Miracle-dz/UniFAR-FTUs huggingface dataset:
Wafer-Scale Electrical Characterization of Silicon Quantum Dots from Room to Low Temperatures

Electron-spin qubits in silicon are one of the most promising platforms for implementing large-scale quantum computing. In this platform, a qubit, i.e., the basic unit of quantum information, is associated with the spin of a single electron confined in a region of silicon called a quantum dot. Electron-spin qubit devices must be operated at the cryogenic temperature of 40mK in a 3He/4He dilution refrigerator. This requirement results in long cool-down (“soak”) times and increased costs, which slow down the technology development. Our research aims at developing a high-volume, room-temperature screening technique to assess quantum dots variability and select suitable candidates for mK measurements. In this paper, we present transistor measurement data of quantum dots across a 300mm wafer at temperatures ranging from 300K down to 225K. We analyze the statistical distributions of transistor metrics to detect outliers across temperatures, and hence to prevent wasting measurement time and resources at mK on known bad devices. From the collected data, we conclude that among the metrics analyzed, the threshold voltage appears to be the preferred metric for an effective pre-screening of silicon quantum dots.
A Novel Generalized EEG Channel Selection Method Using Pearson Correlation Coefficient*

Electroencephalography (EEG), as a non-invasive and convenient method for implementing Brain-Computer Interface (BCI), has been widely used in clinical and research fields. EEG data often requires the acquisition of dozens or even hundreds of channels. Channel selection can reduce irrelevant and redundant channels, improve computational efficiency, and enhance the quality of EEG signals. This study introduces a filter method for channel selection based on Pearson correlation coefficient (PCC) with the candidate channel and employs topographic maps of EEG channel scores, derived from data collected across all subjects, to visualize the spatial distribution of channels selected by different methods. In addition, a generalized channel selection algorithm is proposed to determine consistent channels across all subjects in the experimental group. The effectiveness of the proposed method was evaluated on two steady-state visual evoked potential (SSVEP) datasets, and the results indicated that this method exhibits superior performance compared to both the all-channel method and other channel selection methods. And the application of the generalized channel algorithm has further improved the classification performance. This study uses selected generalized channels applied to new subjects with low BCI performance, yielding a significant improvement. The selected channels have a wide range of applicability, helping to simplify EEG acquisition and improve EEG data quality.
Near-Surface Air Temperature Lapse Rate Over Complex Terrain in the Southern Ecuadorian Andes: Implications for Temperature Mapping

ABSTRACT Near-surface air temperature variation with altitude (Tlr) is important for several applications including hydrology, ecology, climate, and biodiversity. To calculate Tlr accurately, a dense monitoring network over an altitudinal gradient is needed. Typically, meteorological monitoring in mountain regions is scarce and not adequate to calculate Tlr correctly. To overcome this problem in our region, we monitored temperature over a gradient ranging 2600–4200 m a.s. l. during an 18 month period. Using these data, we calculated Tlr for the first time at this altitude in the Andes and tested the impact of using the standard Tlr values instead of the observed ones to map temperature by means of the MTCLIM model. We found that annual lapse rate values (6.9 °C km-1 for Tmean, 5.5 °C km-1 for Tmin, and 8.8 °C km-1 for Tmax) differ significantly from the MTCLIM default values and that temperature maps improved vastly when measured Tlr was entered, especially for Tmax and Tmin. Our results may be representative of the broader area, as Tlr in our study period is not affected by microclimatic conditions generated by differences in topography and land cover between our monitoring sites; moreover, observed temperature during our study period was found to be representative of the longer-term annual climatology of the region.
Learning Hand Gestures using Synergies in a Humanoid Robot

Hand gestures are a natural way of communication and integrating them into robots could allow for more efficient human-robot collaboration. In recent years, researchers and roboticists have attempted to replicate human hand motor control using the concept of synergies. In this paper, we present a new approach to obtaining kinematic synergies from hand gestures using a single RGB camera. We capture real-time hand gestures using the MediaPipe framework and convert them to joint angular velocities. We then use dimensionality reduction to obtain kinematic synergies from the joint angular velocities. These synergies can be used to reconstruct new hand gestures. We translate these reconstructed hand movement patterns into a humanoid robot, Mitra. Our results show that it is possible to control most of the joints of the robot for performing hand gestures using only a few synergies. This is more efficient than other contemporary methods. Furthermore, robots and prosthetics that use synergy models could enable near-natural human-robot collaboration.
AoI-Oriented Scheduling in Downlink Multiuser MIMO Systems Under Peak-Power Constraint

