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This paper investigates the use of autoencoders and machine learning methods for detecting and analyzing quantum phase transitions in the Two-Component Bose-Hubbard Model. By leveraging deep learning models such as autoencoders, we investigate latent space representations, reconstruction error analysis, and cluster distance calculations to identify phase boundaries and critical points. The study is supplemented by dimensionality reduction techniques such as PCA and t-SNE for latent space visualization. The results demonstrate the potential of autoencoders to describe the dynamics of quantum phase transitions., Comment: 24 pages, 7 figures

This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

We present a new approach to bootstrapping string-like theories by exploiting a local crossing symmetric dispersion relation and field redefinition ambiguities. This approach enables us to use mass-level truncation and to go beyond the dual resonance hypothesis. We consider both open and closed strings, focusing mainly on open tree-level amplitudes with integer-spaced spectrum, and two leading Wilson coefficients as inputs. Using entanglement minimization in the form of the minimum of the first finite moment of linear entropy or entangling power, we get an excellent approximation to the superstring amplitudes, including the leading and sub-leading Regge trajectories. We find other interesting S-matrices which do not obey the duality hypothesis, but exhibit a transition from Regge behaviour to power law behaviour in the high energy limit. Finally, we also examine Machine-Learning techniques to do bootstrap and discuss potential advantages over the present approach., Comment: 48 pages, 21 figures

Astrophysical and cosmological observations strongly suggest the existence of Dark Matter. However, it's fundamental nature is still elusive. Collider experiments at Large Hadron Collider (LHC) offer a promising way to reveal the particle nature of the dark matter. In such an endeavour, we investigate the potential of the mono-Higgs plus missing $E_T$ signature at the LHC to search for dark matter. Without going in a particular Ultra-Violet complete model of dark matter, we have used the framework of Effective Field Theory to describe the dynamics of a relatively light fermionic dark matter candidate, which interacts with the Standard Model via dimension-6 and dimension-7 operators involving the Higgs and the gauge bosons. Both cut-based and Boosted Decision Tree (BDT) algorithms have been used to extract the signal for dark matter production over the Standard Model backgrounds, assuming an integrated luminosity of $3000~fb^{-1}$ at $\sqrt{s}~=~14$ TeV at the High Luminosity phase of the LHC (HL-LHC). The BDT is seen to separate the dark matter signal at $5\sigma$ significance, for masses below 200 GeV, showcasing the prospects of this search at the HL-LHC., Comment: 19 Pages, 8 Figures, 8 Tables

Ophthalmic diseases represent a significant global health issue, necessitating the use of advanced precise diagnostic tools. Optical Coherence Tomography (OCT) imagery which offers high-resolution cross-sectional images of the retina has become a pivotal imaging modality in ophthalmology. Traditionally physicians have manually detected various diseases and biomarkers from such diagnostic imagery. In recent times, deep learning techniques have been extensively used for medical diagnostic tasks enabling fast and precise diagnosis. This paper presents a novel approach for ophthalmic biomarker detection using an ensemble of Convolutional Neural Network (CNN) and Vision Transformer. While CNNs are good for feature extraction within the local context of the image, transformers are known for their ability to extract features from the global context of the image. Using an ensemble of both techniques allows us to harness the best of both worlds. Our method has been implemented on the OLIVES dataset to detect 6 major biomarkers from the OCT images and shows significant improvement of the macro averaged F1 score on the dataset., Comment: 5 pages

The perception module in autonomous vehicles (AVs) relies heavily on deep learning-based models to detect and identify various objects in their surrounding environment. An AV traffic sign classification system is integral to this module, which helps AVs recognize roadway traffic signs. However, adversarial attacks, in which an attacker modifies or alters the image captured for traffic sign recognition, could lead an AV to misrecognize the traffic signs and cause hazardous consequences. Deepfake presents itself as a promising technology to be used for such adversarial attacks, in which a deepfake traffic sign would replace a real-world traffic sign image before the image is fed to the AV traffic sign classification system. In this study, the authors present how a generative adversarial network-based deepfake attack can be crafted to fool the AV traffic sign classification systems. The authors developed a deepfake traffic sign image detection strategy leveraging hybrid quantum-classical neural networks (NNs). This hybrid approach utilizes amplitude encoding to represent the features of an input traffic sign image using quantum states, which substantially reduces the memory requirement compared to its classical counterparts. The authors evaluated this hybrid deepfake detection approach along with several baseline classical convolutional NNs on real-world and deepfake traffic sign images. The results indicate that the hybrid quantum-classical NNs for deepfake detection could achieve similar or higher performance than the baseline classical convolutional NNs in most cases while requiring less than one-third of the memory required by the shallowest classical convolutional NN considered in this study.

Lattice quantum chromodynamics (QCD) calculations share a defining challenge by requiring a small finite range of spatial separation $z$ between quark/gluon bilinears for controllable power corrections in the perturbative QCD factorization, and a large hadron boost $p_z$ for a successful determination of collinear parton distribution functions (PDFs). However, these two requirements make the determination of PDFs from lattice data very challenging. We present the application of generative machine learning algorithms to estimate the polarized and unpolarized gluon correlation functions utilizing short-distance data and extending the correlation up to $zp_z \lesssim 14$, surpassing the current capabilities of lattice QCD calculations. We train physics-informed machine learning algorithms to learn from the short-distance correlation at $z\lesssim 0.36$ fm and take the limit, $p_z \to \infty$, thereby minimizing possible contamination from the higher-twist effects for a successful reconstruction of the polarized gluon PDF. We also expose the bias and problems with underestimating uncertainties associated with the use of model-dependent and overly constrained functional forms, such as $x^\alpha(1-x)^\beta$ and its variants to extract PDFs from the lattice data. We propose the use of generative machine learning algorithms to mitigate these issues and present our determination of the polarized and unpolarized gluon PDFs in the nucleon., Comment: 24 pages, 18 figures

Water is often overused in irrigation, making efficient management of it crucial. Precision Agriculture emphasizes tools like stem water potential (SWP) analysis for better plant status determination. However, such tools often require labor-intensive in-situ sampling. Automation and machine learning can streamline this process and enhance outcomes. This work focused on automating stem detection and xylem wetness classification using the Scholander Pressure Chamber, a widely used but demanding method for SWP measurement. The aim was to refine stem detection and develop computer-vision-based methods to better classify water emergence at the xylem. To this end, we collected and manually annotated video data, applying vision- and learning-based methods for detection and classification. Additionally, we explored data augmentation and fine-tuned parameters to identify the most effective models. The identified best-performing models for stem detection and xylem wetness classification were evaluated end-to-end over 20 SWP measurements. Learning-based stem detection via YOLOv8n combined with ResNet50-based classification achieved a Top-1 accuracy of 80.98%, making it the best-performing approach for xylem wetness classification.

Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing valuable knowledge the model had learned in pre-training. For example, fine-tuning a pre-trained classifier capable of recognizing a large number of classes to master a subset of classes at hand is shown to drastically degrade the model's accuracy in the other classes it had previously learned. As such, it is hard to further use the fine-tuned model when it encounters classes beyond the fine-tuning data. In this paper, we systematically dissect the issue, aiming to answer the fundamental question, "What has been damaged in the fine-tuned model?" To our surprise, we find that the fine-tuned model neither forgets the relationship among the other classes nor degrades the features to recognize these classes. Instead, the fine-tuned model often produces more discriminative features for these other classes, even if they were missing during fine-tuning! {What really hurts the accuracy is the discrepant logit scales between the fine-tuning classes and the other classes}, implying that a simple post-processing calibration would bring back the pre-trained model's capability and at the same time unveil the feature improvement over all classes. We conduct an extensive empirical study to demonstrate the robustness of our findings and provide preliminary explanations underlying them, suggesting new directions for future theoretical analysis. Our code is available at https://github.com/OSU-MLB/Fine-Tuning-Is-Fine-If-Calibrated., Comment: The paper has been accepted to NeurIPS 2024. The first three authors contribute equally

This work contributes to the scarce empirical literature on LLM-based interactive homework in real-world educational settings and offers a practical, scalable solution for improving homework in schools. Homework is an important part of education in schools across the world, but in order to maximize benefit, it needs to be accompanied with feedback and followup questions. We developed a prompting strategy that enables GPT-4 to conduct interactive homework sessions for high-school students learning English as a second language. Our strategy requires minimal efforts in content preparation, one of the key challenges of alternatives like home tutors or ITSs. We carried out a Randomized Controlled Trial (RCT) in four high-school classes, replacing traditional homework with GPT-4 homework sessions for the treatment group. We observed significant improvements in learning outcomes, specifically a greater gain in grammar, and student engagement. In addition, students reported high levels of satisfaction with the system and wanted to continue using it after the end of the RCT., Comment: Submitted to LAK25

We propose a low energy model for simulating an analog black hole on an optical lattice using ultracold atoms. Assuming the validity of the holographic principle, we employ the Sachdev-Ye-Kitaev (SYK) model, which describes a system of randomly infinite range interacting fermions, also conjectured to be an exactly solvable UV-complete model for an extremal black hole in a higher dimensional Anti-de Sitter (AdS) dilaton gravity. At low energies, the SYK model exhibits an emergent conformal symmetry and is dual to the extremal black hole solution in near AdS2 spacetime. Furthermore, we show how the SYK maximally chaotic behaviour at large N limit, found to be dual to a gauge theory in higher dimensions, can also be employed as a non-trivial investigation tool for the holographic principle. The proposed setup is a theoretical platform to realize the SYK model with relevant exotic effects and behaviour at low energies as a highly non-trivial example of the AdS/CFT duality and a framework for studying black holes., Comment: 15 pages, 3 figures

Decentralized diagnostic testing that is accurate, portable, quantitative, and capable of making multiple simultaneous measurements of different biomarkers at the point-of-need remains an important unmet need in the post-pandemic world. Resonator-based biosensors using silicon photonic integrated circuits are a promising technology to meet this need, as they can leverage (1) semiconductor manufacturing economies of scale, (2) exquisite optical sensitivity, and (3) the ability to integrate tens to hundreds of sensors on a millimeter-scale photonic chip. However, their application to decentralized testing has historically been limited by the expensive, bulky tunable lasers and alignment optics required for their readout. In this work, we introduce a segmented sensor architecture that addresses this important challenge by facilitating resonance-tracking readout using a fixed-wavelength laser. The architecture incorporates an in-resonator phase shifter modulated by CMOS drivers to periodically sweep and acquire the resonance peak shifts as well as a distinct high-sensitivity sensing region, maintaining high performance at a fraction of the cost and size. We show, for the first time, that fixed-wavelength sensor readout can offer similar performance to traditional tunable laser readout, demonstrating a system limit of detection of 6.1 x 10-5 RIU as well as immunoassay-based detection of the SARS-CoV-2 spike protein. We anticipate that this sensor architecture will open the door to a new data-rich class of portable, accurate, multiplexed diagnostics for decentralized testing.

Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations. We address this gap by introducing seven synthetic datasets in dialects alongside Modern Standard Arabic (MSA), created using Machine Translation (MT) combined with human post-editing. We present AraDiCE, a benchmark for Arabic Dialect and Cultural Evaluation. We evaluate LLMs on dialect comprehension and generation, focusing specifically on low-resource Arabic dialects. Additionally, we introduce the first-ever fine-grained benchmark designed to evaluate cultural awareness across the Gulf, Egypt, and Levant regions, providing a novel dimension to LLM evaluation. Our findings demonstrate that while Arabic-specific models like Jais and AceGPT outperform multilingual models on dialectal tasks, significant challenges persist in dialect identification, generation, and translation. This work contributes ~45K post-edited samples, a cultural benchmark, and highlights the importance of tailored training to improve LLM performance in capturing the nuances of diverse Arabic dialects and cultural contexts. We will release the dialectal translation models and benchmarks curated in this study., Comment: Benchmarking, Culturally Informed, Large Language Models, Arabic NLP, LLMs

