Miracle-dz/UniFAR-FTUs huggingface dataset:
Recursive architectures for realizing modified discrete cosine transform and its inverse

In this paper, we present efficient recursive architectures for realizing the modified discrete cosine transform (MDCT) and the inverse MDCT (IMDCT) acquired in many audio coding systems. After data rearrangement, the MDCT and IMDCT can be represented as the Chebyshev polynomials such that we can efficiently implement them in the recursive structures. For verification, we design an ASIC to realize the recursive IMDCT. The analyzed results show that the proposed recursive infinite-impulse response (IIR) structures possess advantages of high efficiency and high throughput rate. The high regularity and modularity of the proposed recursive IMDCT and MDCT algorithms are other merits for very large scale integration implementation.
Training Low-Latency Deep Spiking Neural Networks with Knowledge Distillation and Batch Normalization Through Time

Spiking Neural Networks (SNNs) can significantly enhance energy efficiency on neuromorphic hardware due their sparse, biological plausibility and binary event (or spike) driven processing. However, from the non-differentiable nature of a spiking neuron, training high-accuracy and low-latency SNNs is challenging. Recent researches continue to look for ways to improve accuracy and latency. To address these issues in SNNs, we propose a technique that concatenates Knowledge Distillation (KD) and Batch Normalization Through Time (BNTT) method in this study. The BNTT boosts low-latency and low-energy training in SNNs by allowing a neuron to handle the spike rate through various timesteps. The KD approach effectively transfers hidden information from the teacher model to the student network, which converts artificial neural network parameters to SNN weights. This concept allows enriching the performance of SNNs better than the prior technique. Experiments are carried out on the Tiny-ImageNet, CIFAR-10, and CIFAR-100 datasets. on various VGG architectures. We reach top-1 accuracy of 55.67% for ImageNet on VGG-11 and 73.11% for the CIFAR-100 dataset on VGG-16. These results demonstrate that our proposal outperforms earlier converted SNNs in accuracy with only 5 timesteps.
Absolute UV and Vacuum UV Oscillator Strengths for Ni II

Radiative lifetimes, measured using time-resolved laser-induced fluorescence, are reported for 18 odd-parity levels of Ni+. The radiative lifetimes are combined with Ni II emission branching fractions to produce 59 accurate (typically ±10%) absolute atomic transition probabilities in the ultraviolet (UV) and vacuum ultraviolet (VUV). The UV and VUV branching fractions are determined using a combination of measurements from a Fourier transform spectrometer at the University of Lund and a 3 m focal length vacuum echelle spectrometer system at the University of Wisconsin. Accurate f-values for Ni II resonance lines are important for analysis of spectra on the diffuse interstellar medium (ISM) at low redshift and for studies of the same lines in high-redshift QSO absorption line systems. Resonance lines at vacuum wavelengths of 170.960, 174.155, and 175.191 nm have log gf values of -0.68, -0.60, and -0.80, respectively. Until additional laboratory measurements are completed, we recommend thatf-values for other Ni II resonance lines be taken from the relative f-values from Zsargó & Federman with a multiplicative scaling factor of 0.534 (±10%). The present measurements indicate that nickel is less depleted in the ISM than previously estimated.
Semi-supervised learning with cross-localisation in shared GAN latent space for enhanced OCT data augmentation