This paper investigates user scheduling in a slotted downlink multi-user multiple-input multi-output (MU-MIMO) system, where a base station (BS) equipped with multiple antennas transmits status updates to multiple single-antenna devices. We assume perfect channel station information (CSI) is available at the BS to make scheduling decisions. The goal is to optimize the information freshness of the network, quantified by the network-wide average age of information (AoI), subject to the peak transmission power constraint in each time slot. To tackle this problem, we employ the Lyapunov optimization method to convert the constrained long-term optimization problem into a per-slot optimization problem. We propose two low-complexity algorithms to solve the per-slot optimization problem, namely the AoI-aware scheduling (AS) algorithm and the joint age- and channel-aware scheduling (JACS) algorithm. These algorithms aim to determine the optimal device or devices for delivering the latest status updates to them in each time slot. We evaluate the performance of the proposed algorithms through numerical simulations in both symmetric and asymmetric networks and compare them with two baseline algorithms: semi-orthogonal user group (SUG) and exhaustive search (ES) algorithms. Regardless of the network setup, our proposed algorithms consistently outperform SUG, and demonstrate performance comparable to that of the exhaustive search algorithm. Notably, when the peak transmission power is high enough, the proposed algorithms perform almost as well as the ES algorithm. Thanks to the joint consideration of CSI and AoI, JACS tends to have better performance than AS at the cost of slightly higher computational complexity.
A Wearable Sensor-Based Biofeedback System to Predict the Freezing of Gait (FoG) in Parkinson’s Patients

Freezing of Gait (FoG) is a disabling condition in Parkinson’s patients, which could cause their motion to be temporarily hindered. Wearable sensors have been commonly used to detect FoG, but this will be of limited use since the patient cannot be notified prior to the FoG event. In this research, a system that can be used to predict the FoG in Parkinson’s patients well ahead of the event was developed. This is a multimodal sensor system that consists of Plantar pressure sensors placed in the insole of the shoes and Inertial Measurement Units placed on the shank of each leg. 3 machine learning algorithms were developed: K means, K Nearest Neighbors, and Support Vector Machines. Out of the 3 algorithms used, the KNN algorithm was determined to have the highest FoG prediction sensitivity of 0.88. The Pressure sensors captured more swift variations in Gait during freezing compared to the IMUs. This system could identify FoG 4.42s ahead of the event and used an optimal time window of 1s. This system could be developed to yield more accurate predictions, and this could be integrated with an auditory biofeedback system to warn the patient of FoG, to minimize its occurrence.
A Highly Integrated Shape Memory Alloy Actuator With Precision Real-time Self-sensing Control

In recent years, there has been significant advancement in the technological development of shape memory alloy (SMA) actuators, resulting in their increased maturity and wider applicability. Concurrently, the industry’s demand for compact, rapid-response actuators with high precision has seen a notable rise. In light of this, this study presents a novel SMA actuator utilizing Ni-Ti shape memory alloy (Ni-Ti SMA) wire, driven by the concept of SMA wire heating-induced contraction. The proposed actuator serves a dual purpose: measuring the intrinsic self-sensing attributes of the SMA wire and accurately ascertaining displacement alterations through the phase transition of the SMA wire, thereby precisely determining positional changes. By capitalizing on the resistance-displacement mapping relationship during the SMA wire’s phase transformation, autonomous self-sensing control is achieved without the need for external sensors. To meet the imperatives of rapid response, superior precision, and minimal form factor for the SMA wire, the actuator is ingeniously integrated to incorporate an auxiliary position sensor and a self-resetting loading mechanism. This integration significantly downsizes the overall dimensions of the SMA actuator, aligning it with the criteria of compactness while retaining performance.
Mastering Reference Architectures with Modeling Assistants

