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This paper presents the detection of DDoS attacks in IoT networks using machine learning models. Their rapid growth has made them highly susceptible to various forms of cyberattacks, many of whose security procedures are implemented in an irregular manner. It evaluates the efficacy of different machine learning models, such as XGBoost, K-Nearest Neighbours, Stochastic Gradient Descent, and Na\"ive Bayes, in detecting DDoS attacks from normal network traffic. Each model has been explained on several performance metrics, such as accuracy, precision, recall, and F1-score to understand the suitability of each model in real-time detection and response against DDoS threats. This comparative analysis will, therefore, enumerate the unique strengths and weaknesses of each model with respect to the IoT environments that are dynamic and hence moving in nature. The effectiveness of these models is analyzed, showing how machine learning can greatly enhance IoT security frameworks, offering adaptive, efficient, and reliable DDoS detection capabilities. These findings have shown the potential of machine learning in addressing the pressing need for robust IoT security solutions that can mitigate modern cyber threats and assure network integrity., Comment: 6 pages, 6 figures

This paper presents detailed radio propagation measurements for an indoor factory (InF) environment at 6.75 GHz and 16.95 GHz using a 1 GHz bandwidth channel sounder. Conducted at the NYU MakerSpace in the NYU Tandon School of Engineering campus in Brooklyn, NY, USA, our measurement campaign characterizes a representative small factory with diverse machinery and open workspaces across 12 locations, comprising 5 line-of-sight (LOS) and 7 non-line-of-sight (NLOS) scenarios. Analysis using the close-in (CI) path loss model with a 1 m reference distance reveals path loss exponents (PLE) below 2 in LOS at 6.75 GHz and 16.95 GHz, while in NLOS PLE is similar to free-space propagation. The RMS delay spread (DS) decreases at higher frequencies with a clear frequency dependence. Similarly, RMS angular spread (AS) measurements show wider spreads in NLOS compared to LOS at both frequency bands, with a decreasing trend as frequency increases. These observations in a dense-scatterer environment demonstrate frequency-dependent behavior that deviate from existing industry-standard models. Our findings provide crucial insights into complex propagation mechanisms in factory environments, essential for designing robust industrial wireless networks at upper mid-band frequencies., Comment: 6 pages, 4 figures

In this work, we designed a completely blind video quality assessment algorithm using the deep video prior. This work mainly explores the utility of deep video prior in estimating the visual quality of the video. In our work, we have used a single distorted video and a reference video pair to learn the deep video prior. At inference time, the learned deep prior is used to restore the original videos from the distorted videos. The ability of learned deep video prior to restore the original video from the distorted video is measured to quantify distortion in the video. Our hypothesis is that the learned deep video prior fails in restoring the highly distorted videos. The restoring ability of deep video prior is proportional to the distortion present in the video. Therefore, we propose to use the distance between the distorted video and the restored video as the perceptual quality of the video. Our algorithm is trained using a single video pair and it does not need any labelled data. We show that our proposed algorithm outperforms the existing unsupervised video quality assessment algorithms in terms of LCC and SROCC on a synthetically distorted video quality assessment dataset., Comment: Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) 2024 conference tinny paper

Global allocations in the upper mid-band spectrum (4--24 GHz) necessitate a comprehensive exploration of the propagation behavior to meet the promise of coverage and capacity. This paper presents an extensive Urban Microcell (UMi) outdoor propagation measurement campaign at 6.75 GHz and 16.95 GHz conducted in Downtown Brooklyn, USA, using a 1 GHz bandwidth sliding correlation channel sounder over 40--880 m propagation distance, encompassing 7 Line of Sight (LOS) and 13 Non-Line of Sight (NLOS) locations. Analysis of the path loss (PL) reveals lower directional and omnidirectional PL exponents compared to mmWave and sub-THz frequencies in the UMi environment, using the close-in PL model with a 1 m reference distance. Additionally, a decreasing trend in root mean square (RMS) delay spread (DS) and angular spread (AS) with increasing frequency was observed. The NLOS RMS DS and RMS AS mean values are obtained consistently lower compared to 3GPP model predictions. Point data for all measured statistics at each TX-RX location are provided to supplement the models and results. The spatio-temporal statistics evaluated here offer valuable insights for the design of next-generation wireless systems and networks., Comment: 6 pages, 4 figures, 6 tables