Deep learning methods have demonstrated stateof-the-art performance for the segmentation of the retina and choroid in optical coherence tomography (OCT) images. These methods are automatic and fast, yielding high accuracy and precision, thus reducing the load of manual analysis. However, deep learning usually requires large amounts of diverse, labelled data for training which can be difficult or infeasible to obtain, especially for medical images. For example, privacy concerns and lack of confidentiality agreements are common and are an obstacle to the sharing of useful training data. Additionally, some data can be significantly more difficult to obtain in the first place such as that of rare pathologies. Even in cases where sufficient data is available, the cost and time to perform image labelling can be significant. In many cases, data augmentation is employed to enhance the size of the training set. Similarly, semisupervised learning (SSL) can be used to exploit potentially large amounts of unlabeled data which would otherwise be unused. Motivated by this, in this study, we propose an enhanced StyleGAN2-based data augmentation method for OCT images by employing SSL through a novel crosslocalisation technique. For OCT image patches, the proposed method significantly improved the classification accuracy over the previous GAN data augmentation approach which uses labelled data only. The technique works by automatically learning, mixing, and injecting unlabelled styles into the labelled data to further increase the diversity of the synthetic data. The proposed method can be trained using differing quantities of both labelled and unlabelled data simultaneously. The method is simple, effective, generalizable and can be easily applied and used to extend StyleGAN2. Hence, there is also significant potential for the proposed method to be applied to other domains and imaging modalities for data augmentation purposes where unlabelled data exists.
Sommerfeld integrals for left‐handed materials

We study the properties of left‐handed materials (LHMs) with the precise help of Sommerfeld integrals. First we argue by energy conservation principles that LHM has to be a backward‐wave material (BWM). Then we derive the equivalence of the loss and the Sommerfeld far‐field radiation conditions for a BWM. Next, we solve the realistic Sommerfeld problem of a point source over an LHM half‐space and an LHM slab. This analysis lends physical insight into the interaction of a point source field with an LHM, showing that super‐resolution is possible with an LHM slab. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 42: 369–373, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20307
An Overview of Artificial Neuromorphic Circuits

This paper report a brief review of the neuromorphic computing systems along with a brief overview of their working principle, their design methodologies. Also this papers provides an insight to the new design techniques which opens up the possibility to attain high speed and large complexity with a lower energy cost. The neuropmorphic systems which are inspired by biological phenomenon’s are most promising candidate for next generation information processing and computations. The scopes, opportunities and challenges faced by the neuromorphic computing systems has been also discussed.
A Robust Approach for Small-Scale Object Detection From Aerial-View

In computer vision, object detection is an important task and gained significant progress but object detection using aerial images is still a challenging task for researchers. Small target size, low resolution, occlusion, attitude, and scale variations are the big concerns with aerial images that prevent many state-of-the-art object detectors performing well. In our experimentation, we have modified CenterNet and provided comparison of results achieved using nine different CNN-based backbones i.e., resNet18, resNet34, resNet50, resNet101, resNet152, res2Net50, res2Net101, DLA34 and hourglass104 and found promising results using invariant of centerNet and hourglass104 as a backbone. We used three challenging datasets to validate our approach i.e., VisDrone, Stanford and AU-AIR. By keeping the standard mAP, we achieved 91.62, 75.62 and 34.85 validation results using AU-AIR, Stanford and VisDrone datasets respectively. We have also compared the achieved mAP using IoU@0.5 and IoU@0.75 against different backbones. Our approach has achieved the promising results as compared to results provided in latest research.
Ab Initio Study on Stability, Electronic and Optical Properties of Monolayer Mo1−xWxSe2 Alloys

Due to their unique structures and physical properties, monolayer transition metal dichalcogenides (TMDs) have gained attention in nanoelectronic and flexible device applications. We investigated the effect of alloying on stability, electronic and optical properties of two–dimensional TMD alloys by exploring Mo1−xWxSe2. Using density–functional theory (DFT) calculations, we evaluated geometrical structures, electronic band structures, the contributions from atomic orbitals in both valence and conduction bands, Bader charge analysis, effective mass, mobility, dynamic stability, dielectric functions, and absorption coefficient for varying compositions in Mo1−xWxSe2 alloy. Our investigation revealed that the direct band gap in these alloys could be modulated with nonlinear dependency on composition. The effective mass and mobility of both hole and electron can be varied notably for different composition of alloys. Mo1−xWxSe2 alloys were energetically and dynamically stable. Furthermore, the high optical absorption of the alloys can be utilized in optoelectronic devices. The findings of this study enlighten the tunable electronic and optical properties of TMD alloys that will be beneficial for designing nanophotonic and nanoelectronic devices.
A Teams Approach to Idea Generation: An Analysis of Personality and Team Learning on Creativity

