Miracle-dz/UniFAR-FTUs huggingface dataset:
OpenCL on FPGAs for GPU Programmers

Data Parallelism and Kernels Data parallelism is a form of parallelism across multiple processors that is achieved when each processor performs identical tasks on different pieces of distributed data. Data-parallel portions of an algorithm are executed on devices as kernels, which are C functions with some restrictions and a few language extensions. The host launches kernels across a 1D, 2D, or 3D grid of work-items to be processed by the devices. Conceptually, work-items can be thought of as individual processing threads, that each execute the same kernel function. Work-items have a unique index within the grid, and typically compute different portions of the result. Work-items are grouped together into work-groups, which are expected to execute independently from one another.
Revealing perceptual structure through input variation: cross-accent categorization of vowels in five accents of English

This paper characterizes the perceptual structure of vowel systems in five regional accents of English, from Australia (A), New Zealand (Z), London (L), Yorkshire (Y), and Newcastle upon Tyne (N), on the basis of “whole system” vowel categorization experiments. We established patterns of within-accent vowel confusions, and then explored cross-accent perception, assessing how listeners from one accent background categorize vowels from another. Our experimental task required mapping continuous phonetic dimensions to perceptual categories in the absence of phonotactic and lexical cues to vowel identity and socio-indexical information about the talker. Our results show that, without these sources of information, there is uncertainty in vowel categorization, even for native accent vowels, and that this degree of uncertainty increases for unfamiliar accents. The patterns of cross-accent perception largely reflect the accent-specific perceptual structure of the listener, as opposed to adaptations to the stimulus accents. This finding contrasts with the type of active talker adaptation found with tasks offering lexical
Strategies for Addressing Prosopagnosia as a Potential Solution to Facial Deepfake Detection

The detection of deep fakes simulating human faces for potentially malicious motives is a constantly developing and interesting subject. According to prosopagnosia research, certain facial features and how they move can help people with the disorder recognise others. This paper outlines studies in the area of detecting and addressing the effects of prosopagnosia. For the first time, we suggest that the findings of these studies could be applied to the detection of deep fake faces, drawing a link between the facial features and movements most useful in combating the effects of prosopagnosia, with the features most productive for analysis in deep fake facial detection.
Joint Task Offloading and Resource Allocation in Heterogeneous Edge Environments

Mobile edge computing is becoming one of the ubiquitous computing paradigms to support applications requiring low latency and high computing capability. FPGA-based reconfigurable accelerators have high energy efficiency and low latency compared to general-purpose servers. Therefore, it is natural to incorporate reconfigurable accelerators in mobile edge computing systems. This paper formulates and studies the problem of joint task offloading, access point selection, and resource allocation in heterogeneous edge environments for latency minimization. Due to the heterogeneity in edge computing devices and the coupling between offloading, access point selection, and resource allocation decisions, it is challenging to optimize over them simultaneously. We decomposed the proposed problem into two disjoint subproblems and developed algorithms for them. The first subproblem is to jointly determine offloading and computing resource allocation decisions and is NP-hard, where we developed an algorithm based on semidefinite relaxation. The second subproblem is to jointly determine access point selection and communication resource allocation decisions, where we proposed an algorithm with a provable approximation ratio of 2.62. We conducted extensive numerical simulations to evaluate the proposed algorithms. Results highlighted that the proposed algorithms outperformed baselines and were near-optimal over a wide range of settings.
Observations and Predictability of a High-Impact Narrow Cold-Frontal Rainband over Southern California on 2 February 2019

