Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of sample thickness on the van der Pauw technique for resistivity measurements

The van der Pauw technique is commonly used for electrical transport measurements on solid materials and is suited to thin, arbitrarily shaped samples, with the contacts placed anywhere on the periphery. Previous investigations have determined the effects of sample shape, contact size, and contact placement on the accuracy of this technique. Sample inhomogeneity effects have also been investigated. The effects of the sample thickness appear not to have been previously studied and are investigated here in order to establish precisely what is meant by a thin sample. The deterioration in the accuracy of the technique is followed as the sample thickness is increased. The results indicate that the van der Pauw technique may be used for samples where the thickness is comparable to the surface dimensions. The investigation also shows that the technique is valid for arbitrarily thick samples provided that low resistance electrodes are placed across the entire edge, perpendicular to the surface, in order to mainta.
MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural Networks

Driven by the wide adoption of deep neural networks (DNNs) across different application domains, multi-tenancy execution, where multiple DNNs are deployed simultaneously on the same hardware, has been proposed to satisfy the latency requirements of different applications while improving the overall system utilization. However, multi-tenancy execution could lead to undesired system-level resource contention, causing quality-of-service (QoS) degradation for latency-critical applications.To address this challenge, we propose MoCA1, an adaptive multi-tenancy system for DNN accelerators. Unlike existing solutions that focus on compute resource partition, MoCA dynamically manages shared memory resources of co-located applications to meet their QoS targets. Specifically, MoCA leverages the regularities in both DNN operators and accelerators to dynamically modulate memory access rates based on their latency targets and user-defined priorities so that co-located applications get the resources they demand without significantly starving their co-runners. We demonstrate that MoCA improves the satisfaction rate of the service level agreement (SLA) up to 3.9× (1.8× average), system throughput by 2.3× (1.7× average), and fairness by 1.3× (1.2× average), compared to prior work.
7.8 A 22nm Delta-Sigma Computing-In-Memory (Δ∑CIM) SRAM Macro with Near-Zero-Mean Outputs and LSB-First ADCs Achieving 21.38TOPS/W for 8b-MAC Edge AI Processing

In Al-edge devices, the changes of input features are normally progressive or occasional, e.g., abnormal surveillance, hence the reprocessing of unchanged data consumes a tremendously redundant amount of energy. Computing-in-memory (CIM) directly executes matrix-vector multiplications (MVMs) in memory, eliminating costly data movement energy in deep neural networks (DNNs) [2–6]. Prior CIM work only explored the sparsity of DNNs to improve energy efficiency, but the trend of employing non-sparse activation functions, e.g., leaky ReLU, degrade the benefits of leveraging sparsity [1]. Even if sparsity can be exploited, the redundant unchanged input features in analog CIM still consume massive amount of dynamic power (Fig. 7.8.1). From a circuit point-of-view, the energy consumption of analog CIMs is dominated by full-precision ADCs. In different DNN applications, the mean of analog CIM outputs is unpredictable and fluctuating, which requires the ADC to have a high dynamic range to guarantee coverage, introducing a high-power overhead.
Temporal Response Measurements of GaAs-Based Photocathodes

It is well known that a negative electron affinity GaAs photocathode shows a moderate temporal response when excited by a laser pulse of wavelength close to its band gap energy. We show here that the temporal response can be estimated using a diffusion model that describes the internal transport of the conduction electrons. Using a transverse deflection cavity system, we measured the temporal profile of the electron bunch generated by a DC photocathode gun illuminated by a ps pulsed laser. A systematic set of measurements of GaAs cathodes with various active layer thicknesses and boundary conditions confirmed that the observed temporal response is well understood by the diffusion model calculation.
Quantifying Cognitive Load from Voice using Transformer-Based Models and a Cross-Dataset Evaluation

Cognitive load is frequently induced in laboratory setups to measure responses to stress, and its impact on voice has been studied in the field of computational paralinguistics. One dataset on this topic was provided in the Computational Paralinguistics Challenge (ComParE) 2014, and therefore offers great comparability. Recently, transformer-based deep learning architectures established a new state-of-the-art and are finding their way gradually into the audio domain. In this context, we investigate the performance of popular transformer architectures in the audio domain on the ComParE 2014 dataset, and the impact of different pre-training and fine-tuning setups on these models. Further, we recorded a small custom dataset, designed to be comparable with the ComParE 2014 one, to assess cross-corpus model generalisability. We find that the transformer models outperform the challenge baseline, the challenge winner, and more recent deep learning approaches. Models based on the ‘large’ architecture perform well on the task at hand, while models based on the ‘base’ architecture perform at chance level. Fine-tuning on related domains (such as ASR or emotion), before fine-tuning on the targets, yields no higher performance compared to models pre-trained only in a self-supervised manner. The generalisability of the models between datasets is more intricate than expected, as seen in an unexpected low performance on the small custom dataset, and we discuss potential ‘hidden’ underlying discrepancies between the datasets. In summary, transformer-based architectures outperform previous attempts to quantify cognitive load from voice. This is promising, in particular for healthcare-related problems in computational paralinguistics applications, since datasets are sparse in that realm.
Multi-Learning Generalised Low-Rank Models