As the signatories of this manifesto, we denounce the attention economy as inhumane and a threat to our sociopolitical and ecological well-being. We endorse policymakers' efforts to address the negative consequences of the attention economy's technology, but add that these approaches are often limited in their criticism of the systemic context of human attention. Starting from Buddhist philosophy, we advocate a broader approach: an ecology of attending, that centers on conceptualizing, designing, and using attention (1) in an embedded way and (2) focused on the alleviating of suffering. With 'embedded' we mean that attention is not a neutral, isolated mechanism but a meaning-engendering part of an 'ecology' of bodily, sociotechnical and moral frameworks. With 'focused on the alleviation of suffering' we explicitly move away from the (often implicit) conception of attention as a tool for gratifying desires., Comment: 21 pages, 1 figure

The pursuit of targeted therapies capable of overcoming biological barriers, including the tenacious blood-brain barrier, has spurred the investigation into stimuli-responsive microagents. This approach could improve therapeutic efficacy, reduce undesirable side effects, and open avenues for treating previously incurable diseases. Intravenously-administered ultrasound-responsive microbubbles are one of the most promising agents, having demonstrated potential in several clinical trials. However, the mechanism by which microbubbles enhance drug absorption remains unclear. Here, we reveal through unprecedented time-resolved side-view visualisations that single microbubbles, upon microsecond-long ultrasound driving, puncture the cell membrane and induce drug uptake via stable cyclic microjets. Our theoretical models successfully reproduce the observed bubble and cell dynamic responses. We find that cyclic jets arise from shape instabilities, warranting recognition as a novel class of jets in bubbles, distinct from classical inertial jets driven by pressure gradients. We also establish a threshold for bubble radial expansion beyond which microjets form and facilitate cellular permeation. Remarkably, these microjets occur at ultrasound pressures below 100 kPa due to their unique formation mechanism. We show that the stress generated by microjetting surpasses all previously suggested mechanisms by at least an order of magnitude. In summary, this work elucidates the physics behind microbubble-mediated targeted drug delivery and provides criteria for its effective yet safe application.

Extensive work has been carried out in the past year by various organizations in an effort to determine standardized statistical channel impulse response (CIR) parameters for the newly-released mid-band spectrum (7.25 GHz -- 24.25 GHz) [1]--[5]. In this work, we show that the wireless community currently lacks a unified method for presenting key parameters required for transparency and utilization by several constituencies when presenting propagation data for use by standard bodies or third parties to create statistical CIR models. This paper aims to solve the existing problem by offering a standard method for providing key propagation parameters such as bandwidth, antenna beamwidth, noise-threshold level, and coarseness in point form, for use in creating standards or comparing parameters, rather than providing commonly-used cumulative distribution function (CDF) plots, which hide the observed key statistics on a location-by-location, point-by-point basis. The method for presenting propagation data, proposed here, may be used for statistical channel modeling of pooled datasets from many contributors, additionally also holding promise for exploring ray-tracing (e.g. site-specific) channel modeling. The indoor hotspot (InH) data collected in Spring2024 at 6.75 GHz and 16.95 GHZ by NYU WIRELESS [1]--[3] is provided for the first time in point form, to augment statistical models previously presented solely as CDFs, to demonstrate how a standardized approach to measurement data could allow others to utilize the site-specific locations and key channel parameters observed at each location, to better understand, vet, and build upon statistical or site-specific CIRs from the contributions of many different data sources., Comment: 6 pages, 1 figure, 1 table, IEEE International Communications Conference

