bluuebunny/crossref_metadata_2025 huggingface dataset:
New oxide structures clearing up the origin of two-dimensional electron gas in AlGaN/GaN heterostructures
Authors: Wang Zhixiu , Yi Wencai , Cao Yiqing , Miao Maosheng , Liu Jingyao
Published: November 2023

Control over the two-dimensional electron gas (2DEG) in AlGaN/GaN heterostructures is crucial for their practical applications in current semiconducting devices. However, the oxide surface structures inducing 2DEG are still ambiguous because oxide-stoichiometry (OS) matching structures possess occupied surface donor states at 1.0–1.8 eV below the conduction band minimum of bulk but are usually not available in energy than electron counting (EC) rule structures. In this work, a global optimization algorithm was introduced to explore the possible oxidation structures on GaN (0001) and AlN (0001) surfaces; the method was demonstrated to be available due to the fact that the reported oxidized structures were reproduced at each stoichiometry. Interestingly, the two similar oxide structures with close energy were found in each oxide-bilayer, which can be used to clarify the experimental observations of disordered surface oxide layers below 550 °C. Additionally, new stable oxidation structures with low surface energy were proposed. Interestingly, the new OS matching structures are proposed with remarkably lower energy than EC rule structures under cation-rich and oxygen-poor conditions, which is caused by the large formation enthalpy of Al2O3 and Ga2O3. Further electronic structure calculations demonstrate that the new OS structures possess highest occupied states above the half of the gap and are the origin of 2DEG in AlGaN/GaN heterostructures.
Optical and x-ray characterization of the Daedalus ultrafast x-ray imager
Authors: Looker Quinn , Kimmel Mark , Yang Chi , Porter John L.
Published: November 2023

The Daedalus ultrafast x-ray imager is the latest generation in Sandia’s hybrid CMOS detector family. With three frames along an identical line of sight, 1 ns minimum integration time, a higher full well than Icarus, and added features, Daedalus brings exciting new capabilities to diagnostic applications in inertial confinement fusion and high energy density science. In this work, we present measurements of time response, dynamic range, spatial uniformity, pixel cross-talk, and absolute x-ray sensitivity using pulsed optical and x-ray sources. We report a measured 1.5 Me− full well, pixel sensitivity at 9.58 × 10−7 V/e−, and an estimate of spatial uniformity at ∼5% across the sensor array.
Computational study of laser-induced heating of PVDF/nAl composites
Authors: Olsen Daniel , Uhlenhake Kyle , Gomez Mateo , Örnek Metin , Son Steven F. , Zhou Min
Published: November 2023

Optical sources such as lasers provide a means for precise temporal and spatial control of the ignition of energetic materials through customized deposition of excitation energy. Using coupled microscale electromagnetic (EM) and thermal simulations, we analyze the interactions between the EM waves and the microstructures of PVDF/nAl composites with weight fractions of nAl particles (or solids loadings), wf, ranging from 10 to 40 wt. %. Statistically equivalent microstructure sample sets with multiple random microstructure instantiations are generated and used for each solid loading, thereby allowing the statistical variations in the material heating behavior due to microstructure randomness to be analyzed. Maxwell’s equations are solved to characterize the interactions between the materials and EM waves at wavelengths of 266, 532, and 1064 nm. The resulting energy deposition rate is calculated, accounting for Joule heating, dielectric heating, and magnetic induction heating. The coupled thermal analysis accounts for the energy deposition and thermal diffusion, yielding the temperature fields in the materials. The energy deposition and heating are characterized using three measures: the skin depth of the EM wave, the depth of the significant temperature increase in the material, and the average temperature. An empirical relation is developed for the average temperature increase in the heated layer of the material as a function of the intensity of the input laser, solids loading, and time. It is found that trends in the average temperature and depth of significant heating correlate well with the ignition trends observed in experiments.
Multimode vibrational dynamics and orientational effects in fluorescence-encoded infrared spectroscopy. I. Response function theory
Authors: Whaley-Mayda Lukas , Guha Abhirup , Tokmakoff Andrei
Published: November 2023

Fluorescence-encoded infrared (FEIR) spectroscopy is an emerging technique for performing vibrational spectroscopy in solution with detection sensitivity down to single molecules. FEIR experiments use ultrashort pulses to excite a fluorescent molecule’s vibrational and electronic transitions in a sequential, time-resolved manner, and are therefore sensitive to intervening vibrational dynamics on the ground state, vibronic coupling, and the relative orientation of vibrational and electronic transition dipole moments. This series of papers presents a theoretical treatment of FEIR spectroscopy that describes these phenomena and examines their manifestation in experimental data. This first paper develops a nonlinear response function description of Fourier-transform FEIR experiments for a two-level electronic system coupled to multiple vibrations, which is then applied to interpret experimental measurements in the second paper [L. Whaley-Mayda et al., J. Chem. Phys. 159, 194202 (2023)]. Vibrational coherence between pairs of modes produce oscillatory features that interfere with the vibrations’ population response in a manner dependent on the relative signs of their respective Franck–Condon wavefunction overlaps, leading to time-dependent distortions in FEIR spectra. The orientational response of population and coherence contributions are analyzed and the ability of polarization-dependent experiments to extract relative transition dipole angles is discussed. Overall, this work presents a framework for understanding the full spectroscopic information content of FEIR measurements to aid data interpretation and inform optimal experimental design.
High-precision and flexible magnetoelectric sensor operated at 25–330 °C
Authors: Zhao Zeen , Zhao Xuefeng , Liu Yang , Wu Hanzhou , Xing Yisong , Ding Yecheng , Liu Nannan , Wang Yiping , Yang Ying , Wang Yaojin , Yuan Guoliang
Published: November 2023