Reference architectures (RA) can significantly simplify and speed up the design and evolution of software systems in virtually any domain. Rather than starting from scratch or trying to reuse models not meant for reuse, software architects can exploit RAs to initiate their designs from rigorously-defined architectural models specifically defined to support reuse. RAs can guarantee the satisfaction of key functional and non-functional requirements, including those related to technical and societal challenges, such as security and sustainability, significantly reducing the design effort and improving the design quality. The growing popularity of RAs is confirmed by the many providers, including Amazon AWS, Microsoft Azure, IBM, and Salesforce, that offer RAs for application deployment on their infrastructures. Unfortunately, RAs are expensive to define since they must comprehensively synthesize the design knowledge of specific application domains (e.g., e-commerce, automotive, etc.) and technical contexts (e.g., cloud systems, mobile apps, IoT, etc.). This challenge limits the availability of RAs to a few domains and contexts, hindering their adoption. We envision the opportunity to facilitate RA-based design by introducing a novel combination of mining in-the-wild and continuous knowledge refinement through a framework named ASSISTRA. Mining in-the-wild consists of bots crawling architectural models at scale (e.g., from public repos) and analyzing them continuously to automatically synthesize RAs and extract RAs-related practices that shall feed knowledge bases. Continuous knowledge refinement consists of a population of recommender systems that, while interactively supporting the architects in their activity using the shared knowledge bases, shall continuously refine the mined content in the knowledge base (e.g., based on architects accepting, modifying, or rejecting suggestions). This process shall generate high-quality recommendations, drastically simplifying the design activity. This paper elaborates on this vision, highlights the key challenges, and calls for novel research contributions.
Machine Learning-Based Prediction of Machine Hours and Fuel Consumption: A Case Study in Aruwakkalu Limestone Quarry, Sri Lanka

The machine hours are of paramount importance in the mining sector as they directly impact production levels, operational costs, and overall efficiency. Accurate prediction of machine hours and fuel consumption using machine learning techniques relies on the availability of a comprehensive historical database. This prediction study focuses on a site-specific context and is specifically applicable to large-scale open pit limestone mines, such as the renowned Aruwakkalu Limestone Quarry in Sri Lanka. The scientific objective involves analyzing the accuracy of algorithms and utilizing a highly precise model to predict machine hours and fuel consumption based on monthly tonnage of limestone. This scientific study utilizes four machine learning algorithms: decision tree, linear regression, LGBM (Light Gradient Boosting Machine) regressor, and random forest. The assessment utilizes monthly report data from the quarry spanning a three-year period. Subsequently, the chosen model is applied to predict machine hours and fuel consumption based on tonnage. Findings reveal that the decision tree algorithm demonstrates remarkable accuracy for dump trucks compared to other methods, while the LGBM regressor performs better for excavators and dozers. For predicting fuel consumption, LGBM outperforms dump trucks; decision tree excels in excavators; random forest achieves dozer accuracy.
The Enhanced Network Swin-T by CNN on Flow Pattern Recognition for Two-phase Image Dataset with Low Similarity

In the two-phase flow experiments with different conditions of materials and process parameters, the collected image dataset with the low similarity and small amount was difficult for the common deep learning algorithms to achieve a high-precision recognition of flow pattern. due to the low extraction capability of global features. In this article, we proposed a new deep learning algorithm to enhance Swin-T network by CNN which combined the advantages of Swin-T network with Dynamic Region-Aware Convolution. The new algorithm retained the window multi-head self-attention mechanism and added the self-attention adjustment module to enhance the extraction of image features and the convergence speed of network. It significantly improved the recognition accuracy of the different flow patterns in the sharp and blurred images. The enhanced network Swin-T by CNN had the high applicability to the classification of image dataset with low similarity and small amount.
Density of periodic measures and large deviation principle for generalised mod one transformations

We introduce a new class of piecewise monotonic maps, called generalised mod one transformations, which include all (α,β) and generalised β -transformations, and prove that all transitive generalised mod one transformations with positive topological entropy satisfy the level-2 large deviation principle with a unique measure of maximal entropy. This is obtained by our result on the density of periodic measures for more general class of piecewise mototonic maps, which is a partial answer to the open problem posed by Hofbauer and Raith (1998 Fund. Math. 157 221–34); Raith (www.math.iupui.edu/~mmisiure/open/PR1.pdf).
The importance of detail fault geometry in modelling the stress change caused by large earthquake: Case Study of 2017 Mw 6.7 Poso earthquake

One of the criteria that can describe the stress condition under which a fault can rupture is the Coulomb Failure Criterion. One technique to calculate the Coulomb Failure Stress (∆CFS) is through the specified orientation approach that requires the strike, dip, and rake of fault geometry parameters. Most studies use planar fault which could degrade the accuracy of the stress calculation. In this study, we present the fault model with variable geometry in order to produce a more detailed stress calculation. We implement the 2017 Mw 6.7 Poso earthquake to model the stress transfer into Poso Fault which has a bent geometry. We compare the stress result with three different cases: planar and curve fault geometries. The result from curve fault geometries is sufficiently different than using the former one. The Coulomb stress distribution is generated in a more detail way when using multi-strike geometry as this parameter is more sensitive to the Coulomb stress change calculation. As demonstrated from this study, fault geometry plays an important role in the Coulomb stress imparted to highly orientated faults.
The Observed O vi Is Just the Tip of the Iceberg: Estimating the Hidden Material in Circumgalactic and Intergalactic Clouds