We present detailed multipath propagation spatial statistics for next-generation wireless systems operating at lower and upper mid-band frequencies spanning 6--24 GHz. The large-scale spatial characteristics of the wireless channel include Azimuth angular Spread of Departure (ASD) and Zenith angular Spread of Departure (ZSD) of multipath components (MPC) from a transmitter and the Azimuth angular Spread of Arrival (ASA) and Zenith angular Spread of Arrival (ZSA) at a receiver. The angular statistics calculated from measurements were compared with industry-standard 3GPP models, and ASD and ASA values were found to be in close agreement at both 6.75 GHz and 16.95 GHz. Measured LOS ASD was found larger than 3GPP ASD indicating more diverse MPC departure directions in the azimuth. ZSA and ZSD were observed smaller than the 3GPP modeling results as most multipath arrivals and departures during measurements were recorded at the boresight antenna elevation. The wide angular spreads indicate a multipath-rich spatial propagation at 6.75 GHz and 16.95 GHz, showing greater promise for the implementation of MIMO beamforming systems in the mid-band spectrum., Comment: 6 pages, 3 figures, 1 table, IEEE Wireless Communications and Networking Conference

Voice cloning is a prominent feature in personalized speech interfaces. A neural vocal cloning system can mimic someone's voice using just a few audio samples. Both speaker encoding and speaker adaptation are topics of research in the field of voice cloning. Speaker adaptation relies on fine-tuning a multi-speaker generative model, which involves training a separate model to infer a new speaker embedding used for speaker encoding. Both methods can achieve excellent performance, even with a small number of cloning audios, in terms of the speech's naturalness and similarity to the original speaker. Speaker encoding approaches are more appropriate for low-resource deployment since they require significantly less memory and have a faster cloning time than speaker adaption, which can offer slightly greater naturalness and similarity. The main goal is to create a vocal cloning system that produces audio output with a Nepali accent or that sounds like Nepali. For the further advancement of TTS, the idea of transfer learning was effectively used to address several issues that were encountered in the development of this system, including the poor audio quality and the lack of available data., Comment: 6 pages, 10 figures

X-ray Computed Tomography (CT) is used to recover the true surfaces of fluid channels and fed to simulation tool (ANSYS) to create accurate cyber environment. The simulation tool also receives CT-assisted multiphase fluid profiles (belonging to the instance just before the flow starts) as an initial condition. This unique methodology is made possible by using a novel in-situ compact adaptor design is used to create fluid channels that can be placed inside any industrial X-ray CT and fulfill the above objective. It is integrated with an android based App to control the flow once placed inside CT. It is portable and compact enough: (a) to be placed inside various experimental environments, and (b) modular enough to be mounted with multi-modal systems simultaneously. Two key parameters, (a) spatial distribution and (b) the air volume fraction, are measured using two different non-invasive imaging modalities: (a) Electrical Impedance Tomography (EIT) and (d) X-ray Computed Tomography (CT). Simulated outcomes are correlated with the experimental outcomes from both EIT and X-ray CT, showing an agreement of 85 to 98 percent, respectively. Time-averaged electrically conductive fluid flow profile obtained by EIT shows a match with mass mass-attenuated fluid profile obtained by X-ray CT, justifying the utility of an in-situ adaptor. CT assistance for CFD studies can be replaced by EIT assistance as former techniques: (a) scanning time may be relatively slower than the latter, (b) it does not require rotations, (c) economical, and (d) fluid channels need not be placed inside of shielded compartment thus improving practicality. The data of analysis is shared in this work. Multimodal non-invasive imaging provides multiphase flow information, it also differentiates conductive, and mass-attenuated multiphase profiles at common cross-sections., Comment: 11 Pages, 5 figures

We present a novel realization of leptogenesis from the decays of sterile (right-handed) neutrinos (RHNs) produced from runaway bubble collisions at a first order phase transition. Such configurations can produce heavy RHNs with mass many orders of magnitude above the scale of symmetry breaking as well as the temperature of the plasma, thereby enabling high scale leptogenesis without the need for high reheat temperatures while also naturally suppressing washout effects. This mechanism also extends the window of viability to RHN masses $\gtrsim 10^{14}$ GeV, the natural scale for type-I seesaw with $\mathcal{O}(1)$ couplings, where standard thermal leptogenesis cannot produce the observed baryon asymmetry. The corresponding phase transitions are at scales $\gtrsim\!10^9$ GeV and produce gravitational wave signals that could be detected by future experiments., Comment: 30 pages, 7 figures

Acoustically vaporizable droplets are phase-change agents that can improve the effectiveness of ultrasound-based therapies. In this study, we demonstrate that the compression part of an acoustic wave can generate tension that initiates the vaporization. This counter-intuitive process is explained by the occurrence of Gouy phase shift due to the focusing of the acoustic wave inside the droplet. Our analysis unifies the existing theories for acoustic droplet vaporization under a single framework and is supported by experiments and simulations. We use our theory to identify governing parameters that allow to vaporize droplets using predominantly compression waves, which are safer in medical use.

