434 research outputs found
Redefining Disproportionate Arrest Rates: An Exploratory Quasi-Experiment that Reassesses the Role of Skin Tone
The New York Times reported that Black Lives Matter was the third most-read subject of 2020. These articles brought to the forefront the question of disparity in arrest rates for darker-skinned people. Questioning arrest disparity is understandable because virtually everything known about disproportionate arrest rates has been a guess, and virtually all prior research on disproportionate arrest rates is questionable because of improper benchmarking (the denominator effect). Current research has highlighted the need to switch from demographic data to skin tone data and start over on disproportionate arrest rate research; therefore, this study explored the relationship between skin tone and disproportionate arrest rates. This study also sought to determine which of the three theories surrounding disproportionate arrests is most predictive of disproportionate rates. The current theories are that disproportionate arrests increase as skin tone gets darker (stereotype threat theory), disproportionate rates are different for Black and Brown people (self-categorization theory), or disproportionate rates apply equally across all darker skin colors (social dominance theory). This study used a quantitative exploratory quasi-experimental design using linear spline regression to analyze arrest rates in Alachua County, Florida, before and after the county’s mandate to reduce arrests as much as possible during the COVID-19 pandemic to protect the prison population. The study was exploratory as no previous study has used skin tone analysis to examine arrest disparity. The findings of this study redefines the understanding of the existence and nature of disparities in arrest rates and offer a solid foundation for additional studies about the relationship between disproportionate arrest rates and skin color
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Structural optimization in steel structures, algorithms and applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
A study of electrostatic nuclear fusion devices as a low pressure hollow cathode discharge
Discharge operated inertial electrostatic confinement (IEC) is a method of achieving nuclear fusion using a simple discharge system.
For many years it has been proposed that a spherically gridded cathode placed at the centre of a low pressure DC discharge will converge ions into a fusion core of high energy and density.
The high transparency of the cathode grid allows ions to oscillate within the core so that they are confined there, forming a virtual anode of positive space charge.
In recent years, however, evidence has been building that the exact opposite occurs; ions seem to start at a high density and low energy at the cathode centre and subsequently diverge outwards under acceleration by a virtual anode.
A high rate of internal ionisation and diverging beams of charged particles are instead reminiscent of the hollow cathode effect.
In this thesis we undertake an experimental, computational, and analytical study to prove that discharge IEC devices in fact operate as a hybrid form of abnormal hollow cathode discharge.
A hybrid IEC/hollow cathode consisting of two co-axial rings is analysed using the laser-induced fluorescence (LIF) diagnostic technique.
A higher density of diverging ions is observed compared to converging ions.
This behaviour was replicated by considering the acceleration of ions from a virtual anode at the cathode centre as determined by a computational model of the cathode sheath.
The two-ring discharge is argued to be consistent with a hollow cathode discharge but did not exhibit some aspects of the hollow cathode effect expected to contribute to forming the virtual anode.
An LIF analysis of a cylindrical hollow cathode with solid walls in low pressure IEC discharge conditions was undertaken to clarify the link between each discharge type.
Although failing to observe ion divergence, the fluorescence signal indicated an increase in ion density approaching the cathode while most pre-sheath theory predicts the opposite.
This seemingly..
Virtual Model Building for Multi-Axis Machine Tools Using Field Data
Accurate machine dynamic models are the foundation of many advanced machining technologies such as virtual process planning and machine condition monitoring. Viewing recent designs of modern high-performance machine tools, to enhance the machine versatility and productivity, the machine axis configuration is becoming more complex and diversified, and direct drive motors are more commonly used. Due to the above trends, coupled and nonlinear multibody dynamics in machine tools are gaining more attention. Also, vibration due to limited structural rigidity is an important issue that must be considered simultaneously. Hence, this research aims at building high-fidelity machine dynamic models that are capable of predicting the dynamic responses, such as the tracking error and motor current signals, considering a wide range of dynamic effects such as structural flexibility, inter-axis coupling, and posture-dependency.
Building machine dynamic models via conventional bottom-up approaches may require extensive investigation on every single component. Such approaches are time-consuming or sometimes infeasible for the machine end-users. Alternatively, as the recent trend of Industry 4.0, utilizing data via Computer Numerical Controls (CNCs) and/or non-intrusive sensors to build the machine model is rather favorable for industrial implementation. Thus, the methods proposed in this thesis are top-down model building approaches, utilizing available data from CNCs and/or other auxiliary sensors. The achieved contributions and results of this thesis are summarized below.
As the first contribution, a new modeling and identification technique targeting a closed-loop control system of coupled rigid multi-axis feed drives has been developed. A multi-axis closed-loop control system, including the controller and the electromechanical plant, is described by a multiple-input multiple-output (MIMO) linear time-invariant (LTI) system, coupled with a generalized disturbance input that represents all the nonlinear dynamics. Then, the parameters of the open-loop and closed-loop dynamic models are respectively identified by a strategy that combines linear Least Squares (LS) and constrained global optimization. This strategy strikes a balance between model accuracy and computational efficiency. This proposed method was validated using an industrial 5-axis laser drilling machine and an experimental feed drive, achieving 2.38% and 5.26% root mean square (RMS) prediction error, respectively. Inter-axis coupling effects, i.e., the motion of one axis causing the dynamic responses of another axis, are correctly predicted. Also, the tracking error induced by motor ripple and nonlinear friction is correctly predicted as well.
As the second contribution, the above proposed methodology is extended to also consider structural flexibility, which is a crucial behavior of large-sized industrial 5-axis machine tools. More importantly, structural vibration is nonlinear and posture-dependent due to the nature of a multibody system. In this thesis, prominent cases of flexibility-induced vibrations in a linear feed drive are studied and modeled by lumped mass-spring-damper system. Then, a flexible linear drive coupled with a rotary drive is systematically analyzed. It is found that the case with internal structural vibration between the linear and rotary drives requires an additional motion sensor for the proposed model identification method. This particular case is studied with an experimental setup.
