Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a Remote Instrumental Music Course Focused on Creativity, Interaction, and Bodily Movement. Preliminary Insights and Thematic Analysis

In a newly designed collaborative online music course, four musical novices unknown to each other learned to play the clarinet starting from zero. Over the course of 12 lessons, a special emphasis was placed on creativity, mutual interaction, and bodily movement. Although addressing these dimensions might be particularly challenging in distance learning contexts, a thematic analysis of semi-structured interviews with the learners revealed how the teaching approach proposed has generally facilitated learning. Qualitative findings highlight the importance of establishing meaningful relationships with the musical instrument as well as with other students to build musicality, and of the interplay between creativity and control in individual and collective music-making activities. We suggest that remote music tuition with a small group can be a valuable resource to start learning music and that a creative, collaborative, and movement-based approach can contribute to musical growth.
What Happens When Coups Fail? The Problem of Identifying and Weakening the Enemy Within

The ruler’s ability to cope with crises is critical for authoritarian durability. Yet, the coping mechanism—the actual management strategies by which rulers confront crises—is largely treated as a black box. This study takes a step in addressing this problem by examining how rulers use their appointment powers to manage the crisis that is the aftermath of failed coups. I argue that the principle challenge of this period is that rulers cannot identify the opposition and to cope, they deliberately infuse the center of the regime with officials from the periphery to dilute and ultimately weaken the invisible enemy they confront. Using a novel dataset on the appointments of mid and high level officials over the course of 34 years in Ethiopia, I find that the ruler relied considerably on outside officials following the failed coup in 1960 in ways he never did before or after the event.
Response of centrifugal pendulum vibration absorber considering gravity at low engine speed

This study examined the performance of the centrifugal pendulum vibration absorber’s (CPVA) under the influence of centrifugal force and gravity fields. The focus was on identifying the factor that influenced noise, vibration, and harshness (NVH) problems that originate owing to the centrifugal pendulum absorber being at low engine speed such as engine start-stop or idle. Consequently, mathematical models were established employing Lagrange equation of the second kind, analytical solutions were obtained using the method of multiple scales, and several simple elementary mathematical equations describing the movement of the absorber were derived. Further, the analytical solutions were compared with the numerical simulation results of the complete non-linear equation of motion, which indicated that the former were suitable for studying the performance of CPVA. The theoretical analysis of the application with four absorbers and two absorbers under gravity field was conducted. The absorbers were found to be not always synchronized in the gravity field in certain special conditions such as lower engine speed range or small viscous damping. It was concluded that larger viscous damping coefficient can result in an improvement in the synchronization or reduce the range of nonlinear jumps caused by changes in excitation torque. Further, super-harmonic resonance of CPVA under the gravity field was determined and exact response was calculated, wherein the term “2nd order response gravity impact factor” was defined. In addition, the effects of CPVA installation angle relative to engine ignition angle were analyzed, and a set of tautochronic epicycloid CPVA was tested to verify the findings. The analytical solutions were consistent with the test results, thereby proving the applicability of the research method and results in a vehicle.
Self-Terminating Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing

The Resistive Random-Access-Memory (ReRAM) in crossbar structure has shown great potential in accelerating the vector-matrix multiplication, owing to the fascinating computing complexity reduction (from O(n2) to O(1)). Nevertheless, the ReRAM cells still encounter device programming variation and resistance drifting during computation (known as read disturbance), which significantly hamper its analog computing precision. Inspired by prior precise memory programming works, we propose a Self-Terminating Write (STW) circuit for Multi-Level Cell (MLC) ReRAM. In order to minimize the area overhead, the design heavily reuses inherent computing peripherals (e.g., Analog-to-Digital Converter and Trans-Impedance Amplifier) in conventional dot-product engine. Thanks to the fast and precise programming capability of our design, the ReRAM cell can possess 4 linear distributed conductance levels, with minimum latency used for intermediate resistance refreshing. Our comprehensive cross-layer (device/circuit/architecture) simulation indicates that the proposed MLC STW scheme can effectively obtain 2-bit precision via a single programming pulse. Besides, our design outperforms the prior write&verify schemes by 4.7× and 2× in programming latency and energy, respectively.
The Application of Series-Connected SiC MOSFETs in Buck Converter Based on Active Driving Signal Delay Control for Voltage Balancing