Objective: Variation of forearm orientation is one of the crucial factors that drastically degrades the forearm orientation invariant hand gesture recognition performance or the degree of freedom and limits the successful commercialization of myoelectric prosthetic hand or electromyogram (EMG) signal-based human-computer interfacing devices. This study investigates the impact of surface EMG electrode positions (elbow and forearm) on forearm orientation invariant hand gesture recognition. Methods: The study has been performed over 19 intact limbed subjects, considering 12 daily living hand gestures. The quality of the EMG signal is confirmed in terms of three indices. Then, the recognition performance is evaluated and validated by considering three training strategies, six feature extraction methods, and three classifiers. Results: The forearm electrode position provides comparable to or better EMG signal quality considering three indices. In this research, the forearm electrode position achieves up to 5.35% improved forearm orientation invariant hand gesture recognition performance compared to the elbow electrode position. The obtained performance is validated by considering six feature extraction methods, three classifiers, and real-time experiments. In addition, the forearm electrode position shows its robustness with the existence of recent works, considering recognition performance, investigated gestures, the number of channels, the dimensionality of feature space, and the number of subjects. Conclusion: The forearm electrode position can be the best choice for getting improved forearm orientation invariant hand gesture recognition performance. Significance: The performance of myoelectric prosthesis and human-computer interfacing devices can be improved with this optimized electrode position., Comment: 10 pages, 4 figures, 5 tables

Microgravity mass gauging has gained increasing importance in recent years due to the acceleration in planning for long-term space missions as well as in-space refueling and transfer operations. It is of particular importance with cryogenic propellants where periodic tank venting maneuvers and leak detection place a special emphasis on accurate mass gauging. Several competing technologies have arisen, but capacitance mass gauging has several distinct advantages due to its low mass, non-intrusiveness, and whole volume interrogation technique. Capacitance based measurement has also seen recent success in measuring cryogenic liquid nitrogen and hydrogen volume fraction and flow rate, showing its compatibility with cryogenic propellants. However, the effects of gravity on fluid behavior make the calibration and testing of these sensors difficult on the ground. In this paper a prototype sensor is constructed that can emulate fluid positions in microgravity and earth gravity configurations. Experimental propellant fills and drains are conducted using a simulant fluid with similar electrical properties to cryogenic propellants. This expanded dataset is compared with previous simulation results and used to construct a machine learning model capable of calculating the fluid mass in tanks both with and without propellant management devices., Comment: 6 figures, 8 references

Understanding how species interactions shape biodiversity is a core challenge in ecology. While much focus has been on long-term stability, there is rising interest in transient dynamics-the short-lived periods when ecosystems respond to disturbances and adjust toward stability. These transitions are crucial for predicting ecosystem reactions and guiding effective conservation. Our study introduces a model that uses convex combinations to blend pairwise and higher-order interactions, offering a more realistic view of natural ecosystems. We find pairwise interactions slow the journey to stability, while higher-order interactions speed it up. Employing global stability analysis and numerical simulations, we establish that as the proportion of higher-order interactions (HOIs) increases, mean transient times exhibit a significant reduction, thereby underscoring the essential role of HOIs in enhancing biodiversity stabilization. Our results reveal a robust correlation between the most negative real part of the eigenvalues of the Jacobian matrix associated with the linearized system at the coexistence equilibrium and the mean transient times. This indicates that a more negative leading eigenvalue correlates with accelerated convergence to stable coexistence abundances. This insight is vital for comprehending ecosystem resilience and recovery, emphasizing the key role of HOIs in promoting stabilization. Amid growing interest in transient dynamics and its implications for biodiversity and ecological stability, our study enhances the understanding of how species interactions affect both transient and long-term ecosystem behavior. By addressing a critical gap in ecological theory and offering a practical framework for ecosystem management, our work advances knowledge of transient dynamics, ultimately informing effective conservation strategies., Comment: 13 pages, 6 figures

With the rise in the wholesale adoption of Deep Learning (DL) models in nearly all aspects of society, a unique set of challenges is imposed. Primarily centered around the architectures of these models, these risks pose a significant challenge, and addressing these challenges is key to their successful implementation and usage in the future. In this research, we present the security challenges associated with the current DL models deployed into production, as well as anticipate the challenges of future DL technologies based on the advancements in computing, AI, and hardware technologies. In addition, we propose risk mitigation techniques to inhibit these challenges and provide metrical evaluations to measure the effectiveness of these metrics., Comment: 10 pages, 1 table, 6 equations/metrics

The Von Neumann bottleneck, a fundamental challenge in conventional computer architecture, arises from the inability to execute fetch and data operations simultaneously due to a shared bus linking processing and memory units. This bottleneck significantly limits system performance, increases energy consumption, and exacerbates computational complexity. Emerging technologies such as Resistive Random Access Memories (RRAMs), leveraging crossbar arrays, offer promising alternatives for addressing the demands of data-intensive computational tasks through in-memory computing of analog vector-matrix multiplication (VMM) operations. However, the propagation of errors due to device and circuit-level imperfections remains a significant challenge. In this study, we introduce MELISO (In-Memory Linear Solver), a comprehensive end-to-end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the error propagation in VMM operations, analyzing the impact of RRAM device metrics on error magnitude and distribution. This paper introduces the MELISO framework and demonstrates its utility in characterizing and mitigating VMM error propagation using state-of-the-art RRAM device metrics., Comment: ICONS 2024.Copyright 2024 IEEE.Personal use of this material is permitted.Permission from IEEE must be obtained for all other uses,in any current or future media,including reprinting/republishing this material for advertising or promotional purposes,creating new collective works,for resale or redistribution to servers or lists or reuse of any copyrighted component of this work in other works