It is a big challenge to in situ monitor the health status of high-temperature magnetic equipment such as electric motors and generators since it is difficult to achieve a high-temperature magnetoelectric sensor. Here, the Pb(Zr0.52Ti0.48)O3 film with a ferroelectric Curie temperature of 400 °C and the Metglas alloy slice with a magnetic Curie temperature of 430 °C were combined by using a high-temperature inorganic glue to achieve a high-temperature magnetoelectric sensor. The magnetoelectric coefficient αE of the flexible sensor is as high as 104 V/(cm Oe) at 25 °C, 63.6 V/(cm Oe) at 200 °C, and 39.7 V/(cm Oe) at 330 °C. Besides, the magnetic sensor has a detection accuracy of ∼0.3 nT at 25–330 °C. Most importantly, the high-temperature sensor is flexible, high precision, low cost, light weight, and low power consumption simultaneously.
Dislocations and a domains coupling in PbTiO3 thin films
Authors: Cheng Long , Zhang Heng , Xu Ran , Co Kevin , Guiblin Nicolas , Otoničar Mojca , Paillard Charles , Wang Yujia , Dkhil Brahim
Published: November 2023

The interaction of domain structure and defects in ferroelectric thin films has been studied for decades. However, the role of dislocations and thermal stability of microstructures is still poorly studied. By combining transmission electron microscopy, x-ray diffraction experiments, and phase-field simulations, we show that dislocation pairs induced by post-annealing above 550 °C provide a stress field stabilizing a domains in 30 nm thick tetragonal PbTiO3 films on SrTiO3 substrate, initially exhibiting pure c domains. Based on phase-field simulations, we further discuss the effects of single dislocations and dislocation pairs on the nucleation of a-domains and the occurrence of non-ferroelastic 180° domains. Dislocations, and the possibility to tune them using an appropriate thermal annealing process, offer a path for modulating the domains and domain wall states and, thus, the physical properties of ferroelectric films.
The thermo-viscous fingering instability of a cooling spreading liquid dome
Authors: Algwauish Ghanim , Naire Shailesh
Published: November 2023

We investigate a theoretical model of a molten viscous planar liquid dome spreading under gravity over an inclined substrate. The liquid in the dome cools as it spreads, losing its heat to the surrounding colder air and substrate. Coupled nonlinear evolution equations for the dome's thickness and temperature describing the spreading flow are derived employing the lubrication approximation. The coupling between the flow and cooling is via a temperature-dependent viscosity. For intermediate Péclet numbers, a new one-dimensional free surface shape is identified. In this solution, the hotter and more mobile liquid piles up behind the dome's colder and less mobile leading edge, forming a distinct elevated ridge at the flow front. The ridge solution is mapped in parameter space. The transverse stability of the one-dimensional ridge solution is investigated using linear stability analysis and numerical simulations. The existence of a thermo-viscous fingering instability is revealed. For this instability to occur, the presence of the ridge is shown to be necessary. Two-dimensional simulations confirm the stability analysis elucidating the underlying thermo-viscous mechanism.
Supersonic turbulent boundary layer on a plate. II. Flow in the wall region and the Crocco integral
Authors: Vigdorovich Igor
Published: November 2023

We develop an asymptotic theory of the compressible turbulent boundary layer on a flat plate, in which the mean velocity and temperature profiles can be obtained as exact asymptotic solutions of the boundary-layer equations, which are closed using functional relations of a general form connecting the turbulent shear stress and turbulent enthalpy flux to mean velocity and enthalpy gradients. In this part of the study, we consider the near-wall region that consists of viscous and logarithmic sublayers. The solution is constructed in the form of expansions in a small parameter ε that is proportional to the Mach number formed with the friction velocity and the speed of sound on the wall. Three characteristic flow regimes are possible in the viscous sublayer, which occur at small (including zero), moderate, and large negative wall heat flux. For the first two regimes, the flow is incompressible to the first approximation, while the compressibility is significant in the viscous sublayer on a strongly cooled plate. The Crocco integral in the logarithmic region is obtained, which in the zeroth-order approximation in ε gives the Waltz equation, but in contrast to it, the new relation describes well the dependence of temperature on velocity for any heat flux on the wall. Along with the constants known for incompressible flow, the theory contains two new universal constants, which are determined from a comparison with direct numerical simulation data for velocity and temperature.
Sloshing reduction in microgravity with passive baffles: Design, performance, and supplemental thermocapillary control
Authors: Peromingo C. , Salgado Sánchez P. , Gligor D. , Bello A. , Rodríguez J.
Published: November 2023