The thesis presents a method to reconstruct such missing internal structural vibration using the data from the embedded encoders as well as a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU) mounted on the machine table. It is achieved by first synchronizing the data, aligning inertial frames, and calibrating mounting misalignments. Finally, the unknown internal vibration is reconstructed by comparing the rigid and flexible machine kinematic models. Due to the measurement limitation of MEMS IMUs and geometric assembly error, the reconstructed angle is unfortunately inaccurate. Nevertheless, the vibratory angular velocity and acceleration are consistently reconstructed, which is sufficient for the identification with reasonable model simplification.
Finally, the reconstructed internal vibration along with the gathered servo data are used to identify the proposed machine dynamic model. Due to the separation of linear and nonlinear dynamics, the vibratory dynamics can be simply considered by adding complex pole pairs into the MIMO LTI system. Experimental validation shows that the identified model is able to predict the dynamic responses of the tracking error and motor force/torque to the input command trajectory and external disturbances, with 2% ~ 6% RMS error. Especially, the vibratory inter-axis coupling effect and posture-dependent effect are accurately depicted.
Overall, this thesis presents a dynamic model-building approach for multi-axis feed drive assemblies. The proposed model is general and can be configured according to the kinematic configuration. The model-building approach only requires the data from the servo system or auxiliary motion sensors, e.g., an IMU, which is non-intrusive and in favor of industrial implementation. Future research includes further investigation of the IMU measurement, geometric error identification, validation using more complicated feed drive system, and applications to the planning and monitoring of 5-axis machining process
Making Presentation Math Computable
This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book
Multi-ciphertext security degradation for lattices
Typical lattice-based cryptosystems are commonly believed to resist multi-target attacks. For example, the New Hope proposal stated that it avoids all-for-the-price-of-one attacks . An ACM CCS 2021 paper from Duman–Hövelmanns–Kiltz–Lyubashevsky–Seiler stated that we can show that Adv_{PKE}^{IND-CPA} ≈ Adv_{PKE}^{(n,q_C)-IND-CPA} for lattice-based schemes such as Kyber, i.e. that one-out-of-many-target IND-CPA is as difficult to break as single-target IND-CPA, assuming the hardness of MLWE as originally defined for the purpose of worst-case to average-case reductions . Meanwhile NIST expressed concern regarding multi-target attacks against non-lattice cryptosystems.
This paper quantifies the asymptotic impact of multiple ciphertexts per public key upon standard analyses of known primal lattice attacks, assuming existing heuristics. The qualitative conclusions are that typical lattice PKEs asymptotically degrade in heuristic multi-ciphertext IND-CPA security as the number of ciphertexts increases. These PKE attacks also imply multi-ciphertext IND-CCA2 attacks against typical constructions of lattice KEMs. Quantitatively, the asymptotic heuristic security degradation is exponential in Θ(n) for decrypting many ciphertexts, cutting a constant fraction out of the total number of bits of security, and exponential in Θ(n/log n) for decrypting one out of many ciphertexts, for conservative cryptosystem parameters.
This shows a contradiction between the existing heuristics and the idea that multi-target security matches single-target security. Also, whether or not the existing heuristics are correct, (1) there are flaws in the claim of an MLWE-based proof of tight multi-target security, and (2) there is a 2^{88}-guess attack breaking one out of 2^{40} ciphertexts for a FrodoKEM-640 public key, disproving FrodoKEM\u27s claim that the FrodoKEM parameter sets comfortably match their target security levels with a large margin
The Shape Dependence of Chameleon Gravity
The chameleon model is a modified gravity theory that introduces an additional scalar field that couples to matter through a conformal coupling. This `chameleon field' possesses a screening mechanism through a nonlinear self-interaction term which allows the field to affect cosmological observables in diffuse environments whilst still being consistent with current local experimental constraints. Due to the self-interaction term, the equations of motion of the field are nonlinear and therefore difficult to solve analytically. The analytic solutions that do exist in the literature are either approximate solutions and or only apply to highly symmetric systems.
In this work I introduce the software package SELCIE (\url{https://github.com/C-Briddon/SELCIE.git}). This package equips the user with tools to construct an arbitrary system of mass distributions and then to calculate the corresponding solution to the chameleon field equation. It accomplishes this by using the finite element method and either the Picard or Newton nonlinear solving methods. I compare the results produced by SELCIE with analytic results from the literature including discrete and continuous density distributions. I find strong (sub-percentage) agreement between the solutions calculated by SELCIE and the analytic solutions.
One consequence of this screening mechanism is that the force induced by the field is dependent on the shape of the source mass (a property that distinguishes it from gravity). Therefore an optimal shape must exist for which the chameleon force is maximised. Such a shape would allow experiments to improve their sensitivity by simply changing the shape of the source mass. In this work I use a combination of genetic algorithms and SELCIE to find shapes that optimise the force at a single point in an idealised experimental environment. I note that the method I use is easily customised, and so can be used to optimise a more realistic experiment involving particle trajectories or the force acting on an extended body. I find the shapes outputted by the genetic algorithm possess common characteristics, such as a preference for smaller source masses, and that the largest fifth forces are produced by small `umbrella'-like shapes with a thickness such that the source is unscreened but the field reaches its minimum inside the source. This remains the optimal shape even as we change the chameleon potential, and the distance from the source, and across a wide range of chameleon parameters. I find that by optimising the shape in this way the fifth force can be increased by times when compared to a sphere, centred at the origin, of the same volume and mass
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