This paper proposes a new method for estimating the total quantity of material in moving circumgalactic and intergalactic clouds from O vi measurements. We simulate high-velocity clouds (HVCs) with the FLASH hydrodynamic code and track the ionization and recombination of all ionization levels of oxygen as a function of time. We calculate the O vi/oxygen ratio (f O VI ) in our dynamic nonequilibrium ionization clouds, finding that it differs significantly from that in static gas. We find that O vi exists in cool, medium, and hot gas in the clouds. As such, it traces all of the hydrogen rather than merely the ionized hydrogen. The total quantity of hydrogen along a typical observed line of sight through a cloud can be estimated from the observed O vi column density, metallicity, and our f O VI . We provide the simulations’ f O VI values, a prescription for finding f O VI for observed dynamic clouds, and a methodology for calculating the total hydrogen column density from f O VI and an observed O vi column density. As examples, we use our f O VI values to estimate the total hydrogen column densities along various observed sight lines through two HVCs, Complex C and the Magellanic Stream, finding that these clouds contain more material than previous lower limits. We also extend this analysis to low-redshift intergalactic O vi clouds, finding that they contain several times more baryonic material than previously thought and therefore may account for a significant fraction of the Universe’s baryons.
Response of Geologic Units to The Ground Parameters of Tarutung Earthquake 2022 Mw 5.8 : A Preliminary Study

On 1st October 2022, a moderate crustal earthquake with Mw 5.8 occurred in the northen part of Tarutung region and was strongly felt by the local peoples and followed by massive damages. The occurrence of the Tarutung earthquake was firstly assumed in the southern part of the Renun fault then updated to the pull-apart system of Toru fault. Here, we concern about the ground paramaters that generated a massive shaking and connect with the recent geologic units beneath the Tarutung region. The peak ground acceleration (PGA) and peak ground velocity (PGV) clearly show an amplification case may arise in the basin formation that consists of Alluvium Formation whereas the attenuation to the west and east part with Martimbang and Sipoholon Caldera Formation. The PGV was ranging at 0 – 10 cm/s and the PGA was ranging at 0% – 16% gal which are suitable with the intensity range at 3 – 6 MMI. The PGV, PGA and intensity relationship show a possibility of the building damage percentage is up to 80% and 50% of the surrounding area may potentially experience a complete damage. The pseudo-spectral acceleration (PSV) at 0.3s, 1.0s, and 3.0s also show a potential damage that can be found at low-moderate building structure with 8% - 16% gal. The ground parameters show an empirical relationship that can be used to assess the possible of geologic units in the Tarutung region. Furthermore, our result benefits can be applied to support the mitigation program and environmental planning in Tarutung region in the near future.
Journal of Economic Behavior & Organization

In this article we explore the issues that surround within-subject and between-subject designs. We describe experiments in economics and in psychology that make comparisons using either of these designs (or both) that sometimes yield the same results and some-times do not. The overall goal is to establish a framework for understanding which critical questions need to be asked about such experimental studies, what authors of such studies can do to ameliorate fears of confoundedness, and which scenarios are particularly susceptible to divergent results from the two approaches. Overall, we ﬁnd that both designs have their merits, and the choice of designs should be carefully considered in the context of the question being studied and in terms of the practical implementation of the research study.
Future Response of Temperature and Precipitation to Reduced Aerosol Emissions as Compared with Increased Greenhouse Gas Concentrations

AbstractExperiments with a climate model (NorESM1) were performed to isolate the effects of aerosol particles and greenhouse gases on surface temperature and precipitation in simulations of future climate. The simulations show that by 2025–49 a reduction of aerosol emissions from fossil fuels following a maximum technically feasible reduction (MFR) scenario could lead to a global and Arctic warming of 0.26 and 0.84 K, respectively, as compared with a simulation with fixed aerosol emissions at the level of 2005. If fossil fuel emissions of aerosols follow a current legislation emissions (CLE) scenario, the NorESM1 model simulations yield a nonsignificant change in global and Arctic average surface temperature as compared with aerosol emissions fixed at year 2005. The corresponding greenhouse gas effect following the representative concentration pathway 4.5 (RCP4.5) emission scenario leads to a global and Arctic warming of 0.35 and 0.94 K, respectively. The model yields a marked annual average northward shi.