A detailed analysis of the design and performance of passive baffles for sloshing reduction in microgravity is presented. Sloshing dynamics are investigated for a rectangular container holding a L×H=30 × 15 mm2 volume of liquid with properties similar to a 5 cSt silicone oil. The system response to a pulse-like perturbation is analyzed in terms of the sloshing frequency ω, decay time τd, and damping ratio ξ=γ/γ2+ω2, characterizing the decay rate γ∝τd−1 relative to ω. We explore first simple rectangular baffles, parameterized in terms of their length and height, orientation, and position of their center, finding that the vertical centered baffle is optimal for its good performance and simplicity. The analysis is further extended to other designs of higher complexity, including multiple-baffle arrangements, cross-shaped baffles, and free surface baffles. Finally, motivated by the recent work of Peromingo et al. [“Sloshing reduction in microgravity: thermocapillary-based control and passive baffles,” Phys. Fluids 35, 102114 (2023)], we also demonstrate the effectiveness of passive baffles combined with active thermocapillary control. As a whole, the present results suggest a maximum achievable sloshing reduction of approximately 90%.
Anderson transition in compositionally graded p-AlGaN
Authors: Rathkanthiwar Shashwat , Reddy Pramod , Quiñones Cristyan E. , Loveless James , Kamiyama Masahiro , Bagheri Pegah , Khachariya Dolar , Eldred Tim , Moody Baxter , Mita Seiji , Kirste Ronny , Collazo Ramón , Sitar Zlatko
Published: November 2023

Mg-doped, graded AlGaN films showed the formation of an impurity band and high, temperature-invariant p-conductivity even for doping levels well below the Mott transition. However, compensating point defects disrupted the impurity band, resulting in an Anderson transition from the impurity band to valence band conduction and a more than tenfold reduction in room-temperature conductivity. This is the first demonstration of Anderson-like localization in AlGaN films.
Inverse design of graded phononic materials via ray tracing
Authors: Dorn Charles , Kochmann Dennis M.
Published: November 2023

We present a framework for the inverse design of spatially graded phononic materials based on ray tracing. Spatial grading of phononic materials allows the unit cell to vary smoothly in space. Compared to periodic architectures, spatial grading opens up a vast design space that promises new capabilities for manipulating elastic and acoustic waves. However, the use of spatial grading to control wave propagation has been limited to simple gradings, largely due to the bottleneck of modeling efficiency, and to the long-wavelength limit of low frequencies. In this work, ray tracing is exploited as an efficient alternative, around which we develop an optimization framework based on the adjoint state method toward the flexible inverse design of graded phononic materials. We demonstrate the design of graded mass-spring networks for objectives including focusing all directions of a point source, broadband focusing of a plane wave, and frequency sorting. These objectives are out of reach of periodic phononic materials, highlighting the high potential of spatially graded phononic materials. Moreover, our results demonstrate the importance of wave dispersion, which is classically neglected in the long-wavelength limit of elastodynamics.
Robust cryogenic matched low-pass coaxial filters for quantum computing applications
Authors: Ivanov Anton I. , Polozov Victor I. , Echeistov Vladimir V. , Samoylov Andrey A. , Malevannaya Elizaveta I. , Matanin Aleksei R. , Smirnov Nikita S. , Rodionov Ilya A.
Published: November 2023

Electromagnetic noise is one of the main external factors decreasing superconducting qubit coherence. Matched coaxial filters can prevent microwave and IR photons' negative influence on superconducting quantum circuits. In this report, we describe the design and fabrication process of matched coaxial filters for noise-sensitive measurements at millikelvin temperatures. A robust transmission coefficient and ultralow reflection loss of −20 dB in the frequency range up to 20 GHz is achieved. Fabricated low-pass filters have linear transmission and reflection characteristics with 3 dB-cutoff frequency of 1.5–2.5 GHz. A method for extracting the propagation constant and filter impedance from scattering parameter measurements is demonstrated. This method is experimentally approved on a filter with a compound of Cu powder and Stycast epoxy resin and a filter filled with ECCOSORB CR-110 epoxy resin. The proposed design and assembly technology are versatile for various compounds and provide highly repeatable geometric and microwave characteristics. Furthermore, we demonstrate that these low-pass coaxial filters can be effectively utilized to improve superconducting qubit relaxation due to suppressing standing waves originating from reflections in control coaxial cables.
Curare le donne nell’Egitto greco-romano
Authors: Reggiani Nicola
Published: November 2023