: Short-duration, high-intensity rainfall in Southern California, often associated with narrow cold-frontal rainbands (NCFR), threaten life and property. While the mechanisms that drive NCFRs are relatively well understood, their regional characteristics, speciﬁc contribution to precipitation hazards, and their predictability in the western United States have received little research attention relative to their impact. This manuscript presents observations of NCFR physical processes made during the Atmospheric River Reconnaissance ﬁeld campaign on 2 February 2019 and investigates the predictability of the observed NCFR across spatiotemporal scales and forecast lead time. Dropsonde data collected along transects of an atmospheric river (AR) and its attendant cyclone during rapid cyclogenesis, and radiosonde observations during landfall 24h later, are used to demonstrate that a conﬁguration of the Weather Research and Forecasting (WRF) Model skillfully reproduces the physical processes responsible for the development and maintenance of the impactful NCFR. Ensemble simulations provide quantitative uncertainty information on the representation of these features in numericalweatherpredictionand instillconﬁdence in theutilityof WRFas a forecastguidancetoolforshort-to medium-range prediction of mesoscale precipitation processes in landfalling ARs. This research incorporates novel data and methodologies to improve forecast guidance for NCFRs impacting Southern California. While this study focuses on a single event, the outlined approach to observing and predicting high-impact weather across a range of spatial and temporal scales will support regional water management and hazard mitigation, in general.
Time-optimal quantum control of nonlinear two-level systems

Nonlinear two-level Landau-Zener type equations for systems with relevance for Bose-Einstein condensates and nonlinear optics are considered and the minimal time ${T}_{\mathrm{min}}$ to drive an initial state to a given target state is investigated. Surprisingly, the nonlinearity may be canceled by a time-optimal unconstrained driving and ${T}_{\mathrm{min}}$ becomes independent of the nonlinearity. For constrained and unconstrained driving explicit expressions are derived for ${T}_{\mathrm{min}}$, the optimal driving, and the protocol.
Progress towards a new definition of the kelvin

At its 24th meeting (2011) the General Conference on Weights and Measures noted the CIPM’s intention to express new definitions of the kilogram, ampere, kelvin, and mole in terms of fixed numerical values of the Planck constant, elementary charge, Boltzmann constant, and Avogadro constant, respectively. The changes proposed for the International System of Units will not actually be adopted until the experimental results on the new definitional constants that are proposed have reached a further stage of refinement. This paper provides an overview of the activities and progress of the research groups who are carrying out experiments to determine the Boltzmann constant. The most promising methods, acoustic gas thermometry, dielectric-constant gas thermometry, Johnson noise thermometry and Doppler-broadening thermometry, are reviewed. The prospects for meeting the requirements of the Consultative Committee for Thermometry for a new definition of the kelvin are discussed.
Spectral clustering in eye‐movement researches

Eye tracking is a widely used technology to capture the eye movements of participants completing different tasks. Several eye‐tracking parameters are measured, which later can be used to characterize the gazing pattern of the individuals. Clustering based on the path walked on by the participants may enable the researchers to create clusters based on the unconscious personality and thinking style. Common clustering methods generally are unable to handle path data; hence, new dynamic variables are needed. Spectral clustering can handle these types of data well. Spectral clustering handles clustering as a graph partitioning problem without making specific assumptions on the form of the clusters and uses eigenvectors of matrices derived from the data. This way, data are mapped to a low‐dimensional space, which can be easily clustered. Different food choice tasks were presented, and each of the 149 participants had to choose 1 product of the presented 4 and later from 8 alternatives. A new measure was introduced based on all 3 consecutive points from the fixations, and the areas of the triangles formed by these 3 points were computed. The new eye‐movement index captures the temporal variation and also considers the orientation of the fixation points. Spectral clustering resulted 5 balanced clusters defined by Dunn, Silhouette, and C‐indices. Results were compared to the most widely applied hierarchical and centroid‐based clustering (k‐means) methods. Spectral clustering achieved the best results in clustering indices and cluster sizes proved to be more balanced; hence, it outperforms the commonly used applied hierarchical and k‐means.
Quantum Thermalization and Localization in a Trapped Ion Quantum Simulator