We compute the thermal conductivity of monolayer beryllene using the linearized phonon Boltzmann transport equation with interatomic force constants obtained from \textit{ab-initio} calculations. Monolayer beryllene exhibits an impressive thermal conductivity of 270 W/m$\cdot$K at room temperature, exceeding that of bulk beryllium by over 100%. Our study reveals a remarkable temperature-dependent behavior: $\kappa \sim T^{-2}$ at low temperatures, attributed to higher normal phonon-phonon scatterings, and $\kappa \sim T^{-1}$ at high temperatures, due to Umklapp phonon interactions. Mode-specific analysis reveals that flexural phonons with longer lifetimes are the primary contributors to thermal conductivity, accounting for approximately 80%. This dominance results from their lower scattering rates in the out-of-plane direction due to a restricted phase space for scattering processes. Additionally, our findings highlight suppressed Umklapp scattering and reduced phase space for flexural modes, providing a thorough understanding of the eased thermal conductivity in monolayer beryllene and its potential for advanced thermal management applications., Comment: 5 pages, 5 figures

In this work, we investigate the Krylov complexity in quantum optical systems subject to time--dependent classical external fields. We focus on various interacting quantum optical models, including a collection of two--level atoms, photonic systems and the quenched oscillator. These models have Hamiltonians which are linear in the generators of $SU(2)$, $H(1)$ (Heisenberg--Weyl) and $SU(1,1)$ group symmetries allowing for a straightforward identification of the Krylov basis. We analyze the behaviour of complexity for these systems in different regimes of the driven field, focusing primarily on resonances. This is achieved via the Gauss decomposition of the unitary evolution operators for the group symmetries. Additionally, we also investigate the Krylov complexity in a three--level $SU(3)$ atomic system using the Lanczos algorithm, revealing the underlying complexity dynamics. Throughout we have exploited the the relevant group structures to simplify our explorations., Comment: 23 pages, 15 figures, 4 appendices

We tune the onset of optical response in aluminium kinetic inductance detectors from a natural cutoff frequency of 90 GHz to 60 GHz by applying an external magnetic field. The change in spectral response is due to the decrease of the superconducting gap, from 90 GHz at zero magnetic field to 60 GHz at a magnetic field of around 3 mT. We characterize the variation of the superconducting gap, the detector frequency shift and the internal quality factor as a function of the applied field. In principle, the magnetic field tunable response could be used to make spectroscopic measurements. In practice, the internal quality factor behaves hysteretically with the magnetic field due to the presence of vortices in the thin superconducting film. We conclude by discussing possible solutions to achieve spectroscopy measurements using kinetic inductance detectors and magnetic field.

This paper proposes Proteus, a protocol state machine, property-guided, and budget-aware automated testing approach for discovering logical vulnerabilities in wireless protocol implementations. Proteus maintains its budget awareness by generating test cases (i.e., each being a sequence of protocol messages) that are not only meaningful (i.e., the test case mostly follows the desirable protocol flow except for some controlled deviations) but also have a high probability of violating the desirable properties. To demonstrate its effectiveness, we evaluated Proteus in two different protocol implementations, namely 4G LTE and BLE, across 23 consumer devices (11 for 4G LTE and 12 for BLE). Proteus discovered 25 unique issues, including 112 instances. Affected vendors have positively acknowledged 14 vulnerabilities through 5 CVEs., Comment: Accepted to ACM CCS 2024

The gravitational wave (GW) interferometers LISA and ET are expected to be functional in the next decade(s), possibly around the same time. They will operate over different frequency ranges, with similar integrated sensitivities to the amplitude of a stochastic GW background (SGWB). We investigate the synergies between these two detectors, in terms of a multi-band detection of a cosmological SGWB characterised by a large amplitude, and a broad frequency spectrum. By investigating various examples of SGWBs, such as those arising from cosmological phase transition, cosmic string, primordial inflation, we show that LISA and ET operating together will have the opportunity to assess more effectively the characteristics of the GW spectrum produced by the same cosmological source, but at separate frequency scales. Moreover, the two experiments in tandem can be sensitive to features of early universe cosmic expansion before big-bang nucleosynthesis (BBN), which affects the SGWB frequency profile, and which would not be possible to detect otherwise, since two different frequency ranges correspond to two different pre-BBN (or post-inflationary) epochs. Besides considering the GW spectrum, we additionally undertake a preliminary study of the sensitivity of LISA and ET to soft limits of higher order tensor correlation functions. Given that these experiments operate at different frequency bands, their synergy constitutes an ideal direct probe of squeezed limits of higher order GW correlators, which can not be measured operating with a single instrument only., Comment: 44 pages, 10 figures

A person's Body Mass Index, or BMI, is the most widely used parameter for assessing their health. BMI is a crucial predictor of potential diseases that may arise at higher body fat levels because it is correlated with body fat. Conversely, a community's or an individual's nutritional status can be determined using the BMI. Although deep learning models are used in several studies to estimate BMI from face photos and other data, no previous research established a clear connection between deep learning techniques for handwriting analysis and BMI prediction. This article addresses this research gap with a deep learning approach to estimating BMI from handwritten characters by developing a convolutional neural network (CNN). A dataset containing samples from 48 people in lowercase English scripts is successfully captured for the BMI prediction task. The proposed CNN-based approach reports a commendable accuracy of 99.92%. Performance comparison with other popular CNN architectures reveals that AlexNet and InceptionV3 achieve the second and third-best performance, with the accuracy of 99.69% and 99.53%, respectively.

Anti-seizure medications (ASMs) are the mainstay of treatment for epilepsy, yet their effect on seizure spread is not fully understood. Higher ASM doses have been associated with shorter and less severe seizures. Our objective was to test if this effect was due to limiting seizure spread through early termination of otherwise unchanged seizures. We retrospectively examined intracranial EEG (iEEG) recordings in 15 subjects that underwent ASM tapering during pre-surgical monitoring. We estimated ASM plasma concentrations based on pharmaco-kinetic modelling. In each subject, we identified seizures that followed the same onset and initial spread patterns, but some seizures terminated early (truncated seizures), and other seizures continued to spread (continuing seizures). We compared ASM concentrations at the times of truncated seizures and continuing seizures. We found no substantial difference between ASM concentrations when truncated vs. continuing seizures occurred (Mean difference = 4%, sd = 29%, p=0.6). Our results indicate that ASM did not appear to halt established seizures in this cohort. Further research is needed to understand how ASM may modulate seizure duration and severity.