The contribution examines the attestations of
women-related diseases and therapies in the Greek papyri from Hellenistic
and Roman Egypt, connecting them to the progress of Hellenic gynaecological
medicine from Hippocrates to Soranus.
Realidad de la institución matrimonial en la ficción de las novelas
grecolatinas
Authors: Casinos Mora F. Javier
Published: November 2023

The purpose of this work is to make evident how the
informations about various aspects of the marriage institution contained in
the Greco-Latin novels are not purely fictional data, but approximately
respond to historical reality and are reliable enough to be contributed to
the task of rebuilding a legal institution such as marriage in the
particular eastern historical and geographical context of late Greco-Roman
antiquity.
La expositio en Dafnis y Cloe
Authors: Zaera García Ana Belén
Published: November 2023

From a legal point of view, we can affirm that the
stories of the protagonists of Longo’s novel, Daphnis and Chloe, show the
legal situation of the time, adapting the novel to local law. The literary
fiction is not an obstacle to recognise that in the juridical field the work
shows tints of reality, which proves that this literary genre constitutes a
source of knowledge of Greek law.
Chloe as learning subject in Longus’ Daphnis and
Chloe
Authors: Downie Janer
Published: November 2023

Setting the erotic education of Daphnis and Chloe in the
context of contemporary discourses of paideia, this essay
argues that it is Chloe who represents the ideal learning subject. While
Daphnis is associated with the language of teaching and training
(διδάσκω,
παιδεύω), Chloe is associated with the language of
learning and understanding (μανθάνω). When her learning process is
depicted in the novel’s inset scenes of mythological story-telling, Chloe
repeatedly rejects or eludes hierarchical and transactional frameworks of
education, modeling a reflective self-awareness and intellectual agency that
separates her from Daphnis and connects her instead with adult figures like
the Hunter-Narrator and with the novel’s implied reader.
Le collane di Charicleia
Authors: Berg Ria
Published: November 2023

In questo articolo si esamina il ruolo dei gioielli,
oggetti femminili par excellence, nei romanzi greco-romani.
Ci si chiederà chi sono i personaggi distinti dall’uso di gioielli e, in
conseguenza, quale sia il loro significato nella costruzione della loro
identità. Si analizzerà, inoltre, se è possibile identificare
archeologicamente le tipologie dei gioielli usati nelle narrative.
Kidnapping in the ancient novels
Authors: Papaioannou Sophia
Published: November 2023

Kidnapping is an essential component of Ancient novel
narrative because it is a major force behind the development of the plot.
Additionally, kidnapping enables the author to experiment with travelling as
a literary mechanism to signify transition (kidnapping initiates travelling
which allows for convenient transition points, and supplies closures and
openings to narrative units), but also to employ travelling as an allegory
for soul-/identitysearching, as a means to trace characterization and as a
rite of passage onto maturity.
“Parthian” women in Vīs and Rāmīn
Authors: Gregoratti Leonardo
Published: November 2023

A few decades ago in a series of articles, the Russian
orientalist Vladimir Minorsky argued that the Persian romance novel Vīs u
Rāmīn by As’ad Gurgānī re-elaborated materials derived from the court
minstrel tradition of the Parthian period noble houses. His assumption was
mainly based on the political system described in the poem. Enlarging the
scope of the investigation this paper aims at connecting what is known about
the role of women at the Arsacid court with the prominent role of women as
decision makers in the novel.
Radiation fluxes of gravitational, electromagnetic, and scalar perturbations in type-D black holes: an exact approach
Authors: Chen Changkai , Jing Jiliang
Published: November 2023

We present a novel method that solves Teukolsky equations with the source to calculate radiation fluxes at infinity and event horizon for any perturbation fields of type-D black holes. For the first time, we use the confluent Heun function to obtain the exact solutions of ingoing and outgoing waves for the Teukolsky equation. This benefits from our derivation of the asymptotic analytic expression of the confluent Heun function at infinity. It is interesting to note that these exact solutions are not subject to any constraints, such as low-frequency and weak-field. To illustrate the correctness, we apply these exact solutions to calculate the gravitational, electromagnetic, and scalar radiations emitted by a particle in circular orbits around a Schwarzschild black hole. Numerical results show that the proposed exact solution appreciably improves the computational accuracy and efficiency compared with the 23rd post-Newtonian order expansion and the Mano-Suzuki-Takasugi method.
Probing the equation of state of the early Universe with pulsar timing arrays
Authors: Liu Lang , Chen Zu-Cheng , Huang Qing-Guo
Published: November 2023