Title of dissertation: QUANTUM THERMALIZATION AND LOCALIZATION IN A TRAPPED ION QUANTUM SIMULATOR Jacob Smith, Doctor of Philosophy, 2016 Dissertation directed by: Professor Christopher Monroe Joint Quantum Institute, University of Maryland Department of Physics and National Institute of Standards and Technology When a system thermalizes it loses all memory of its initial conditions. Even within a closed quantum system, subsystems usually thermalize using the rest of the system as a heat bath. Exceptions to quantum thermalization have been observed, but typically require inherent symmetries or noninteracting particles in the presence of static disorder. The prediction of many-body localization (MBL), in which disordered quantum systems can fail to thermalize despite strong interactions and high excitation energy, was therefore surprising and has attracted considerable theoretical attention. We experimentally generate MBL states by applying an Ising Hamiltonian with long-range interactions and programmably random disorder to ten spins initialized far from equilibrium with respect to the Hamiltonian. Using experimental and numerical methods we observe the essential signatures of MBL: initial state memory retention, Poissonian distributed many-body energy level spacings, and evidence of long-time entanglement growth. Our platform can be scaled to more spins, where detailed modeling of MBL becomes impossible.
Adaptive state estimation for cyber physical systems under sparse attacks

This paper investigates the state estimation problem for cyber physical systems under sparse attacks. Firstly, the fundamental state estimation problem is transferred to an optimization problem with a unique solution. Secondly, an adaptive estimation method for sparse attacks is proposed, which convergence property is well proved. The advantage of proposed method is that the step-size can be adaptively adjusted based on the dynamic estimation errors. Therefore, the computing time is less than some existing methods while guaranteeing the desired performance. Then, a suitable state feedback is designed to improve the computing speed while enhancing the resiliency for the destroyed system. Finally, the speed performance and accuracy of proposed algorithm are verified by two numerical examples.
Sampled‐data control under magnitude and rate saturating actuators

This paper addresses the problems of stability analysis and stabilization of sampled‐data control systems under magnitude and rate saturating actuators. A position‐type feedback modeling for the actuator is considered. Based on the use of a quadratic Lyapunov function, a looped‐functional, and generalized sector relations (to cope with nested saturation functions), LMI‐based conditions are derived to assess local (regional) and global stability of the closed‐loop systems under aperiodic sampling strategies and also to synthesize stabilizing sampled‐data state feedback control laws. These conditions are then incorporated in convex optimization problems aiming at maximizing estimates of the region of attraction of the origin or maximizing the inter‐sampling time for which the stability is ensured regionally or, when possible, globally. Copyright © 2016 John Wiley & Sons, Ltd.
PrivMon: A Stream-Based System for Real-Time Privacy Attack Detection for Machine Learning Models

Machine learning (ML) models can expose the private information of training data when confronted with privacy attacks. Specifically, a malicious user with black-box access to a ML-as-a-service platform can reconstruct the training data (i.e., model inversion attacks) or infer the membership information (i.e., membership inference attacks) simply by querying the ML model. Despite the pressing need for effective defenses against privacy attacks with black-box access, existing approaches have mostly focused on enhancing the robustness of the ML model via modifying the model training process or the model prediction process. These defenses can compromise model utility and require the cooperation of the underlying AI platform (i.e., platform-dependent). These constraints largely limit the real-world applicability of existing defenses. Despite the prevalent focus on improving the model’s robustness, none of the existing works have focused on the continuous protection of already deployed ML models from privacy attacks by detecting privacy leakage in real-time. This defensive task becomes increasingly important given the vast deployment of ML-as-a-service platforms these days. To bridge the gap, we propose PrivMon, a new stream-based system for real-time privacy attack detection for ML models. To facilitate wide applicability and practicality, PrivMon defends black-box ML models against a wide range of privacy attacks in a platform-agnostic fashion: PrivMon only passively monitors model queries without requiring the cooperation of the model owner or the AI platform. Specifically, PrivMon takes as input a stream of ML model queries and provides an efficient attack detection engine that continuously monitors the stream to detect the privacy attack in real-time, by identifying self-similar malicious queries. We show empirically and theoretically that PrivMon can detect a wide range of realistic privacy attacks within a practical time frame and successfully mitigate the attack success rate. Code is available at https://github.com/ruoxi-jia-group/privmon.
End-User Computing Satisfaction Instrument: Assessment of Students’ Satisfaction Using E-Learning in Higher Education