Trustless tracking of Resident Space Objects (RSOs) is crucial for Space Situational Awareness (SSA), especially during adverse situations. The importance of transparent SSA cannot be overstated, as it is vital for ensuring space safety and security. In an era where RSO location information can be easily manipulated, the risk of RSOs being used as weapons is a growing concern. The Tracking Data Message (TDM) is a standardized format for broadcasting RSO observations. However, the varying quality of observations from diverse sensors poses challenges to SSA reliability. While many countries operate space assets, relatively few have SSA capabilities, making it crucial to ensure the accuracy and reliability of the data. Current practices assume complete trust in the transmitting party, leaving SSA capabilities vulnerable to adversarial actions such as spoofing TDMs. This work introduces a trustless mechanism for TDM validation and verification using deep learning over blockchain. By leveraging the trustless nature of blockchain, our approach eliminates the need for a central authority, establishing consensus-based truth. We propose a state-of-the-art, transformer-based orbit propagator that outperforms traditional methods like SGP4, enabling cross-validation of multiple observations for a single RSO. This deep learning-based transformer model can be distributed over a blockchain, allowing interested parties to host a node that contains a part of the distributed deep learning model. Our system comprises decentralised observers and validators within a Proof of Stake (PoS) blockchain. Observers contribute TDM data along with a stake to ensure honesty, while validators run the propagation and validation algorithms. The system rewards observers for contributing verified TDMs and penalizes those submitting unverifiable data., Comment: Accepted for AMOS 2024

We study the radial transport of cold gas within simulated disk galaxies at cosmic noon, aiming at distinguishing between disk instability and accretion along cold streams from the cosmic web as its driving mechanism. Disks are selected based on kinematics and flattening from the VELA zoom-in hydro-cosmological simulations. The radial velocity fields in the disks are mapped, their averages are computed as a function of radius and over the whole disk, and the radial mass flux in each disk as a function of radius is obtained. The transport directly associated with fresh incoming streams is identified by selecting cold gas cells that are either on incoming streamlines or have low metallicity. The radial velocity fields in VELA disks are found to be highly non-axisymmetric, showing both inflows and outflows. However, in most cases, the average radial velocities, both as a function of radius and over the whole disk, are directed inwards, with the disk-averaged radial velocities typically amounting to a few percent of the disk-averaged rotational velocities. This is significantly lower than the expectations from various models that analytically predict the inward mass transport as driven by torques associated with disk instability. Under certain simplifying assumptions, the latter typically predict average inflows of more than $10\%$ of the rotational velocities. Analyzing the radial motions of streams and off-stream material, we find that the radial inflow in VELA disks is dominated by the stream inflows themselves, especially in the outer disks. The high inward radial velocities inferred in observed disks at cosmic noon, at the level of $\sim \! 20\%$ of the rotational velocities, may reflect inflowing streams from the cosmic web rather than being generated by disk instability., Comment: 20 pages, 14 figures, submitted to A&A

The stochastic background of gravitational wave signals arising from the core-collapse supernovae is produced through various complex mechanisms that need detailed and careful investigation. We proposed a simplified multi-peak waveform of the amplitude spectrum. The corresponding energy spectra of our model fit the energy spectra obtained from different numerical simulations of various types of core-collapse events such as non-rotating, slow and fast rotating massive progenitors resulting in neutron stars. The maximum dimensionless energy density $\Omega_{gw}$ corresponding to our model is of $\mathcal{O}(10 {^{-12})} $ around 650 Hz. Assuming some degree of uncertainty, we estimated the parameters for the core collapse waveform using BILBY. We studied the detectability of the signal of our model against gravitational-wave detectors like the Einstein Telescope, advanced LIGO and Virgo. Our study indicates that these detectors have to gain more sensitivity to pick up the gravitational wave signals of stochastic background., Comment: Accepted for publication in Phys. Rev. D

In this paper, we study two classes of models, Recursive Neural Networks (RvNNs) and Transformers, and show that a tight connection between them emerges from the recent development of two recent models - Continuous Recursive Neural Networks (CRvNN) and Neural Data Routers (NDR). On one hand, CRvNN pushes the boundaries of traditional RvNN, relaxing its discrete structure-wise composition and ends up with a Transformer-like structure. On the other hand, NDR constrains the original Transformer to induce better structural inductive bias, ending up with a model that is close to CRvNN. Both models, CRvNN and NDR, show strong performance in algorithmic tasks and generalization in which simpler forms of RvNNs and Transformers fail. We explore these "bridge" models in the design space between RvNNs and Transformers, formalize their tight connections, discuss their limitations, and propose ideas for future research.

The circular economy has the potential to increase resource efficiency and minimize waste through the 4R framework of reducing, reusing, recycling, and recovering. Blockchain technology is currently considered a valuable aid in the transition to a circular economy. Its decentralized and tamper-resistant nature enables the construction of transparent and secure supply chain management systems, thereby improving product accountability and traceability. However, the full potential of blockchain technology in circular economy models will not be realized until a number of concerns, including scalability, interoperability, data protection, and regulatory and legal issues, are addressed. More research and stakeholder participation are required to overcome these limitations and achieve the benefits of blockchain technology in promoting a circular economy. This article presents a systematic literature review (SLR) that identified industry use cases for blockchain-driven circular economy models and offered architectures to minimize resource consumption, prices, and inefficiencies while encouraging the reuse, recycling, and recovery of end-of-life products. Three main outcomes emerged from our review of 41 documents, which included scholarly publications, Twitter-linked information, and Google results. The relationship between blockchain and the 4R framework for circular economy; discussion the terminology and various forms of blockchain and circular economy; and identification of the challenges and obstacles that blockchain technology may face in enabling a circular economy. This research shows how blockchain technology can help with the transition to a circular economy. Yet, it emphasizes the importance of additional study and stakeholder participation to overcome potential hurdles and obstacles in implementing blockchain-driven circular economy models.