For voltage balancing of series-connected silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET), adjusting the delay time of the device's drive signals is an effective method, but most of the current studies focuses on the voltage balancing itself. However, the purpose of studying the voltage balancing of devices is for its application, which is more complicated because it has not only the voltage balancing control but also the control for the converter. This paper introduces the series-connected SiC MOSFETs based on active delay voltage balancing into Buck converter, and studies the methods of coordination, control, and modeling. This system works well for Buck converter's operation and series-connected SiC MOSFETs' voltage balancing at the same time. Experimental results are presented to verify the practicability and flexibility of this system.
XANDAR: Exploiting the X-by-Construction Paradigm in Model-based Development of Safety-critical Systems

Realizing desired properties “by construction” is a highly appealing goal in the design of safety-critical embedded systems. As verification and validation tasks in this domain are often both challenging and time-consuming, the by-construction paradigm is a promising solution to increase design productivity and reduce design errors. In the XANDAR project, partners from industry and academia develop a toolchain that will advance current development processes by employing a modelbased X-by-Construction (XbC) approach. XANDAR defines a development process, metamodel extensions, a library of safety and security patterns, and investigates many further techniques for design automation, verification, and validation. The developed toolchain will use a hypervisor-based platform, targeting future centralized, AI-capable high-performance embedded processing systems. It is co-developed and validated in both an avionics use case for situation perception and pilot assistance as well as an automotive use case for autonomous driving.
Simulation of Shake Table Tests on Out-of-Plane Masonry Buildings. Part (V): Discrete Element Approach

ABSTRACT The analysis of the shaking table test of a 3-wall stone masonry structure performed with a discrete element model is presented. The numerical model, created with the code 3DEC, employed a rigid block representation and a Mohr-Coulomb joint model. Joint stiffness calibration to match the experimental natural frequencies is discussed, as well as the boundary conditions to simulate the shake table. Comparisons are made with the measured displacements at key locations, and the modes of deformation and fracture of the walls. The DEM model was able to reproduce important features of the shaking table tests. The experimental deformation and near collapse patterns were clearly identifiable in the numerical simulations, which produced displacements within the observed orders of magnitude, for the various levels of excitation.
Creation of Visual Culture in Seoul Business Signboards: The Seoul Good Sign Contest, 2009–2013

Abstract Signboards are one of the most ubiquitous features of urban cityscapes. Signboards embody a system of meanings in which messages are woven together and layered with the history and cultures of the people who produce and use the messages. This paper documents contemporary visual culture in Seoul through examination of a juried group of awarded business signboards that are products of socio-cultural and politico-economic production, consumption and reproduction. This study has also discovered an emergence of professionalism in which artists, designers and manufacturers have contributed to producing and legitimizing culturally specific and aesthetically sound signboards in Seoul.
HyperSpike: HyperDimensional Computing for More Efficient and Robust Spiking Neural Networks

Today's Machine Learning(ML) systems, especially those running in server farms running workloads such as Deep Neural Networks, which require billions of parameters and many hours to train a model, consume a significant amount of energy. To combat this, researchers have been focusing on new emerging neuromorphic computing models. Two of those models are Hyperdimensional Computing (HDC) and Spiking Neural Networks (SNNs), both with their own benefits. HDC has various desirable properties that other Machine Learning (ML) algorithms lack such as: robustness to noise in the system, simple operations, and high parallelism. SNNs are able to process event based signal data in an efficient manner. In this paper, we create HyperSpike, which utilizes a single, randomly initialized and untrained SNN layer as feature extractor connected to a trained HDC classifier. HDC is used to enable more efficient classification as well as provide robustness to errors. We experimentally show that HyperSpike is on average 31.5× more robust to errors than traditional SNNs. We also implement HyperSpike in hardware, and show that it is 10x faster and 2.6× more energy efficient over traditional SNN networks run on Intel's Loihi [1].
Reported Road Casualties in Great Britain: Main Results 2013

What we can conclude: There has been statistically significant decreases in the number of people seriously and slightly injured (but not killed) in road traffic accidents between 2014 and 2015. This indicates that there are a number of factors that have combined together to improve some aspects of safety on Britain’s roads. However, it is not definitive evidence of a continued improvement in road deaths. What we cannot conclude: Although the number of people killed in road traffic accidents has decreased between 2014 and 2015, this change is small enough that it can be explained by the natural variation in deaths over time. There is no evidence that the number of fatalities has changed over recent years. Statistical Release 30 June 2016
The influence of unsteady aerodynamics and inter-blade aerodynamic coupling on the blades responses to harmonic variations of their pitch angles

