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Spatial disorientation is a leading cause of fatal aircraft accidents. This paper explores the potential of AI agents to aid pilots in maintaining balance and preventing unrecoverable losses of control by offering cues and corrective measures that ameliorate spatial disorientation. A multi-axis rotation system (MARS) was used to gather data from human subjects self-balancing in a spaceflight analog condition. We trained models over this data to create "digital twins" that exemplified performance characteristics of humans with different proficiency levels. We then trained various reinforcement learning and deep learning models to offer corrective cues if loss of control is predicted. Digital twins and assistant models then co-performed a virtual inverted pendulum (VIP) programmed with identical physics. From these simulations, we picked the 5 best-performing assistants based on task metrics such as crash frequency and mean distance from the direction of balance. These were used in a co-performance study with 20 new human subjects performing a version of the VIP task with degraded spatial information. We show that certain AI assistants were able to improve human performance and that reinforcement-learning based assistants were objectively more effective but rated as less trusted and preferable by humans., Comment: 10 pages, To be published in the International Conference on Human-Agent Interaction (HAI '24) proceedings

After receiving an X-ray photon, an X-ray detector is not operational for a duration known as deadtime. It is detector specific and its effect on the data depends upon the luminosity of the source. It reduces the observed photon count rate in comparison to the expected one. In periodic sources such as the Crab pulsar, it can distort the folded light curve (FLC). An undistorted FLC of the Crab pulsar is required in combination with its polarization properties for studying its X-ray emission mechanism. This work derives a simple formula for the distortion of the FLC of a pulsar caused by the detector deadtime, and validates it using Crab pulsar data from the X-ray observatories {{\it NICER}} and {{\it NUSTAR}}, which have very small and relatively large detector deadtimes respectively. Then it derives a method for correcting the distorted FLC of the Crab pulsar in {{\it IXPE}} data, which has intermediate detector deadtime. The formula is verified after addressing several technical issues. This work ends with a discussion of why an undistorted FLC is important for studying the formation of cusps in the FLC of the Crab pulsar., Comment: Accepted by ApJ

The most recent advances in medical imaging that have transformed diagnosis, especially in the case of interpreting X-ray images, are actively involved in the healthcare sector. The advent of digital image processing technology and the implementation of deep learning models such as Convolutional Neural Networks (CNNs) have made the analysis of X-rays much more accurate and efficient. In this article, some essential techniques such as edge detection, region-growing technique, and thresholding approach, and the deep learning models such as variants of YOLOv8-which is the best object detection and segmentation framework-are reviewed. We further investigate that the traditional image processing techniques like segmentation are very much simple and provides the alternative to the advanced methods as well. Our review gives useful knowledge on the practical usage of the innovative and traditional approaches of manual X-ray interpretation. The discovered information will help professionals and researchers to gain more profound knowledge in digital interpretation techniques in medical imaging., Comment: 29 pages, 20 figures

We present a tunable magnetoelastic lattice with a multistable onsite potential, focusing on a tristable potential. Through experimental and numerical analysis, we verify the existence of three types of transition waves with distinct amplitudes and velocities. Additionally, we establish the presence of a scaling law that elucidates various characteristics of these transition waves. By manipulating the onsite potential, we investigate the collision dynamics of two transition waves within the system. In chains featuring an asymmetric potential well, the collision of similar transition waves leads to the remote nucleation of a new phase. In chains with a symmetric potential well, the collision of dissimilar transition waves results in the formation of a stationary domain wall

Introduction: Music generation is a complex task that has received significant attention in recent years, and deep learning techniques have shown promising results in this field. Objectives: While extensive work has been carried out on generating Piano and other Western music, there is limited research on generating classical Indian music due to the scarcity of Indian music in machine-encoded formats. In this technical paper, methods for generating classical Indian music, specifically tabla music, is proposed. Initially, this paper explores piano music generation using deep learning architectures. Then the fundamentals are extended to generating tabla music. Methods: Tabla music in waveform (.wav) files are pre-processed using the librosa library in Python. A novel Bi-LSTM with an Attention approach and a transformer model are trained on the extracted features and labels. Results: The models are then used to predict the next sequences of tabla music. A loss of 4.042 and MAE of 1.0814 are achieved with the Bi-LSTM model. With the transformer model, a loss of 55.9278 and MAE of 3.5173 are obtained for tabla music generation. Conclusion: The resulting music embodies a harmonious fusion of novelty and familiarity, pushing the limits of music composition to new horizons.

