic volatility models. Numerical results indicate that the multi-asset EMS and EPMS price estimators are capable of improving the efficiency of their Monte Carlo counterparts. In addition, the asymptotic distribution serves as a persuasive approximation to the finite-sample distribution of the EPMS price estimator, which helps to reduce the computation time of finding confidence intervals for the prices of multi-asset derivatives.