Creativity has been identified as a prerequisite for innovation which is considered highly important for organisations' survival and competitiveness. Research on factors affecting creativity has generally focused on a single level at a time i.e. individual, team or organisational. This research takes a multi-level approach to creativity by investigating the impact of two personality traits - extraversion and openness and team learning on individual creativity. It was hypothesized that openness, extraversion and team learning are positively related to individual creativity and the interaction between personality and team learning would have a positive effect on creativity. A survey using questionnaires was sent to 116 team members working in 24 teams from a mining company in South Africa. The results from multiple regression analysis showed openness to be positively related to creativity in developing new methods as well as creativity in problem solving. Extraversion was however found to negatively relate to creativity in developing new methods while having no significant relation to creativity in problem solving. Team learning did not significantly relate with creativity in developing new methods but related positively with creativity in problem solving. Finally, the interaction between team learning and either personality trait did not have any significant effect on creativity Based on this study it is recommended that managers must invest in employee development programs aimed at building openness in their subordinates as it was found to be the most consistent trait to predict employee creativity.
Sheath propagation along the cathode of a plasma opening switch

A model is proposed for sheath propagation along the cathode of a plasma opening switch that is valid for switch conditions intermediate to the erosion-dominated and MHD-dominated regimes. The model assumes that the sheath propagates due to erosion of the plasma that conducts the current. The calculated velocity of propagation agrees with particle-in-cell simulation results much better than do velocities calculated by previous models that assumed magnetic pressure opens a vacuum gap along the cathode.
Study on Vertical Vibration System of Person-Seat-Suspension of Electric Wheelchair

In order to reduce secondary injuries due to the Electric wheelchair vibration, the author established the mathematical model of the vertical vibration of the electric wheelchair person-seat-suspension coupling system. The vibration equation of the wheelchair-road coupling system is theoretically studied to study the interaction and coupling between the wheelchair and the road. This research result can be used to optimize the performance of the wheelchair suspension system, improve the comfort of the rider and avoid the secondary injury of the body.
GAN-Uplift: 2D to 3D Uplift with Generative Adversarial Networks

Human pose estimation and prediction has many applications from autonomous vehicles to video games development, animation and security. In many instances humans are recorded by video in two dimensions and this two-dimensional representation requires uplifting into three dimensions before being fully utilised. This paper proposes lifting two-dimensional skeleton representations of human movement into three dimensional skeleton representations over a sequence of human action, 2D to 3D uplift. The proposed approach builds on the work in HP-GAN [1] utilising a generative adversarial network (GAN) with a recurrent neural network encoder decoder as the generator and a multilayer fully connected neural network as the critic. A novel approach adds random noise from a normal distribution to the z dimension of each joint and a custom loss function consisting of the joint position in 3D space and bone length. The proposed algorithm GAN-Uplift successfully uplifts 2D motion sequences into their respective 3D motion sequences, with a sequence mean joint accuracy of 30.9mm and outperforms several state-of-theart methods and is within 0.4mm of the best state-of-the-art models on the Human3.6M skeleton dataset. In addition, stateof-the-art methods uplift a single pose from a sequence of pose input. GAN-Uplift uplifts a sequence of human poses rather than a single pose.
Deep Learning For Pose Estimation From Event Camera