Multi-output supervised learning and multi-task learning are all instances of a broader learning paradigm where features, parameters and objectives are shared to a certain extent. Examples of such approaches include reusing features from pre-existing models in a new algorithm, performing multi-label regression or optimising for several tasks jointly. In this paper, we address this challenge by devising a generic framework based on generalised low-rank models ("GLRMs"), which include – broadly speaking– most techniques that can be expressed in terms of matrix factorisation. Importantly, while GLRMs first and foremost tackle unsupervised learning problems and supervised linear models. Here, we show that GLRMs can be extended by introducing multivariate functionals and structure regularisation terms to handle multivariate learning. This paper also proposes a coherent framework to design multi-learning strategies and covers existing algorithms. Finally, we prove the simplicity and effectiveness of our approach on empirical data.
Compact Omnidirectional Circularly Polarized Antenna Realized by a Parallel ME-dipole Pair

A compact omnidirectional circularly polarized antenna (OCP) realized by a parallel ME-dipole pair is proposed in this paper. This antenna consists of two symmetrical meandering clues and a small ground plane, which helps to obtain a compact design. The arrangement of two clues and ground plane help to construct equivalent M-dipole and E-dipole, respectively, which are the key operation to realize OCP radiation. For the final antenna design, simulated $\vert S_{11}\vert$ below -10dB is from 1.723 GHz to 1.728 GHz. At $f_{0}=1.726$ GHz, satisfactory OCP pattern is achieved, and the simulated 3-dB axial-ratio beamwidth in the cutting plane of $\varphi=45^{\circ}$ and 135° are as wide as 360° and 167°, respectively.
Generating Syntetic Data for Credit Card Fraud Detection Using GANs

Deep learning-based classifiers for object classification and recognition have been utilized in various sectors. However according to research papers deep neural networks achieve better performance using balanced datasets than imbalanced ones. It's been observed that datasets are often imbalanced due to less fraud cases in production environments. Deep generative approaches, such as GANs have been applied as an efficient method to augment high-dimensional data. In this research study, the classifiers based on a Random Forest, Nearest Neighbor, Logistic Regression, MLP, Adaboost were trained utilizing our novel K-CGAN approach and compared using other oversampling approaches achieving higher F1 score performance metrics. Experiments demonstrate that the classifiers trained on the augmented set achieved far better performance than the same classifiers trained on the original data producing an effective fraud detection mechanism. Furthermore, this research demonstrates the problem with data imbalance and introduces a novel model that's able to generate high quality synthetic data.
Solar radius variations over the past 265 years

Analysis of five different data sets including meridian circle observations, timings of transits of Mercury, and durations of total solar eclipses consistently suggests the presence of a 76 year modulation of the solar radius. The last solar radius maximum occurred in about A. D. 1911, and the half-amplitude of variation is approx.0''. 2 (0.02%). The 76 year radius variation is negatively correlated with the Gleissberg cycle of the mean sunspot numbers at the 2 sigma level of significance. A more significant 3 sigma correlation between the solar radius and sunspot cycle is found at 11 years from examination of several independent data sets. Again the correlation is negative, with maximum sunspot number corresponding to minimum solar radius. The half-amplitude of the 11 year variation is approx.0''. 1. A secular decrease of approx.0''. 1 per century over the last 265 years is also likely from an objective analysis of available data.
A New Framework to Assess the Individual Fairness of Probabilistic Classifiers

Fairness in machine learning has become a global concern due to the predominance of ML in automated decision-making systems. In comparison to group fairness, individual fairness, which aspires that similar individuals should be treated similarly, has received limited attention due to some challenges. One major challenge is the availability of a proper metric to evaluate individual fairness, especially for probabilistic classifiers. In this study, we propose a framework PCIndFair to assess the individual fairness of probabilistic classifiers. Unlike current individual fairness measures, our framework considers probability distribution rather than the final classification outcome, which is suitable for capturing the dynamic of probabilistic classifiers, e.g., neural networks. We perform extensive experiments on four standard datasets and discuss the practical benefits of the framework. This study can be helpful for machine learning researchers and practitioners flexibly assess their models' individual fairness. The complete code of the framework is publicly available1.
Dynamics of possible Trojan planets in binary systems