The recently released data by pulsar timing array (PTA) collaborations have amassed substantial evidence corroborating the existence of a stochastic signal consistent with a gravitational-wave background at frequencies around the nanohertz regime. We investigate the situation in which the PTA signal originates from scalar-induced gravitational waves (SIGWs), which serves as a valuable tool to probe the equation of state parameterwduring the Universe's early stages. The joint consideration of the PTA data from the NANOGrav 15-year data set, PPTA DR3, and EPTA DR2 yields thatw = 0.60+0.32-0.39(median + 90% credible interval), indicating a period of condensate domination at the production of SIGWs is allowed by the data. Moreover, the data also supports radiation domination (w = 1/3) within the 90% credible interval. We also impose an upper bound on the reheating temperature thatTrh ≲ 0.2 GeV and the constraint onwreveals valuable information on the inflationary potential and the dynamics at the end of inflation.
Redundant representations help generalization in wide neural networks
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,
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Authors: Doimo Diego , Glielmo Aldo , Goldt Sebastian , Laio Alessandro
Published: November 2023

Deep neural networks (DNNs) defy the classical bias-variance trade-off; adding parameters to a DNN that interpolates its training data will typically improve its generalization performance. Explaining the mechanism behind this ‘benign overfitting’ in deep networks remains an outstanding challenge. Here, we study the last hidden layer representations of various state-of-the-art convolutional neural networks and find that if the last hidden representation is wide enough, its neurons tend to split into groups that carry identical information and differ from each other only by statistically independent noise. The number of these groups increases linearly with the width of the layer, but only if the width is above a critical value. We show that redundant neurons appear only when the training is regularized and the training error is zero.
The dynamics of representation learning in shallow, non-linear autoencoders
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Authors: Refinetti Maria , Goldt Sebastian
Published: November 2023

Autoencoders are the simplest neural network for unsupervised learning, and thus an ideal framework for studying feature learning. While a detailed understanding of the dynamics of linear autoencoders has recently been obtained, the study of non-linear autoencoders has been hindered by the technical difficulty of handling training data with non-trivial correlations—a fundamental prerequisite for feature extraction. Here, we study the dynamics of feature learning in non-linear, shallow autoencoders. We derive a set of asymptotically exact equations that describe the generalisation dynamics of autoencoders trained with stochastic gradient descent (SGD) in the limit of high-dimensional inputs. These equations reveal that autoencoders learn the leading principal components of their inputs sequentially. An analysis of the long-time dynamics explains the failure of sigmoidal autoencoders to learn with tied weights, and highlights the importance of training the bias in ReLU autoencoders. Building on previous results for linear networks, we analyse a modification of the vanilla SGD algorithm, which allows learning of the exact principal components. Finally, we show that our equations accurately describe the generalisation dynamics of non-linear autoencoders trained on realistic datasets such as CIFAR10, thus establishing shallow autoencoders as an instance of the recently observed Gaussian universality.
Self-consistent dynamical field theory of kernel evolution in wide neural networks
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Authors: Bordelon Blake , Pehlevan Cengiz
Published: November 2023

We analyze feature learning in infinite-width neural networks trained with gradient flow through a self-consistent dynamical field theory. We construct a collection of deterministic dynamical order parameters which are inner-product kernels for hidden unit activations and gradients in each layer at pairs of time points, providing a reduced description of network activity through training. These kernel order parameters collectively define the hidden layer activation distribution, the evolution of the neural tangent kernel (NTK), and consequently, output predictions. We show that the field theory derivation recovers the recursive stochastic process of infinite-width feature learning networks obtained by Yang and Hu with tensor programs. For deep linear networks, these kernels satisfy a set of algebraic matrix equations. For nonlinear networks, we provide an alternating sampling procedure to self-consistently solve for the kernel order parameters. We provide comparisons of the self-consistent solution to various approximation schemes including the static NTK approximation, gradient independence assumption, and leading order perturbation theory, showing that each of these approximations can break down in regimes where general self-consistent solutions still provide an accurate description. Lastly, we provide experiments in more realistic settings which demonstrate that the loss and kernel dynamics of convolutional neural networks at fixed feature learning strength are preserved across different widths on a image classification task.
Phase diagram of stochastic gradient descent in high-dimensional two-layer neural networks
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Authors: Veiga Rodrigo , Stephan Ludovic , Loureiro Bruno , Krzakala Florent , Zdeborová Lenka
Published: November 2023

Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent. The picture can be radically different for narrow networks, which tend to get stuck in badly-generalizing local minima. Here we investigate the cross-over between these two regimes in the high-dimensional setting, and in particular investigate the connection between the so-called mean-field/hydrodynamic regime and the seminal approach of Saad & Solla. Focusing on the case of Gaussian data, we study the interplay between the learning rate, the time scale, and the number of hidden units in the high-dimensional dynamics of stochastic gradient descent (SGD). Our work builds on a deterministic description of SGD in high-dimensions from statistical physics, which we extend and for which we provide rigorous convergence rates.
Exact solutions of a deep linear network
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Authors: Ziyin Liu , Li Botao , Meng Xiangming
Published: November 2023