Abstract During recent years a new vortex model of the unsteady aerodynamics of a hovering rotor or a rotor in axial flight, named Temura, has been developed in the Technion. This model has succeeded in describing various phenomena of rotor dynamics. In the present paper Temura is used in order to develop a model which describes the blades’ responses to harmonic variations of their pitch angles. This model includes unsteady influences of trailing and shed vortices, together with unsteady geometric effects which represent a special capability of Temura. The model is general and can be applied to any number of blades and arbitrary differences between the pitch angle variations of different blades. The application of the model to analyse cases of collective harmonic pitch variations and differential harmonic pitch variations is presented. Numerical results of the new model are compared with experimental results from the literature and good agreement is shown.
Reliability Analysis of a Spiking Neural Network Hardware Accelerator

Despite the parallelism and sparsity in neural network models, their transfer into hardware unavoidably makes them susceptible to hardware-level faults. Hardware-level faults can occur either during manufacturing, such as physical defects and process-induced variations, or in the field due to environmental factors and aging. The performance under fault scenarios needs to be assessed so as to develop cost-effective fault-tolerance schemes. In this work, we assess the resilience characteristics of a hardware accelerator for Spiking Neural Networks (SNNs) designed in VHDL and implemented on an FPGA. The fault injection experiments pinpoint the parts of the design that need to be protected against faults, as well as the parts that are inherently fault-tolerant.
GAN-enhanced simulated sonar images for deep learning based detection and classification

Data sparsity is a well-known limitation in the sonar domain. This limitation is a problem when applying data-intensive techniques from the computer vision community, such as deep learning models for detection and classification. One way of extending a sonar dataset is to use simulated sonar images however, these often have the drawback of looking non-realistic when compared to domain data. To overcome the data-sparsity problem as well as for generating realistic-looking sonar images, we introduce a pipeline where the possibilities and limitations of applying cycleGAN to enhance simulated forward-looking sonar images are explored. The results show improved classification performance when training a classifier on enhanced-simulated images compared to training on solely simulated images.
CR&P: An Efficient Co-operation between Routing and Placement

Placement and Routing (P&R) are two main steps of the physical design flow implementation. Traditionally, because of their complexity, these two steps are performed separately. But the implementation of the physical design in advanced technology nodes shows that the performance of these two steps is tied to each other. Therefore, creating efficient co-operation between the routing and placement steps has become a hot topic in Electronic Design Automation (EDA). In this work, to achieve an efficient collaboration between the routing and placement engines, an iterative replacement and rerouting framework facilitated with an Integer Linear Programming (ILP)-based legalizer is proposed and tested on the ACM/IEEE International Symposium on Physical Design (ISPD) 2018 contest's benchmarks. Numerical results show that the proposed framework can improve detailed routing vias and wirelength by 2.06% and 0.14% on average in a reasonable runtime without adding new Design Rule Violations (DRVs). The proposed framework can be considered as an add-on to the physical design flow between global routing and detailed routing.
K-TEM: KEMET Thermal Expectancy Model

Metallized film capacitor market is driven by a wide range of applications, which demand higher current capability, higher working temperature and higher lifetime performance while maintaining small sizes. Temperature is an important factor, and it is often the cause of failures and reduced life. For this reason, capacitors designed to operate at high current and high temperature require special attention. Modeling and 3D FEM simulations can be used as a powerful and accurate medium to evaluate local temperature raises and maximum hot spot values. This paper presents how Modeling and 3D FEM has been integrated in a new easy-to-use web tool, first to the market, that runs a finite element analysis for the desired components based on costumer defined inputs, to match the real application environment. K-TEM allows to have a precise and fast estimation of the thermal behavior of KEMET metallized film capacitors under several possible boundary conditions and configurations.
FAST modularization framework for wind turbine simulation: full-system linearization

The wind engineering community relies on multiphysics engineering software to run nonlinear time-domain simulations e.g. for design-standards-based loads analysis. Although most physics involved in wind energy are nonlinear, linearization of the underlying nonlinear system equations is often advantageous to understand the system response and exploit well- established methods and tools for analyzing linear systems. This paper presents the development and verification of the new linearization functionality of the open-source engineering tool FAST v8 for land-based wind turbines, as well as the concepts and mathematical background needed to understand and apply it correctly.
Multiphase FCML Converter with Coupled Inductors for Ripple Reduction and Intrinsic Flying Capacitor Voltage Balancing