This paper is devoted to the dynamical stability of possible Trojan planets in binaries and in binary systems where one of the substellar companions is not larger than a brown dwarf. Using numerical integrations, we investigated how the size of the stable region around the Lagrangian point L 4 depends on the mass parameter and the eccentricity of the secondary star. An additional goal of this work was to create a catalogue of all possible candidates, which could be useful for future observations to detect such objects.
Magnetic properties of CoSm thin films with Cr/sub 80/V/sub 20/ and Cr as underlayers

We investigated the static and time-dependent magnetic properties of thin CoSm films (6-14 nm) on CrV underlayers. We measured remanent coercivities of up to 4.5 kOe and viscosity coefficients as low as 8%/decade for 14-nn films with remanent moment thickness product of 0.49 memu/cm/sup 2/. We compared magnetic hardness, interactions, viscosity, and anisotropy with similar films grown on Cr underlayers, and found that CrV constitutes a better underlayer for CoSm thin film media, most probably because of improved epitaxy at the interface and a more favorable interface, microstructure, and topography.
Influence of the spectrum of all-day LED lighting on human daytime and nighttime performance

Indoor LED lighting is directly related to people’s health and performance. This experiment investigated the effect of the spectrum of LED lighting on human daytime and nighttime performance. Six different LEDs were selected for the experiment: ordinary LEDs and daylight LEDs that mimic the full spectrum, with CCTs of 2700K, 4000K, and 5000K for each spectral type (2 spectral types * 3 CCT). A total of 24 subjects participated in the experiment. Each subject was assigned to a specific CCT and experienced two spectral types on different days. Subjects were given a predetermined light exposure for 12 hours a day for each experiment. By analyzing the subjects' performance during the daytime light exposure and nighttime sleep quality after, we found that daytime exposure to light with stronger non-visual stimulus led to higher daytime alertness and better nighttime sleep quality, and the lack of daytime light stimuli led to poor night sleep quality. However, changes in spectral composition within a small range were not enough to cause a sufficiently significantly improve in daily human performance.
Research on Numerical Calculation and Error Analysis of Corona Discharge Current Pulse

The existence of partial discharge (PD) phenomenon seriously affects the operation of high-voltage electrical equipment. In this paper, the research of corona discharge is based on the needle-plate model. By solving the electron continuity equation, the mean electron energy equation and the Poisson's equation of electric field to get transport parameters to replace traditional simplified ionization parameters. Then, the Pau Ta Criterion is used to analysis the validity of the results of multiple numerical calculations. Finally, the calculated current pulse can provide technical support for the in-depth study of the electromagnetic propagation characteristics caused by partial discharge signals.
Training SNNs Low Latency Utilizing Batch Normalization Through Time and Iterative Initialization Retraining

Spiking Neural Network (SNN), developing on neuromorphic hardware, is a promising energy-efficient AI paradigm. However, processing over several timesteps reduces the energy benefits of SNNs due to high latency, the number of operations, and memory access costs from acquiring membrane potentials. Furthermore, their non-derivative nature makes SNNs difficult to train properly. To overcome these issues and leverage the full potential of SNNs, in this research, we offer a novel way for training deep SNNs utilizing Batch Normalization Through Time and Iterative Initialization and Retraining techniques. First, the BNTT improves low-latency and low-energy training in SNNs by allowing neurons to handle the spike rate over many timesteps. Second, we can obtain SNNs with up to unit latency pass during inference when applying the Iterative Initialization and Retraining technique during training SNNs. On the CIFAR-10, CIFAR-100, and ImageNet, we achieve cutting-edge SNN performance using a deep neural network with just one timestep. We achieve top-1 accuracy of 91.01%, 71.88%, and 69.8% on CIFAR-10, CIFAR-100, and ImageNet, respectively, using the VGG 16 architecture.
22.1 A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RISC-V, 2-to-8b Precision-Scalable DNN Acceleration and 30%-Boost Adaptive Body Biasing

Emerging Artificial Intelligence-enabled Internet-of-Things (Al-loT) SoCs [1–4] for augmented reality, personalized healthcare and nano-robotics need to run a large variety of tasks within a power envelope of a few tens of mW: compute-intensive but bit-precision-tolerant Deep Neural Networks (DNNs), as well as signal processing and control requiring high-precision floating-point. Performance and energy constraints vary greatly between different applications and even within different stages of the same application. We present Marsellus (Fig. 22.1.1), an all-digital Al-loT end-node heterogeneous $\mathsf{SoC}$ fabricated in GlobalFoundries $22\mathsf{nm}$ FDX that combines three key contributions to enable aggressive scaling of performance and energy: 1) a generalpurpose cluster of 16 RISC-V DSP cores attuned for execution of a diverse range of workloads exploiting $4\mathsf{b}$ and $2\mathsf{b}$ arithmetic extensions (XpulpNN), combined with fused MAC $\&$ LOAD (M&L) operations and floating-point support; 2) a 2-8b reconfigurable binary engine to accelerate $3\times 3$ and $1\times 1$ (pointwise) convolutions in DNNs; 3) a set of On-Chip Monitoring (OCM) blocks connected to an Adaptive Body Bias (ABB) generator and a hardware control loop, enabling on-the-fly adaptation of transistor threshold voltages.
Gender dynamics of German journalists on Twitter