This work finds the analytical expression for the global minima of a deep linear network with weight decay and stochastic neurons, a fundamental model for understanding the landscape of neural networks. Our result implies that the origin is a special point in the deep neural network loss landscape where highly nonlinear phenomenon emerge. We show that weight decay strongly interacts with the model architecture and can create bad minima at zero in a network with more than one hidden layer, qualitatively different from a network with only one hidden layer. Practically, our result implies that common deep learning initialization methods are generally insufficient to ease the optimization of neural networks.
Learning sparse features can lead to overfitting in neural networks
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Authors: Petrini Leonardo , Cagnetta Francesco , Vanden-Eijnden Eric , Wyart Matthieu
Published: November 2023

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it is beneficial for modern architectures to be trained to classify images, whereas it is detrimental for fully-connected networks to be trained on the same data. Here, we propose an explanation for this puzzle, by showing that feature learning can perform worse than lazy training (via the random feature kernel or the neural tangent kernel) as the former can lead to a sparser neural representation. Although sparsity is known to be essential for learning anisotropic data, it is detrimental when the target function is constant or smooth along certain directions of the input space. We illustrate this phenomenon in two settings: (i) regression of Gaussian random functions on the d-dimensional unit sphere and (ii) classification of benchmark data sets of images. For (i), we compute the scaling of the generalization error with the number of training points and show that methods that do not learn features generalize better, even when the dimension of the input space is large. For (ii), we show empirically that learning features can indeed lead to sparse and thereby less smooth representations of the image predictors. This fact is plausibly responsible for deteriorating the performance, which is known to be correlated with smoothness along diffeomorphisms.
Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for COVID-19 Detection
Authors: Saleh Basma Jumaa , Omar Zaid , Bhateja Vikrant , Izhar Lila Iznita
Published: November 2023

During the COVID-19 pandemic, medical imaging techniques like computed tomography (CT) scans have demonstrated effectiveness in combating the rapid spread of the virus. Therefore, it is crucial to conduct research on computerized models for the detection of COVID-19 using CT imaging. A novel processing method has been developed, utilizing radiomic features, to assist in the CT-based diagnosis of COVID-19. Given the lower specificity of traditional features in distinguishing between different causes of pulmonary diseases, the objective of this study is to develop a CT-based radiomics framework for the differentiation of COVID-19 from other lung diseases. The model is designed to focus on outlining COVID-19 lesions, as traditional features often lack specificity in this aspect. The model categorizes images into three classes: COVID-19, non-COVID-19, or normal. It employs enhancement auto-segmentation principles using intensity dark channel prior (IDCP) and deep neural networks (ALS-IDCP-DNN) within a defined range of analysis thresholds. A publicly available dataset comprising COVID-19, normal, and non-COVID-19 classes was utilized to validate the proposed model’s effectiveness. The best performing classification model, Residual Neural Network with 50 layers (Resnet-50), attained an average accuracy, precision, recall, and F1-score of 98.8%, 99%, 98%, and 98% respectively. These results demonstrate the capability of our model to accurately classify COVID-19 images, which could aid radiologists in diagnosing suspected COVID-19 patients. Furthermore, our model’s performance surpasses that of more than 10 current state-of-the-art studies conducted on the same dataset.
Silver Printed Electrode on Poly-Imide (Pi) Film And Polyethylene Naphthalate (PEN) Film For ECG Monitoring Device: A Case Study
Authors: Abdul-Kadir Nurul Ashikin , Daud Nurizzati Mohd , Saleh Syaidah Md , Sahar Noor Syazwana , Harun Fauzan Khairi Che , Embong Abdul Mutalib , Khee Moey Kar , Seman Mohd Khairy
Published: November 2023

According to World Health Organization (WHO), cardiovascular disease (CVD) is among the tenth of primary global cause of death in 2017. However, this problem could be alleviated via early detections, especially for patients with a history of heart-related problems. In addition, ECG monitoring could reduce the cause of CVD if diagnose earlier thus reduce the risk of sudden death. The measurement of ECG signals is important to assist physicians about people heart and health conditions. The sophisticated characteristics of ECG signals could be acquired by using the good electrodes. Thus, electrodes play a vital role in ECG recording. The commercially available electrodes are the conventional wet electrode (gel) using silver/silver chloride (Ag/AgCl). The gel usage tends to dry gradually and could cause skin problems like dermal inflammation and skin irritation. Due to the limitations associated with conventional gelled electrode, dry electrode is expected to overcome the main limit of Ag/AgCl electrode. By fabricating the silver ink onto the polyimide (PI) and polyethylene naphthalate (PEN) substrates to produce a dry ECG sensor, which could withstand high temperature, reusable, and has been shown to be biocompatible. This study is a case study to measure the performance of dry electrode based by silver printed electrode on PI and PEN films in ECG application.
Heat distribution improvement with the implementation of polyethylene-covered water bolus into breast cancer hyperthermia
Authors: Wong Vei Ling , Lias Kasumawati bt , Basri Hazrul Mohamed , Buniyamin Norlida
Published: November 2023