Six degrees of freedom (6DOF) pose estimation is one of the common challenges in many robotic and computer vision applications. Most state of the art methods focus on conventional camera pose. In this paper, we propose to handle the problem of event camera pose estimation. We present in this paper to predict the camera pose using deep learning based method. It is composed of a convolutional and a recurrent neural networks connected to a dense layer regressor. We present results from a set of convolutional neural networks including commonly used ones. We demonstrated the performance of the proposed method on several datasets. The results demonstrate the superiority of the proposed methods compared to state-of-the art methods.
Area Efficient Energy Saving Inexact Multiplier for Error Resilient Applications

In this paper, we proposed energy efficient-compact approximate multiplier using the compressor technique with a full adder, half adder, and OR gate. A full adder in the least significant digit (LSD) of the exact compressor is replaced by a half adder and OR gate. The proposed compressor exhibits better performance with 23.3% less power, 20% reduction in area, and 4.1% less delay than an exact compressor. The error rate of the proposed compressor is as small as 6.5 %. 8-bit and 16-bit multipliers are designed using the proposed OR compressor and the performance metrics of proposed multipliers are compared with both the exact and existing approximate multipliers. The 8-bit multiplier implemented using the proposed compressor improves the speed by 50.4%, reduces the power by 34.1%, and saves the area by 58.9% with an acceptable error rate of 29.5 %. Further, the 16-bit hybrid multiplier designed using the proposed compressor reduces both PDP value and area by 6.4 % and 41.7 % respectively.
Modelling the influence of rimer surface temperature on the glaciation of intense thunderstorms: The rime–splinter mechanism of ice multiplication

A modelling study is presented to look at the potential importance of ice multiplication in a deep Cb cloud observed during the second Egrett Microphysics Experiment with Radiation, Lidar and Dynamics (EMERALD‐II). Simulations with both a cloud‐resolving model (CRM) and an explicit‐microphysics model (EMM) are used. The CRM has a two‐moment bulk microphysics scheme and resolved cloud dynamics, while the EMM has detailed size‐resolved microphysics, but is driven by the CRM cloud dynamics. The simulations consider the sensitivity of the storms to the rime‐splinter, secondary ice production mechanism (or Hallett–Mossop process).
Laminar Mixed-Convection Heat Transfer in a Lid-Driven Cavity with Modified Heated Wall

In this numerical study, steady laminar mixed-convection heat transfer in a two-dimensional square lid-driven cavity with a modified heated wall is investigated over a range of Richardson numbers, including 0.01, 1, and 10. The heated bottom wall of the cavity is characterized by rectangular, triangular, and sinusoidal wave shapes. The cooled top wall of the cavity is sliding with constant velocity, while the vertical walls are kept stationary and adiabatic. The governing equations are solved using a finite-volume technique. The results are presented in the form of streamlines, isotherms, and Nusselt number plots. The effects of the number of undulations and the amplitude on the flow field and heat transfer are also investigated. The predicted results demonstrate that the heat transfer enhancement is generally observed with the modification of the heated wall, while the improvement is found to be more profound for the case of rectangular wave and at low Richardson number.
An empirical study on the effects of leader behaviour and resilience on innovative work behaviour

As the Fourth Industrial Revolution unfolds, innovation is becoming increasingly critical to ensure business success. Knowledge Workers (KWs) are integral to providing competitive advantage in a knowledge-driven economy. Using a theoretical model developed from existing literature, this study examines the effect of Leader Behaviour (LB) and Resilience (R) on Innovative Work Behaviour (IWB). A survey measuring LB, R and IWB was administered to 120 selected SA KWs. The model was validated using Ordinary Least Squares Regression analysis. It was proven that LB and R have significant, positive relationships with IWB. Influence-based Leadership related positively to IWB Idea Realization (IR), and Transformational Leadership related negatively to IWB IR. Transactional Leadership related positively to R. Influence-based leadership behaviours such as identifying with the KW, sharing expert knowledge, granting work autonomy and providing support for innovation improved IWB. Transactional Leadership describes a task-oriented approach which spells out expectations and grants performance rewards. This cultivates a positive attitude towards challenges and emphasizes beneficial outcomes instead of consequences of failure. Managers should engage in Influence-based Leadership and Transactional Leadership to promote IWB. Transformational Leadership behaviour such as idealized influence, inspirational motivation, intellectual stimulation and individualized consideration should be avoided as it lowered IWB. Respondents showed lower levels of IWB Idea Promotion as compared to IWB Idea Generation and IWB IR. Idea promotion is therefore an area of improvement for KWs.
Large eddy simulation and experimental studies of a confined turbulent swirling flow