This paper investigates multiphase flying capacitor multilevel (FCML) converters with coupled inductors for ripple reduction and intrinsic flying capacitor voltage balancing. FCML topologies and coupled inductors are two commonly used techniques to reduce power conversion stress, reduce ripple current, and improve system performance. Combining coupled inductors with multiphase FCML converters opens a wide range of design opportunities. The coupled inductors are shown to intrinsically balance the flying capacitor voltages, granting greater robustness to disturbances compared to uncoupled inductors and natural balancing. The ripple reduction offered by the coupled inductors and FCML structure are derived. The theoretical derivations are experimentally verified using a two-phase, three-level FCML converter. The ripple reduction and voltage balancing are realized as long as the inductors are sufficiently coupled.
DeepPM: Transformer-based Power and Performance Prediction for Energy-Aware Software

Many system-level management and optimization techniques need accurate estimates of power consumption and performance. Earlier research has proposed many high-level/source-level estimation modeling works, particularly for basic blocks. However, most of them still need to execute the target software at least once on a fine-grained simulator or real hardware to extract required features. This paper proposes a performance/power prediction framework, called Deep Power Meter (DeepPM), which estimates them accurately only using the compiled binary. Inspired by the deep learning techniques in natural language processing, we convert the program instructions in the form of vectors and predict the average power and performance of basic blocks based on a transformer model. In addition, unlike existing works based on a Long Short-Term Memory (LSTM) model structure, which only works for basic blocks with a small number of instructions, DeepPM provides highly accurate results for long basic blocks, which takes the majority of the execution time for actual application runs. In our evaluation conducted with SPEC2006 benchmark suite, we show that DeepPM can provide accurate prediction for performance and power consumption with 10.2% and 12.3% error, respectively. DeepPM also outperforms the LSTM-based model by up to 67.2% and 34.9% error for performance and power, respectively.
Designing High Performance of the Self-coupled Time Synchronization Protocol on Sea Surface for the Argo Float Sensors Network

For the sea surface salinity (SSS) applications, the data collected by the floats needs to be uploaded as a satellite passes through. The floats distributed on sea surface are equipped with sensors to measure interested information to deliver to visible satellites passing through the sky. The architecture can be divided into three subsystems, the satellite, the Argo float sensors and the data center. In this paper, we evaluate the performance of the self-coupled time synchronization protocol in the subsystem “Argo float sensors”. The basic idea is to achieve global synchronization through local pulse coupling. We will discuss the effect of various network parameters such as the degree of the node and the network diameter to the performance of the self-coupled mechanism. Furthermore, we will propose an improvement over this scheme in the paper. It reduces its message complexity by randomly turning on and off the sensor nodes for synchronization. We find that the self-coupled time synchronization protocol can achieve synchronization unless the network is sparse. It exhibits good cost-performance characteristics especially in the network environment of a great number of sensor nodes. Integrating with the proposed improvement, time synchronization can be effectively and economically reached as we will demonstrated in the paper.
RESPONSE OF NONSTRUCTURAL COMPONENTS IN DUCTILE LOAD-BEARING STRUCTURES SUBJECTED TO ORDINARY GROUND MOTIONS

The dynamic seismic behavior of nonstructural components attached to elastic-plastic load-bearing structures is assessed. The assessment is based on floor spectra of two-degree-of-freedom (2DOF) systems with assigned ductility of the supporting structure. Frame structures are transformed into equivalent single-degree-of-freedom (ESDOF) systems, and thus floor spectra of 2DOF systems become applicable for the prediction of the seismic peak response of vibratory nonstructural components. The utilized floor spectra are the outcome of a parametric study involving a set of 40 ordinary ground motions with strong motion characteristics. Example problems show the capability of this method to predict the seismic response of nonstructural elements with sufficient accuracy.
On Linear Model with Markov Signal Priors

In this paper, we estimate free energy, average mutual information, and minimum mean square error (MMSE) of a linear model under the assumption that the source is generated by a Markov chain. Our estimates are based on the replica method in statistical physics. We show that under the MMSE estimator, the linear model with Markov sources is decoupled into single-input AWGN channels with state information available at both encoder and decoder where the state distribution follows the stationary distribution of the stochastic matrix of Markov chains. Numerical results show that the free energies and MSEs obtained via the replica method are closely approximate to their counterparts via MCMC simulations.
Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data

The mixture cure model allows failure probability to be estimated separately from failure timing in settings wherein failure never occurs in a subset of the population. In this paper, we draw on insights from representation learning and causal inference to develop a neural network based mixture cure model that is free of distributional assumptions, yielding improved prediction of failure timing, yet still effectively disentangles information about failure timing from information about failure probability. Our approach also mitigates effects of selection biases in the observation of failure and censoring times on estimation of the failure density and censoring density, respectively. Results suggest this approach could be applied to distinguish factors predicting failure occurrence versus timing and mitigate biases in real-world observational datasets.