In the GitHub ecosystem, workflows are used as an effective means to automate development tasks and to set up a Continuous Integration and Delivery (CI/CD pipeline). GitHub Actions (GHA) have been conceived to provide developers with a practical tool to create and maintain workflows, avoiding reinventing the wheel and cluttering the workflow with shell commands. Properly leveraging the power of GitHub Actions can facilitate the development processes, enhance collaboration, and significantly impact project outcomes. To expose actions to search engines, GitHub allows developers to assign them to one or more categories manually. These are used as an effective means to group actions sharing similar functionality. Nevertheless, while providing a practical way to execute workflows, many actions have unclear purposes, and sometimes they are not categorized. In this work, we bridge such a gap by conceptualizing Gavel, a practical solution to increasing the visibility of actions in GitHub. By leveraging the content of README.MD files for each action, we use Transformer--a deep learning algorithm--to assign suitable categories to the action. We conducted an empirical investigation and compared Gavel with a state-of-the-art baseline. The experimental results show that our proposed approach can assign categories to GitHub actions effectively, thus outperforming the state-of-the-art baseline., Comment: The paper has been peer-reviewed and accepted for publication in the Proceedings of the 18th International Symposium on Empirical Software Engineering and Measurement (ESEM 2024)

This paper presents an end-to-end deep learning model for Automatic Speech Recognition (ASR) that transcribes Nepali speech to text. The model was trained and tested on the OpenSLR (audio, text) dataset. The majority of the audio dataset have silent gaps at both ends which are clipped during dataset preprocessing for a more uniform mapping of audio frames and their corresponding texts. Mel Frequency Cepstral Coefficients (MFCCs) are used as audio features to feed into the model. The model having Bidirectional LSTM paired with ResNet and one-dimensional CNN produces the best results for this dataset out of all the models (neural networks with variations of LSTM, GRU, CNN, and ResNet) that have been trained so far. This novel model uses Connectionist Temporal Classification (CTC) function for loss calculation during training and CTC beam search decoding for predicting characters as the most likely sequence of Nepali text. On the test dataset, the character error rate (CER) of 17.06 percent has been achieved. The source code is available at: https://github.com/manishdhakal/ASR-Nepali-using-CNN-BiLSTM-ResNet., Comment: Accepted at 2022 International Conference on Inventive Computation Technologies (ICICT), IEEE

For decades, the terahertz (THz) frequency band had been primarily explored in the context of radar, imaging, and spectroscopy, where multi-gigahertz (GHz) and even THz-wide channels and the properties of terahertz photons offered attractive target accuracy, resolution, and classification capabilities. Meanwhile, the exploitation of the terahertz band for wireless communication had originally been limited due to several reasons, including (i) no immediate need for such high data rates available via terahertz bands and (ii) challenges in designing sufficiently high power terahertz systems at reasonable cost and efficiency, leading to what was often referred to as "the terahertz gap". This roadmap paper first reviews the evolution of the hardware design approaches for terahertz systems, including electronic, photonic, and plasmonic approaches, and the understanding of the terahertz channel itself, in diverse scenarios, ranging from common indoors and outdoors scenarios to intra-body and outer-space environments. The article then summarizes the lessons learned during this multi-decade process and the cutting-edge state-of-the-art findings, including novel methods to quantify power efficiency, which will become more important in making design choices. Finally, the manuscript presents the authors' perspective and insights on how the evolution of terahertz systems design will continue toward enabling efficient terahertz communications and sensing solutions as an integral part of next-generation wireless systems., Comment: 31 pages. Accepted to the Proceedings of the IEEE, 2024. \c{opyright}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, creating new works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