This paper presents the implementation of polyethylene-covered water bolus into a non-invasive breast cancer hyperthermia applicator. This modified hyperthermia applicator is introduced to improve the performance of hyperthermia by reducing or removing unwanted hotspots during hyperthermia treatment. This simulation-based experiment is carried out to observe the heating distribution of hyperthermia with water bolus coated by three different thicknesses of polyethylene cover or layer, which are 0.5mm, 0.8mm and 1.0mm. The solvent used in the water bolus is distilled water. The 915MHz microstrip antenna as a hyperthermia applicator and stage 2 breast cancer with a cancer depth of 28.6 mm to 73.6 mm is selected for this study. Based on the results, with the modified HTP integrated by water bolus, the heat pattern of hyperthermia simulation becomes more concentrated into the targeted cancer region, and unwanted hotspots nearby the skin area of breast tissue are removed. The 0.5mm thick polyethylene cover showed the best results with a focusing region between 29.4mm to 69.4mm compared with the result of hyperthermia without implemented water bolus, which heated the 26.6mm to 67.3mm region.
Comparison between Romberg test with sensor-based balance assessment using electronic wobble board
Authors: Woon Yvonne Khor Yee , Su Eileen L. M. , Khor Kang Xiang , Abdullah Muhammad Najib Bin , Yeong Che Fai
Published: November 2023

The number of fall-related injuries resulting in hospital admissions could place a significant load on hospitals. Prior research revealed that balance impairment is the most reliable indicator of future falls, and postural instability testing is expensive and tedious to be applied in clinical settings. This paper presents an objective based balance assessment method using an electronic wobble board to examine the balance ability among individuals, with minimal specialist intervention. The balancing assessment software modules provide visual feedback such as visual concentration, test procedure teaching, and time counting. Quick objective balance evaluation is facilitated by the software modules, which also can comprise visual feedback and provides instant biofeedback with objective measurements of user’s progress. The balance assessment score obtained from this electronic wobble board was compared against physiotherapists’ ratings using the Romberg test. The results indicated that the sensor-based balance score is comparable with the expert ratings using the Romberg test. Additionally, the sensor-based score provides higher sensitivity in differentiating among similar categories of subjects as compared to the conventional Romberg test.
Classification of Red Blood Cell Abnormality in Thin Blood Smear Images using Convolutional Neural Networks
Authors: Muhammad Fatima Abdullahi , Sudirman Rubita , Zakaria Nor Aini , Mahmood Nasrul Humaimi
Published: November 2023

One of the most common morphological red blood cell abnormalities encountered during routine thin blood smear microscopy for the detection of malaria parasite is the rouleaux formation, which is the stacking together of red blood cells to form a chain. Rouleaux formation signifies an underlying infection and as such microscopists are mandated to report its presence. A lot of work has been done in automating malaria diagnosis using deep learning, but no model has been developed which is capable of detecting rouleaux formation in malaria infected red blood cells. Thus, this study collected 231 peripheral blood smear (PBS) images of normal red blood cell morphology and 231 PBS images with rouleaux morphology. The images were pre-processed and segmented into equal instances of 3044 coloured images of size 750×750 pixels. Two convolutional neural network (CNN) models were developed and trained to classify the images into normal red blood cell morphology or rouleaux morphology. The CNN models were trained on two different image sizes: 300×300 and 500×500. The first CNN model achieved validation accuracy/loss values of 87.91%/0.8177 and 56.58%/1.4090 when trained on images of sizes 300×300 and 500×500 respectively. In the second CNN model, the CNN layers of the first model were replaced with depthwise separable CNN layers, it was also trained on images of sizes 300×300 and 500×500 achieving validation accuracy/loss values of 90.95%/0.2804 and 87.75%/0.5904 respectively. This study demonstrates the capability of CNN models in detecting red blood cell morphology abnormality in thin smear images at an optimal image size of 300×300.
An Optimized Cosine Similarity-Based Attention Gate for Temporal Sequence Patterns Recognition
Authors: Abdullah Ammar , Abdul-Kadir Nurul Ashikin , Harun Fauzan Khairi Che
Published: November 2023

The attention gate is often employed in various applications, such as recommender systems, to determine the correct context of the input data. Adopting the attention gate has been proven to improve prediction accuracy successfully. However, for a temporal sequence problem such as sensor-based Sign Language Recognition (SLR), it is still challenging to integrate the attention gate into the solution since the processing input is only in the form of a sine waveform. In our work, we propose an optimized cosine similarity-based attention gate to manipulate the sine wave signal while improving the recognition of temporal sequence data. We also evaluate and compare the various distance calculations and determine the best one for the SLR application. Adopting a distance-based attention gate has successfully achieved the recognition accuracy of 99% while reducing the error rate to 5%.
Position correction of mobile robot by detecting line landmark using Hough line transform
Authors: Anggoro Bagus I. J. , Setiawan Iwan , Nurhayati Oky Dwi
Published: November 2023