ThisstudyaimstotestthevalidityandreliabilityoftheEnd-UserCom-puting Satisfaction instrument (Content, Accuracy, Format, ease of Use, Timeli-ness, and User Satisfaction) for e-learning users at the Faculty of Engineering, Universitas Negeri Makassar. The research method used in this research is quantitative. This study uses primary data from a Likert scale measurement questionnaire. The questionnaire was distributed to respondents who were students. The questionnaires distributed were 98 questionnaires at the Faculty of Engineering, Universitas Negeri Makassar. From the validity test, it can be concluded that the results of the instrument validation test with product moment on satisfaction with usingthee-learningInformationSystem,thisresearchinstrumentisdeclaredvalid. Allweredeclaredvalid(r-count > r-table). In comparison, the reliability test is used to measure a questionnaire which is an indicator of a variable or reliability if it provides a Cronbach-Alpha ( A ) value of each variable value greater than 0.60. TheresultsshowedthattheEndUserComputingSatisfactioninstrument(Content, Accuracy,Format,easeofUse,Timeliness,usersatisfaction)isfeasible/excellent consistency(perfect)asaresearchmeasuringinstrumenttobeusedrepeatedly.
On the Decentralized Stochastic Gradient Descent With Markov Chain Sampling

The decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems. This paper studies the decentralized Markov chain gradient descent (DMGD), a variant of the decentralized stochastic gradient method, which draws random samples along the trajectory of a Markov chain. DMGD arises when obtaining independent samples is costly or impossible, excluding the use of the traditional stochastic gradient algorithms. Specifically, we consider the DMGD over a connected graph, where each node only communicates with its neighbors by sending and receiving the intermediate results. We establish both ergodic and nonergodic convergence rates of DMGD, which elucidate the critical dependencies on the topology of the graph that connects all nodes and the mixing time of the Markov chain. We further numerically verify the sample efficiency of DMGD.
A SIMPLE ELECTROPHORETIC PROCEDURE DUE TO SHORT DELETIONS FOR FETAL DIAGNOSIS OF P-THALASSAEMIA

SUMMARY This study describes three couples at risk for homozygous 8-thalassaemia in which one of the partners carried a short deletion 8-thalassaemia defect. Detection of short deletions in trophoblast DNA was accomplished by the very simple procedure of non-denaturing polyacrylamide gel electrophoresis. This method may be applied to detect p-thalassaemia mutations due to deletion or addition of more than two nucleotides.
California Fish and Wildlife

Mountain lion (Puma concolor) population management in California has varied widely over the past 100 plus years, ranging from a bounty system (1906-1963) to specially-protected status (1972-present). To elucidate how these different management approaches have influenced California’s mountain lion populations, we estimated historical population trends by combining purposeful (i.e., bounty and depredation) and incidental (i.e., vehicle strike) mortality statistics with estimates of annual growth and mortality rates derived from the literature. We used a backwards population projection method to estimate annual abundance and population trends, starting with population sizes drawn randomly from a uniform distribution ranging from 1,000-5,000. These back-calculations suggest that the bounty was effective at reducing mountain lion populations, as all simulations indicated a statewide population decline during this period. Specially-protected status was also likely effective, as mountain lion populations appear to have increased statewide following cessation of the bounty period. These analyses demonstrate the effectiveness of various management approaches to influence mountain lion population trends for the intended results, and provide context for understanding historical aspects of mountain lion populations in California, which is unique from other areas given the species’ specially-protected status here.
Time dependent collisional radiative model of an extreme ultraviolet driven plasma