Study of correlation functions in AdS/CFT and in-in correlators in de Sitter space often requires the computation of Witten diagrams. Due to the complexity of evaluating radial integrals for these correlators, several indirect approaches have been developed to simplify computations. However, in momentum space, these methods have been limited to fields with integer spin. In this paper, we formulate tools for evaluating Witten diagrams with spin$-\frac12$ fields in momentum space and discuss where they differ from the corresponding integer-spin analysis. We formulate our tools explicitly for massless fermions and present how appropriate Weight shifting operators with respect to the external kinematics can be used to obtain the generalization to fermions with integer mass. We apply these tools to loop Witten diagrams and also discuss their use for evaluating in-in correlators in dS. In cases where we can evaluate the loop integrals, we find their transcendentality is lower than the corresponding scalar field results. Further, we classify the nature of IR divergences encountered for interacting massive scalars and fermions. We also prove a novel Weinberg-like soft theorem for gauge fields coupled to matter in AdS and show that the universal terms in the leading soft factor are sensitive to the spin of the matter field. These generalize the recently discovered soft theorems for pure Yang-Mills to Yang-Mills with matter., Comment: 50 pages + 15 pages of appendix; v2: typos fixed and references added

There are few results on mean field game (MFG) systems where the PDEs are either fully nonlinear or have degenerate diffusions. This paper introduces a problem that combines both difficulties. We prove existence and uniqueness for a strongly degenerate, fully nonlinear MFG system by using the well-posedness theory for fully nonlinear MFGs established in our previous paper. It is the first such application in a degenerate setting. Our MFG involves a controlled pure jump (nonlocal) L\'evy diffusion of order less than one, and monotone, smoothing couplings. The key difficulty is obtaining uniqueness for the corresponding degenerate, non-smooth Fokker-Plank equation: since the regularity of the coefficient and the order of the diffusion are interdependent, it holds when the order is sufficiently low. Viscosity solutions and a non-standard doubling of variables argument are used along with a bootstrapping procedure., Comment: Earlier version of arXiv:2104.06985 has been split into two articles. The latest version of arXiv:2104.06985 is now the first part and this article is the second part

The popularity of the Internet of Things (IoT) has driven its usage in our homes and industries over the past 10-12 years. However, there have been some major issues related to identity management and ownership transfer involving IoT devices, particularly for consumer IoT devices, e. g. smart appliances such as smart TVs, smart refrigerators, and so on. There have been a few attempts to address this issue; however, user-centric and effective ownership and identity management of IoT devices have not been very successful so far. Recently, blockchain technology has been used to address these issues with limited success. This article presents a Self-sovereign Identity (SSI) based system that facilitates a secure and user-centric ownership management and transfer of consumer IoT devices. The system leverages a number of emerging technologies, such as blockchain and decentralized identifiers (DID), verifiable credentials (VC), under the umbrella of SSI. We present the architecture of the system based on a threat model and requirement analysis, discuss the implementation of a Proof-of-Concept based on the proposed system and illustrate a number of use-cases with their detailed protocol flows. Furthermore, we analyse its security using ProVerif, a state-of-the art protocol verification tool and examine its performance.

Many materials exhibit various types of phase transitions at different temperatures, with many also demonstrating polymorphism. Doping materials can significantly alter their conductivity. In light of this, we have investigated the electrical conductivity of $LaCoO_3$, specifically its temperature dependence when doped with Yttrium (Y). The crystal structure of Lanthanum Yttrium Cobalt oxide $(La_{1-x}Y_x Co O_3)$ adopts a perovskite form, characterized by the general stoichiometry $ABX_3$, where A and B are cations, and X is an anion. This material undergoes a magnetic phase transition between $50-100$ K, a structural phase transition between $100-300$ K, and an insulator-to-metal transition at $500$ K. At room temperature, $LaCoO_3$ exhibits polaron-type hopping conduction. Our aim was to understand the electrical conductivity at $300$ K and how it varies with temperature when $La^{3+}$ is replaced by $Y^{3+}$. The electrical properties of the perovskite crystal are consistent with small polaron hopping conduction, which theoretically follows Mott's variable range hopping model, where conductivity obeys an exponential law, and resistivity follows an inverse exponential pattern. In this work, we compare the experimental resistivity graph with the theoretical inverse of the conductivity graph, showing that our experimental results align with the polaron hopping conduction model within a certain range. Additionally, the experiment confirms polymorphism in various cases. We observed that increasing the concentration of $Y^{3+}$ enhances the metallic properties of $La_{1-x} Y_x Co O_3$, and we found a significant correlation between conductivity and symmetry. Furthermore, the study highlights the material's phase transitions and polymorphic behavior., Comment: 10 pages, 7 figures

Two-dimensional (2D) van der Waals ferromagnets have potential applications as next-generation spintronic devices and provide a platform to explore the fundamental physics behind 2D magnetism. The dual nature (localized and itinerant) of electrons adds further complexity to the understanding of correlated magnetic materials. Here, we present the temperature evolution of electronic structure in 2D van der Waals ferromagnet, Fe$_{3}$GeTe$_{2}$, using photoemission spectroscopy in conjunction with density functional theory (DFT) plus dynamical mean field theory (DMFT). With the appearance of quasiparticle peak and its evolution in the vicinity of Fermi energy, we unveil empirical evidences of incoherent-coherent crossover at around 125 K. DFT+DMFT results show that the quasiparticle lifetime surpasses thermal energy for temperature below 150 K, confirming incoherent-coherent crossover in the system. No appreciable change in the Fe 2$p$ core level, overall valence band spectra across the magnetic transition, and temperature dependent ferromagnetic DFT+DMFT results, provide substantial evidence for non-stoner magnetism in Fe$_{3}$GeTe$_{2}$. We elucidate the temperature dependent intimate relation between magnetism and electronic structure in Fe$_{3}$GeTe$_{2}$. Sommerfeld coefficient of $\sim$ 104 mJ mol$^{-1}$ K$^{-2}$ obtained in the low temperature limit from DFT+DMFT calculations resolve the long standing issue of large Sommerfeld coefficient ($\sim$ 110 mJ mol$^{-1}$ K$^{-2}$) obtained from specific heat measurements., Comment: to appear in Phys. Rev. B