Laser Doppler velocimetry (LDV) measurement and large eddy simulation (LES) were used to study confined isothermal turbulent swirling flows in a model dump combustor. The aim was to gain deeper understanding of the flow and turbulence structures in dump combustors and to examine the capability of LES for prediction of turbulent swirling flows. A refractive index matching technique is used in the LDV measurement to improve the near-wall data. A high-order finite difference scheme on Cartesian grids with a scale-similarity subfilter scale model is used in the LES. Turbulent inflow boundary conditions with different energy spectra, different outflow boundary conditions, and grid resolutions are tested in the LES. Three test cases with different swirl numbers and Reynolds numbers are studied in the measurements and the simulations. The Reynolds numbers range from 10 000 to 20 000, and the swirl number is varied from 0 to 0.43. With appropriate inflow, outflow boundary conditions, and fine grid resolution, the LES results are in fairly good agreement with the LDV data. The experimental and numerical results show that turbulence in the dump combustor is highly anisotropic behind the sudden expansion and in the internal recirculation zone near the axis of the combustor. Turbulence decays rapidly along the streamwise direction downstream, and the structure of turbulence depends highly on the level of inlet swirl. At low swirl numbers, turbulence is primarily generated in the shear layer behind the sudden expansion; at high swirl numbers the near axis flow becomes very unstable and vortex breakdown occurs. The shear layer near the axis of the combustor caused by vortex breakdown generates most of the turbulent kinetic energy. Large-scale motions (coherent structures) are found in the near axis vortex breakdown region. A helical flow in the guiding pipe breaks down near the sudden expansion to form a large bubble-like recirculation zone whose center moves slowly around the axis. Downstream of the bubble the core of the rotational large scale azimuthal flow motion is off the combustor axis and rotates around the axis at a frequency about 18-25 Hz (Strouhal number about 0.17-0.4). As the swirl number increases the coherent structure becomes more evident, and the internal recirculation zone moves upstream. LES successfully simulated the vortex breakdown, the internal recirculation zones and the anisotropic turbulence structures for all the swirl numbers considered. (C) 2004 American Institute of Physics.
Longterm X-ray observations of blazars with MAXI

Abstract Longterm continuous X-ray observations of blazars with MAXI are reported. Thanks to its unprecedentedly high sensitivity as an all sky X-ray monitor, MAXI is an ideal observatory to investigate variability of blazars, which should give a clue to particle acceleration in their jet, as well as the jet dynamics. Actually, since it started its operation in the summer of 2009, MAXI has successfully alerted two strong X-ray flares from the BL Lac object Mrk 421. Especially, in one of these flares, the X-ray flux of the object was found to become the highest in history. By closely examining the MAXI data, the physical quantities associated with the flares were estimated. These results clearly demonstrate the potential of MAXI for the variability of blazars.
SN 1978K: An extraordinary supernova in the nearby galaxy NGC 1313

We have identified an extremely luminous radio and X-ray source with an optically variable emission-line object in the outskirts of the nearby barred spiral galaxy NGC 1313. Archival data show that the object (officially designated SN 1978K) had a major optical outburst (M B <−12.3) in mid-1978 and that a radio and X-ray outburst followed this event. The light curve, optical spectra, radio data, and the X-ray data all indicate that the event was a very unusual Type II supernova, with many similar properties to the so-called Type V supernovae SN 1961V in NGC 1058 and SN 1986J in NGC 891