The ever increasing demand for smaller and faster computer chips requires an ever decreasing size of semiconductor structures. ASML, one of the world’s leading providers of lithography systems for the manufacturing of chips, aims at using EUV (Extreme UltraViolet) radiation at 13.5 nm wavelength for its next generation of products. Due to high attenuation at this wavelength, the whole optics system must be in vacuum and mirrors have to be used instead of lenses. The pulsed EUV source photoionizes the low pressure ( 1 Pa) background argon gas which causes a weakly ionized plasma. In the plasma sheath region at the mirror surface ions will be accelerated towards the surface and can cause undesired sputtering of the multilayer mirror surface. In a previous Particle In Cell (PIC) model by Van der Velden et al.∗ predictions were made of the plasma parameters. In this research a time-dependent Collisional Radiative Model (CRM) is made based on the corona plasma regime. Using nonMaxwellian electron distribution functions (EEDF) provided by the PIC model the CRM model calculates the argon excited state densities and the spontaneous photon emission rate. The results are compared to Optical Emission Spectroscopy measurements. The experiments show that the timescales on which the electrons in the EUV induced plasma lose their energy, are much shorter than predicted by the model. However, the general trends are predicted correctly. Furthermore, the 4p lines of ArI and ArII prove to be the best indicators of this process. ∗Kinetic simulation of an extreme ultraviolet radiation driven plasma near a multilayer mirror M. H. L. van der Velden, W. J. M. Brok, J. J. A. M. van der Mullen, and V. Banine, J. Appl. Phys. 100, 073303 (2006)
Advanced noise modeling for future propulsion systems

In order to meet noise specifications for future foreseen aircraft propulsion systems, such as for ultrahigh bypass ratio turbofans and contra-rotating open rotors, the dominant turbomachinery noise mechanisms need to be modeled accurately at an early design stage. Two novel methods are presented here, which could significantly improve the existing analytical noise models. For the high-solidity ultrahigh bypass ratio, a mode-matching technique based on a modal expansion of acoustic and vortical variables in each subdomain of a blade row is shown to accurately reproduce sound generation and propagation in two-dimensional bifurcated channels and in three-dimensional annular unstaggered flat-plate cascades. For the low solidity contra-rotating open rotors, several extensions to Amiet’s compressible isolated airfoil theory are coupled with Curle’s and Ffowcs Williams and Hawkings’ acoustic analogy in the frequency domain within a strip theory framework, to yield both far-field tonal and broadband noise. Including sweep in both tonal and broadband noise models is shown to significantly improve the comparison with experiments on a stationary swept airfoil in a uniform turbulent stream and on a realistic contra-rotating open rotor geometry at approach conditions.
A New Unit Root Test against Asymmetric ESTAR Nonlinearity with Smooth Breaks

T his paper proposes a new unit root test against the alternative of symmetric or asymmetric exponential smooth transition autoregressive (AESTAR) nonlinearity that accounts for multiple smooth breaks. We provide small sample properties which indicate the test statistics have good empirical size and power. Also, we compared small sample properties of the test statistics with Christopoulos and Leon-Ledesma (2010) test. The results indicate that our unit root test approach is superior to the test method of Christopoulos and Leon-Ledesma (2010) for both transition parameters (i.e. slow and fast speed), and the test power increases along with the frequency. We apply our test statistics for examining the real interest rate parity hypothesis among OECD countries.
Simultaneous estimation of phase and its pth order derivatives

One unaddressed challenge in optical metrology has been the measurement of higher order derivatives of rough specimens subjected to loading. In this paper, we investigate an approach that allows for the simultaneous estimation of the phase and its higher order derivatives from a noisy interference field. The interference phase is represented as a weighted linear combination of linearly independent Fourier basis functions. The interference field is represented as a state space model with the weights of the basis functions as the elements of the state vector. These weights are accurately estimated by employing the extended Kalman filter. The interference phase and phase derivatives are subsequently computed using the estimated weights. Since the Fourier basis functions are infinitely differentiable, phase derivatives of any arbitrary order can be estimated. Simulation and experimental results are provided to substantiate the effectiveness of the proposed method in the presence of high noise.