The infectious disease caused by novel corona virus (2019-nCoV) has been widely spreading since last year and has shaken the entire world. It has caused an unprecedented effect on daily life, global economy and public health. Hence this disease detection has life-saving importance for both patients as well as doctors. Due to limited test kits, it is also a daunting task to test every patient with severe respiratory problems using conventional techniques (RT-PCR). Thus implementing an automatic diagnosis system is urgently required to overcome the scarcity problem of Covid-19 test kits at hospital, health care systems. The diagnostic approach is mainly classified into two categories-laboratory based and Chest radiography approach. In this paper, a novel approach for computerized corona virus (2019-nCoV) detection from lung x-ray images is presented. Here, we propose models using deep learning to show the effectiveness of diagnostic systems. In the experimental result, we evaluate proposed models on publicly available data-set which exhibit satisfactory performance and promising results compared with other previous existing methods., Comment: Accepted in AIP Conference Proceedings (Vol. 2424, No. 1)

The ultrafast magnetization dynamics of an epitaxial Fe/CoO bilayer on Ag(001) is examined in an element-resolved way by resonant soft-x-ray reflectivity. The transient magnetic linear dichroism at the Co L2 edge and the magnetic circular dichroism at the Fe L3 edge measured in reflection in a pump-probe experiment with 120 fs temporal resolution show the loss of antiferromagnetic and ferromagnetic order in CoO and Fe, respectively, both within 300 fs after excitation with 60 fs light pulses of 800 and 400 nm wavelengths. Comparison to spin-dynamics simulations using an atomistic spin model shows that direct energy transfer from the laser-excited electrons in Fe to the magnetic moments in CoO provides the dominant demagnetization channel in the case of 800-nm excitation.

When using AI to detect signs of depressive disorder, AI models habitually draw preemptive conclusions. We theorize that using chain-of-thought (CoT) prompting to evaluate Patient Health Questionnaire-8 (PHQ-8) scores will improve the accuracy of the scores determined by AI models. In our findings, when the models reasoned with CoT, the estimated PHQ-8 scores were consistently closer on average to the accepted true scores reported by each participant compared to when not using CoT. Our goal is to expand upon AI models' understanding of the intricacies of human conversation, allowing them to more effectively assess a patient's feelings and tone, therefore being able to more accurately discern mental disorder symptoms; ultimately, we hope to augment AI models' abilities, so that they can be widely accessible and used in the medical field.

Materials with chiral electronic structures are of great interest. We report a triazatruxene, TAT, molecular semiconductor with chiral alkyl side chains that crystallises from solution to form chirally-stacked columns with a helical pitch of 6 TATs (2.3 nm). These crystals show strong circularly polarised, CP, green photoluminescence, with dissymmetry of 24%. Electronic structure calculations using the full crystal structure, show that this chiral stacking associates angular momentum to the valence and conduction states and thus gives rise to the observed CP luminescence. Free-standing crystals are not useful for active semiconductor devices, but we have discovered that co-sublimation of TAT as the guest in a structurally mismatched host enables the fabrication of thin films where the chiral crystallization is achieved in-situ by thermally-triggered nano-phase segregation of dopant and host whilst preserving the integrity of the film. This enables fabrication of bright (green) organic light-emitting diodes with unexpectedly high external quantum efficiencies of up to 16% and electroluminescence dissymmetries above 10%. These materials and this process method offer significant application potential in spintronics, optical displays and multidimensional optoelectronics.

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

Sleep stages play an essential role in the identification of sleep patterns and the diagnosis of sleep disorders. In this study, we present an automated sleep stage classifier termed the Attentive Dilated Convolutional Neural Network (AttDiCNN), which uses deep learning methodologies to address challenges related to data heterogeneity, computational complexity, and reliable automatic sleep staging. We employed a force-directed layout based on the visibility graph to capture the most significant information from the EEG signals, representing the spatial-temporal features. The proposed network consists of three compositors: the Localized Spatial Feature Extraction Network (LSFE), the Spatio-Temporal-Temporal Long Retention Network (S2TLR), and the Global Averaging Attention Network (G2A). The LSFE is tasked with capturing spatial information from sleep data, the S2TLR is designed to extract the most pertinent information in long-term contexts, and the G2A reduces computational overhead by aggregating information from the LSFE and S2TLR. We evaluated the performance of our model on three comprehensive and publicly accessible datasets, achieving state-of-the-art accuracy of 98.56%, 99.66%, and 99.08% for the EDFX, HMC, and NCH datasets, respectively, yet maintaining a low computational complexity with 1.4 M parameters. The results substantiate that our proposed architecture surpasses existing methodologies in several performance metrics, thus proving its potential as an automated tool in clinical settings., Comment: In review to IEEEtrans NNLS; 15-pages main paper and 3-pages supplementary material

The VIP Cup offers a unique experience to undergraduates, allowing students to work together to solve challenging, real-world problems with video and image processing techniques. In this iteration of the VIP Cup, we challenged students to balance personalization and generalization when performing biomarker detection in 3D optical coherence tomography (OCT) images. Balancing personalization and generalization is an important challenge to tackle, as the variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across different patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Hence, we provided a multimodal OCT dataset to allow teams to effectively target this challenge. Overall, this competition gave undergraduates an opportunity to learn about how artificial intelligence can be a powerful tool for the medical field, as well as the unique challenges one faces when applying machine learning to biomedical data.

We investigate the interfacial dynamics involved in the impact of a droplet on a liquid-liquid system, which involves the impingement of an immiscible core liquid drop from a vertical separation onto an interfacial shell liquid layer floating on a host liquid bath. The dynamics have been studied for a wide range of impact Weber numbers and two different interfacial shell liquids of varying volumes. The core drop, upon impact, dragged the interfacial liquid into the host liquid, forming an interfacial liquid column with an air cavity inside the host liquid bath. The dynamics is resolved into cavity expansion and rapid contraction, followed by thinning of the interfacial liquid. The interplay of viscous dissipation, interfacial pull, and core drop inertia influenced the necking dynamics. The viscous dissipation increases with the thickness of the interfacial layer, which depends on its volume and lateral spread over the water. The necking dynamics transitioned from inertia-dominated deep seal closure at higher spread, lower interfacial film volumes, and higher Weber numbers, into inertia-capillary dominated deep seal closure with an increase in film volumes, decrease in the spread of the interfacial fluid or decrease in Weber number, and finally transitioned into a no seal closure at high volumes, low spread, and low Weber numbers.