Introduction: Automated Lung X-Ray Abnormality Detection System is the application which distinguish the normal x-ray images from infected x-ray images and highlight area considered for prediction, with the recent pandemic a need to have a non-conventional method and faster detecting diseases, for which X ray serves the purpose. Obectives: As of current situation any viral disease that is infectious is potential pandemic, so there is need for cheap and early detection system. Methods: This research will help to eases the work of expert to do further analysis. Accuracy of three different preexisting models such as DenseNet, MobileNet and VGG16 were high but models over-fitted primarily due to black and white images. Results: This led to building up new method such as as V-BreathNet which gave more than 96% percent accuracy. Conclusion: Thus, it can be stated that not all state-of art CNN models can be used on B/W images. In conclusion not all state-of-art CNN models can be used on B/W images.

We study the constraints on primordial magnetic fields (PMFs) in light of the global 21-cm signal observed during the dark ages. Primordial magnetic fields can heat the intergalactic medium (IGM) via magnetohydrodynamic effects. We investigate the impact of magnetic heating on the dark ages global 21-cm signal and constrain the present-day strength of primordial magnetic fields and their spectral indices. Since there were no stars during the Dark Ages, measuring the global 21-cm signal can provide pristine cosmological information. However, detecting this signal using ground-based telescopes is challenging. Several lunar and space-based experiments, such as FARSIDE, DAPPER, and FarView, have been proposed to detect the signal in future. Our findings indicate that measuring the 21-cm global signal during the Dark Ages can provide stronger bounds compared to the existing constraints from Planck 2016. Specifically, the bounds are independent of astrophysical uncertainties and stronger for spectral indices $-2.84 \leq n_B \leq -1.58$. Additionally, we explore the dark-ages consistency ratio, which can identify any non-standard heating of the IGM by measuring the 21-cm signal at only three different redshifts. This approach could complement future experiments aimed at detecting the Dark Ages global 21-cm signal., Comment: 14 pages, 9 figures, Accepted in Physical Review D

While extensive studies have been conducted on purely elastic ribbons, in this paper we explore the influence of magnetisation on the deformation of planar ferromagnetic elastic ribbons. We begin the investigation by deriving the leading-order magnetic energy associated with a curved planar ferromagnetic elastic ribbon. The sum of the magnetic and the elastic energy is the total energy of the ribbon. We derive the equilibrium equations by taking the first variation of the total energy. We then systematically determine and analyse solutions to these equilibrium equations under various canonical boundary conditions. We also analyse the stability of the equilibrium solutions. Comparing our findings with the well-studied Euler's Elastica provides insights into the magnetic effects on the deformation behaviour of elastic ribbons. Our analysis contributes to a deeper understanding of the interplay between magnetisation and the mechanical response of planar ferromagnetic structures, and offers valuable insights for both theoretical and practical applications.

High energy x-ray diffraction microscopy was used to image the microstructure of $\alpha$-Fe before and after a 600 $^\circ$C anneal. These data were used to determine the areas, curvatures, energies, and velocities of approximately 40,000 grain boundaries. The measured grain boundary properties depend on the five macroscopic grain boundary parameters. The velocities are not correlated with the product of the mean boundary curvature and grain boundary energy, usually assumed to be the driving force. Boundary migration is made up of area changes (lateral motion) and translation (normal motion) and both contribute to the total migration. Through the lateral motion component of the migration, low energy boundaries tend to expand in area while high energy boundaries shrink, reducing the average energy through grain boundary replacement. The driving force for this process is not related to curvature and might disrupt the expected curvature-velocity relationship., Comment: 33 pages, double spaced, accepted for publication in Acta Materialia

Consumer complaints are a crucial source of information for companies, policymakers, and consumers alike. They provide insight into the problems faced by consumers and help identify areas for improvement in products, services, and regulatory frameworks. This paper aims to analyze Consumer Complaints Dataset provided by Consumer Financial Protection Bureau (CFPB) and provide insights into the nature and patterns of consumer complaints in the USA. We begin by describing the dataset and its features, including the types of complaints, companies involved, and geographic distribution. We then conduct exploratory data analysis to identify trends and patterns in the data, such as the most common types of complaints, the companies with the highest number of complaints, and the states with the most complaints. We have also performed descriptive and inferential statistics to test hypotheses and draw conclusions about the data. We have investigated whether there are significant differences in the types of complaints or companies involved based on geographic location. Overall, our analysis provides valuable insights into the nature of consumer complaints in the USA and helps stakeholders make informed decisions to improve the consumer experience., Comment: 4 pages, 7 figures, 2 Ttables

We consider a version of the classical group testing problem motivated by PCR testing for COVID-19. In the so-called tropical group testing model, the outcome of a test is the lowest cycle threshold (Ct) level of the individuals pooled within it, rather than a simple binary indicator variable. We introduce the tropical counterparts of three classical non-adaptive algorithms (COMP, DD and SCOMP), and analyse their behaviour through both simulations and bounds on error probabilities. By comparing the results of the tropical and classical algorithms, we gain insight into the extra information provided by learning the outcomes (Ct levels) of the tests. We show that in a limiting regime the tropical COMP algorithm requires as many tests as its classical counterpart, but that for sufficiently dense problems tropical DD can recover more information with fewer tests, and can be viewed as essentially optimal in certain regimes.