Women are underrepresented in many areas of journalistic newsrooms. In this paper, we examine if this es-tablished effect is continued in the new forms of journalistic communication, Social Media Networks. We used mentions and retweets as measures of journalistic amplification and legitimation. Furthermore, we compared two groups of journalists in different stages of development: political and data journalists in Germany in 2021. Our results show that journalists regarded as women tend to favor their peers in mentions and retweets on Twitter: while both professions are dominated by a massive number of men and a high share of men-authored tweets, females mentioned and retweeted other women to a more extensive degree than their male colleagues. In addition, we have found data journalists to be more inclusive towards non-members in their network compared to political journalists.
MAJOR-MERGER GALAXY PAIRS IN THE COSMOS FIELD—MASS-DEPENDENT MERGER RATE EVOLUTION SINCE z = 1

We present results of a statistical study of the cosmic evolution of the mass-dependent major-merger rate since z = 1. A stellar mass limited sample of close major-merger pairs (the CPAIR sample) was selected from the archive of the COSMOS survey. Pair fractions at different redshifts derived using the CPAIR sample and a local K-band-selected pair sample show no significant variations with stellar mass. The pair fraction exhibits moderately strong cosmic evolution, with the best-fitting function of fpair = 10−1.88(± 0.03)(1 + z)2.2(± 0.2). The best-fitting function for the merger rate is Rmg (Gyr−1) = 0.053 × (Mstar/1010.7 M☉)0.3(1 + z)2.2/(1 + z/8). This rate implies that galaxies of Mstar ∼ 1010–1011.5 M☉ have undergone ∼0.5–1.5 major mergers since z = 1. Our results show that, for massive galaxies (Mstar ⩾ 1010.5 M☉) at z ⩽ 1, major mergers involving star-forming galaxies (i.e., wet and mixed mergers) can account for the formation of both ellipticals and red quiescent galaxies (RQGs). On the other hand, major mergers cannot be responsible for the formation of most low mass ellipticals and RQGs of Mstar ≲ 1010.3 M☉. Our quantitative estimates indicate that major mergers have significant impact on the stellar mass assembly of the most massive galaxies (Mstar ⩾ 1011.3 M☉), but for less massive galaxies the stellar mass assembly is dominated by the star formation. Comparison with the mass-dependent (ultra)luminous infrared galaxies ((U)LIRG) rates suggests that the frequency of major-merger events is comparable to or higher than that of (U)LIRGs.
Hybrid Approach using Extreme Gradient Boosting (XGBoost) and Evolutionary Algorithm for Cancer Classification

Cancer is a leading cause of mortality globally. World Health Organization estimates that around 10 million people die from cancer in 2020. Cancers of the colon, breast, lung, central nervous system, cervix, and prostate are quite common. In most cases, cancer may be efficiently treated if caught early. The biological data included in microarrays is very informative. This data analysis aids in the identification and treatment of difficult illnesses. It is quite difficult and time consuming to train a model with such a large number of features, when they are all input directly. Therefore, we employ an Evolutionary Algorithm named as Group Counseling Optimizer (GCO) in tandem with Extreme gradient boost (XGBoost) to classify cancer in microarray data. At the outset, we have the Extreme Gradient Boosting ensemble-selected features. In this step, irrelevant features are eliminated, and a set of optimal characteristics for detecting cancer has been generated. In a second usage, the evolutionary method is used for cancer classification.
Regular approach for generating van der Waals Cs coefficients to arbitrary orders

A completely general formalism is developed to describe the energy Edisp = ∑sCs/Rs of dispersion interaction between two atoms in spherically symmetric states. Explicit expressions are given up to the tenth order of perturbation theory for the dispersion energy Edisp and dispersion coefficients Cs. The method could, in principle, be used to derive the expressions for any s while including all contributing orders of perturbation theory for asymptotic interaction between two atoms. The theory is applied to the calculation of the complete series up to s = 30 for two hydrogen atoms in their ground state. A pseudo-state series expansion of the two-atom Green function gives rapid convergence of the series for radial matrix elements. The numerical values of Cs are computed up to C30 to a relative accuracy of 10−7 or better. The dispersion coefficients for the hydrogen–antihydrogen interaction are obtained from the H–H coefficients by simply taking the absolute magnitude of Cs.