The existence of an early matter-dominated epoch prior to the big bang nucleosynthesis may lead to a scenario where the thermal dark matter cools faster than plasma before the radiation dominated era begins. In the radiation-dominated epoch, dark matter free-streams after it decouples both chemically and kinetically from the plasma. In the presence of an early matter-dominated era, chemical decoupling of the dark matter may succeed by a partial kinetic decoupling before reheating ends, depending upon the contributions of different partial wave amplitudes in the elastic scattering rate of the dark matter. We show that the s-wave scattering is sufficient to partially decouple the dark matter from the plasma, if the entropy injection during the reheating era depends on the bath temperature, while p-wave scattering leads to full decoupling in such cosmological backdrop. The decoupling of dark matter before the end of reheating causes an additional amount of cooling, reducing its free-streaming horizon compared to usual radiation-dominated cosmology. The enhanced matter perturbations for scales entering the horizon prior to the end of reheating, combined with the reduced free-steaming horizon, increase the number density of sub-earth mass halos. Resulting boost in the dark matter annihilation signatures could offer an intriguing probe to differentiate pre-BBN non-standard cosmological epochs. We show that the free-streaming horizon of the dark matter requires to be smaller than a cut-off to ensure boost in the sub-earth halo populations. As case studies we present two examples: one for a scalar dark matter with s-wave elastic scattering and the other one featuring a fermionic dark matter with p-wave elastic scattering. We identify regions of parameter space in both models where the dark matter kinetically decouples during reheating, amplifying small scale structure formation., Comment: 31 pages, 10 captioned figures. Comments are welcome

Developers continuously interact in crowd-sourced community-based question-answer (Q&A) sites. Reportedly, 30% of all software professionals visit the most popular Q&A site StackOverflow (SO) every day. Software engineering (SE) research studies are also increasingly using SO data. To find out the trend, implication, impact, and future research potential utilizing SO data, a systematic mapping study needs to be conducted. Following a rigorous reproducible mapping study approach, from 18 reputed SE journals and conferences, we collected 384 SO-based research articles and categorized them into 10 facets (i.e., themes). We found that SO contributes to 85% of SE research compared with popular Q&A sites such as Quora, and Reddit. We found that 18 SE domains directly benefited from SO data whereas Recommender Systems, and API Design and Evolution domains use SO data the most (15% and 16% of all SO-based research studies, respectively). API Design and Evolution, and Machine Learning with/for SE domains have consistent upward publication. Deep Learning Bug Analysis and Code Cloning research areas have the highest potential research impact recently. With the insights, recommendations, and facet-based categorized paper list from this mapping study, SE researchers can find potential research areas according to their interest to utilize large-scale SO data.

The cost of software maintenance often surpasses the initial development expenses, making it a significant concern for the software industry. A key strategy for alleviating future maintenance burdens is the early prediction and identification of change-prone code components, which allows for timely optimizations. While prior research has largely concentrated on predicting change-prone files and classes, an approach less favored by practitioners, this paper shifts focus to predicting highly change-prone methods, aligning with the preferences of both practitioners and researchers. We analyzed 774,051 source code methods from 49 prominent open-source Java projects. Our findings reveal that approximately 80% of changes are concentrated in just 20% of the methods, demonstrating the Pareto 80/20 principle. Moreover, this subset of methods is responsible for the majority of the identified bugs in these projects. After establishing their critical role in mitigating software maintenance costs, our study shows that machine learning models can effectively identify these highly change-prone methods from their inception. Additionally, we conducted a thorough manual analysis to uncover common patterns (or concepts) among the more difficult-to-predict methods. These insights can help future research develop new features and enhance prediction accuracy.

Ising machines, hardware accelerators for combinatorial optimization and probabilistic sampling problems, have gained significant interest recently. A key element is stochasticity, which enables a wide exploration of configurations, thereby helping avoid local minima. Here, we refine the previously proposed concept of coupled chaotic bits (c-bits) that operate without explicit stochasticity. We show that augmenting chaotic bits with stochasticity enhances performance in combinatorial optimization, achieving algorithmic scaling comparable to probabilistic bits (p-bits). We first demonstrate that c-bits follow the quantum Boltzmann law in a 1D transverse field Ising model. We then show that c-bits exhibit critical dynamics similar to stochastic p-bits in 2D Ising and 3D spin glass models, with promising potential to solve challenging optimization problems. Finally, we propose a noise-augmented version of coupled c-bits via the adaptive parallel tempering algorithm (APT). Our noise-augmented c-bit algorithm outperforms fully deterministic c-bits running versions of the simulated annealing algorithm. Other analog Ising machines with coupled oscillators could draw inspiration from the proposed algorithm. Running replicas at constant temperature eliminates the need for global modulation of coupling strengths. Mixing stochasticity with deterministic c-bits creates a powerful hybrid computing scheme that can bring benefits in scaled, asynchronous, and massively parallel hardware implementations.

In this paper we construct and theoretically analyse group equivariant convolutional kernel networks (CKNs) which are useful in understanding the geometry of (equivariant) CNNs through the lens of reproducing kernel Hilbert spaces (RKHSs). We then proceed to study the stability analysis of such equiv-CKNs under the action of diffeomorphism and draw a connection with equiv-CNNs, where the goal is to analyse the geometry of inductive biases of equiv-CNNs through the lens of reproducing kernel Hilbert spaces (RKHSs). Traditional deep learning architectures, including CNNs, trained with sophisticated optimization algorithms is vulnerable to perturbations, including `adversarial examples'. Understanding the RKHS norm of such models through CKNs is useful in designing the appropriate architecture and can be useful in designing robust equivariant representation learning models.