Mobile robots are used in a laboratory automation system to transport specimens or experiments to their destinations. When moving, the robot has some error positions because of the wheels skid on the floor and the inaccuracy direction of the robot. The robot needs to correct its position and return to the track. By reading the landmarks in the environment, the robot can correct its position. This research proposes to do the position correction of the robot using a camera by reading the orange lines as an artificial landmark on the laboratory environment floor. The camera detects the orange lines as two parallel edges. And by Hough line transform (HLT) it produces Δρ value between line edges. The robot calculates the error position using the Δρ value and the biggest ρ (ρMAX) as the distance prediction robot to the line. Compared to the original robot movement on the Webots simulator application, this method can help the robot keep its position on the track. On the goal position, the robot can reduce 90% the error position. This error improvement makes the error position of the robot becomes around 5 mm from the target position.
Siamese Networks for Speaker Identification on Resource-Constrained Platforms
Authors: Jie Lim Jun , Zabidi Muhammad Mun’im Ahmad , Sadiah Shahidatul , Rahman Ab Al-Hadi Ab
Published: November 2023

This paper investigates the implementation of a lightweight Siamese neural network for enhancing speaker identification accuracy and inference speed in embedded systems. Integrating speaker identification into embedded systems can improve portability and versatility. Siamese neural networks achieve speaker identification by comparing input voice samples to reference voices in a database, effectively extracting features and classifying speakers accurately. Considering the trade-off between accuracy and complexity, as well as hardware constraints in embedded systems, various neural networks could be applied to speaker identification. This paper compares the incorporation of CNN architectures targeted for embedded systems, MCUNet, SqueezeNet and MobileNetv2, to implement Siamese neural networks on a Raspberry Pi. Our experiments demonstrate that MCUNet achieves 85% accuracy with a 0.23-second inference time. In comparison, the larger MobileNetv2 attains 84.5% accuracy with a 0.32-second inference time. Additionally, contrastive loss was superior to binary cross-entropy loss in the Siamese neural network. The system using contrastive loss had almost 68% lower loss scores, resulting in a more stable performance and more accurate predictions. In conclusion, this paper establishes that an appropriate lightweight Siamese neural network, combined with contrastive loss, can significantly improve speaker identification accuracy, and enable efficient deployment on resource-constrained platforms.
3D Brain Tumour Segmentation Using UNet with Quantitative Analysis of the Tumour Features
Authors: Azeez Ibrahim Izdhan , Chow Li Sze , Solihin Mahmud Iwan , Ang Chun Kit
Published: November 2023

Brain tumours are abnormal cells that grow within the brain and may cause severe harm to an individual. The diagnosis of tumours often requires a manual review of Magnetic Resonance (MR) images by a skilled radiologist, which is a laborious task. The study aims to use a deep learning-based approach to segment the brain tumour and quantitatively evaluate the performance of the model by comparing the statistical distribution of five textural and four non-textural feature differences between the ground truth and the prediction. Textural features were extracted from a Grey Level Co-occurrence Matrix (GLCM) and averaged over four different directions. A UNet model was constructed with MONAI using BraTS 2020 dataset. Due to hardware constraints, the model was trained over a short epoch range of 100 but managed to achieve a mean Dice similarity coefficient (DSC) of 0.86. The quantitative analysis showed that the ground truth and the prediction had low differences in most features, except for percentage variation in signal intensity, energy, correlation and Hausdorff distance, which could be attributed to false positive voxels that are far from the ground truth. The textural properties of homogeneity, entropy, and correlation had an average absolute percentage difference of less than 5% from the ground truth.
Classification of COVID-19 and lung opacity using vision transformer on chest x-ray images
Authors: Toroghi Manoochehr Noghanian , Sheikh Usman Ullah , Irani Shima Shahi
Published: November 2023

There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT’s is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.
Graphite Paper-based Device for Energy Storage Application
Authors: Abidin Mastura Shafinaz Zainal , Razali Muhammad Ameruddin , Peng Tan Jin
Published: November 2023

The demand for versatile flexible energy storage device has arisen due to their potential, application in wearable and portable electronic product. This study presents the characterization of graphite paper-based capacitor and supercapacitor that are produced utilising commercially available pencils to draw a design by developing cheap, green, and reliable low profile electrical electrodes. The objectives of this study are to design graphite paper-based electrodes for energy storage application, characterize the physical properties of the electrodes and electrical properties of the device. Material characterization have been performed including morphology analysis of the electrodes using field emission scanning electron microscope (FESEM), compositional structures by using Energy dispersive X-ray spectroscopy (EDX) and chemical properties of the graphitic electrodes using Raman spectroscopy. Next, the electrical performance of the pencil drawn capacitor and supercapacitor have been performed. It is found that the Acidic G/PANI-Paper supercapacitor sample shows the best electrical performance in terms of lowest resistance with 0.59 MΩ, 1.12 MΩ, 1.8 MΩ and highest capacitance with 27.5 μF, 38.1 μF, 55.25 μF for respective 6 cm2, 8 cm2 and 10 cm2 area size. These characteristics show that the graphite-paper based device has a great potential in flexible energy storage applications.