Temporal spike recognition plays a crucial role in various domains, including anomaly detection, keyword spotting and neuroscience. This paper presents a novel algorithm for efficient temporal spike pattern recognition on sparse event series data. The algorithm leverages a combination of reward-modulatory behavior, Hebbian and anti-Hebbian based learning methods to identify patterns in dynamic datasets with short intervals of training. The algorithm begins with a preprocessing step, where the input data is rationalized and translated to a feature-rich yet sparse spike time series data. Next, a linear feed forward spiking neural network processes this data to identify a trained pattern. Finally, the next layer performs a weighted check to ensure the correct pattern has been detected.To evaluate the performance of the proposed algorithm, it was trained on a complex dataset containing spoken digits with spike information and its output compared to state-of-the-art.

Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting potential discrepancies between chronological and biological age. To improve understanding of age-related changes in various body parts, we investigate the ageing of the human body on a large scale by using whole-body 3D images. We utilise the Grad-CAM method to determine the body areas most predictive of a person's age. In order to expand our analysis beyond individual subjects, we employ registration techniques to generate population-wide importance maps that show the most predictive areas in the body for a whole cohort of subjects. We show that the investigation of the full 3D volume of the whole body and the population-wide analysis can give important insights into which body parts play the most important roles in predicting a person's age. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance. Finally, we investigate differences between subjects that show accelerated and decelerated ageing.

The high energy emission of rotation powered pulsars is supposed to be produced in "gaps" in the pulsar magnetosphere where charges are accelerated and currents are produced. The rest of the magnetosphere is supposed to be mostly a "force-free" plasma without any currents. Two important currents are the main current that flows away from the pulsar, that produces the observed radiation, and the current that returns to the pulsar to maintain charge neutrality. This work attempts to study the return current in the Crab pulsar using the soft X-ray data from the {\it{NICER}} observatory. It is assumed that the two currents vary as a function of time. This would modulate the electric fields in the "gaps", which would affect the observed X-ray flux. These flux variations will show up only in the on-pulse phases, while those caused by the Crab Nebula, instrumental effects, etc. will be present in the off-pulse phases also. This work obtains the correlation coefficient of the flux variations in the two peaks of the Crab pulsar, after removing the off-pulse flux variations. No correlation was observed; its error of $0.000012$ sets an upper limit of $0.036\%$ on the rms variation of correlated X-ray flux in the Crab pulsar. Reasons exist for the return current variations to be correlated, while the main current variations are probably uncorrelated. So the above number is considered an upper limit on correlated return current variations, which may be an important constraint for pulsar magnetospheric structure.

Understanding the astrophysical nature of the first stars still remains an unsolved problem in cosmology. The redshifted global 21-cm signal $(\text{T}_{21})$ acts as a treasure trove to probe the Cosmic Dawn era -- when the intergalactic medium was mostly neutral. Many experiments, like SARAS 3, EDGES, and DARE have been proposed to probe the cosmic dawn era. However, extracting the faint cosmological signal buried inside a brighter foreground $\mathcal{O}(10^4)$ remains challenging. Thus we use two artificial neural networks, one for extraction of foreground, via parameter estimation with R-square $(R^2)$ score $(0.8034 - 0.9984)$, from the total sky-averaged spectrum. The other is for extraction of a global 21-cm signal in the presence of noise with $R^2$ score $(0.6960 - 0.9978)$. Considering an excess radio background scenario, we constructed all possible $\text{T}_{21}$ signals in the EDGES limit, along with the foreground signal, to train the neural networks. Here, we also explore the variation in parameter estimation due to the presence of heating of intergalactic medium by background radio radiation mediated via Ly$\alpha$ photons from first stars, and we found that the presence and absence of this effect can change the global 21-cm signal estimation by $\sim 33$ mK in the EDGES limit $(\sim -0.5\text{ K})$, Comment: 13 pages, 47 figures, 1 table