This paper introduces Waste Factor (W), also denoted as Waste Figure (WF) in dB, a promising new metric for quantifying energy efficiency in a wide range of circuits and systems applications, including data centers and RANs. Also, the networks used to connect data centers and AI computing engines with users for ML applications must become more power efficient. This paper illustrates the limitations of existing energy efficiency metrics that inadequately capture the intricate energy dynamics of RAN components. We delineate the methodology for applying W across various network configurations, including MISO, SIMO, and MIMO systems, and demonstrate the effectiveness of W in identifying energy optimization opportunities. Our findings reveal that W not only offers nuanced insights into the energy performance of RANs but also facilitates informed decision-making for network design and operational efficiency. Furthermore, we show how W can be integrated with other KPIs to guide the development of optimal strategies for enhancing network energy efficiency under different operational conditions. Additionally, we present simulation results for a distributed multi-user MIMO system at 3.5, 17, and 28 GHz, demonstrating overall network power efficiency on a per square kilometer basis, and show how overall W decreases with an increasing number of base stations and increasing carrier frequency. This paper shows that adopting W as a figure of merit can significantly contribute to the sustainability and energy optimization of next-generation wireless communication networks, paving the way for greener and more sustainable, energy-efficient 5G and 6G technologies., Comment: 28 pages, 21 figures, 5 tables

The 4--8 GHz FR1(C) and 7--24 GHz upper mid-band FR3 spectrum are promising new 6G spectrum allocations being considered by the International Telecommunications Union (ITU) and major governments around the world. There is an urgent need to understand the propagation behavior and radio coverage, outage, and material penetration for the global mobile wireless industry in both indoor and outdoor environments in these emerging frequency bands. This work presents measurements and models that describe the penetration loss in co-polarized and cross-polarized antenna configurations, exhibited by common materials found inside buildings and on building perimeters, including concrete, low-emissivity glass, wood, doors, drywall, and whiteboard at 6.75 GHz and 16.95 GHz. Measurement results show consistent lower penetration loss at 6.75 GHz compared to 16.95 GHz for all ten materials measured for co and cross-polarized antennas at incidence. For instance, the low-emissivity glass wall presents 33.7 dB loss at 6.75 GHz, while presenting 42.3 dB loss at 16.95 GHz. Penetration loss at these frequencies is contrasted with measurements at sub-6 GHz, mmWave and sub-THz frequencies along with 3GPP material penetration loss models. The results provide critical knowledge for future 5G and 6G cellular system deployments as well as refinements for the 3GPP material penetration models., Comment: 6 pages, 4 figures, 2 tables, IEEE GLOBECOM 2024

New spectrum allocations in the 4--8 GHz FR1(C) and 7--24 GHz FR3 mid-band frequency spectrum are being considered for 5G/6G cellular deployments. This paper presents results from the world's first comprehensive indoor hotspot (InH) propagation measurement campaign at 6.75 GHz and 16.95 GHz in the NYU WIRELESS Research Center using a 1 GHz wideband channel sounder system over distances from 11 to 97 m in line-of-sight (LOS) and non-LOS (NLOS). Analysis of directional and omnidirectional path loss (PL) using the close-in free space 1 m reference distance model shows a familiar waveguiding effect in LOS with an omnidirectional path loss exponent (PLE) of 1.40 at 6.75 GHz and 1.32 at 16.95 GHz. Compared to mmWave frequencies, the directional NLOS PLEs are lower at FR3 and FR1(C), while omnidirectional NLOS PLEs are similar, suggesting better propagation distances at lower frequencies for links with omnidirectional antennas at both ends of the links, but also, importantly, showing that higher gain antennas will offer better coverage at higher frequencies when antenna apertures are kept same over all frequencies. Comparison of the omnidirectional and directional RMS delay spread (DS) at FR1(C) and FR3 with mmWave frequencies indicates a clear decrease with increasing frequency. The mean spatial lobe and omnidirectional RMS angular spread (AS) is found to be wider at 6.75 GHz compared to 16.95 GHz indicating more multipath components are found in the azimuthal spatial domain at lower frequencies., Comment: 6 pages, 7 figures, 4 tables, IEEE GLOBECOM 2024

This paper introduces Waste Factor (W) and Waste Figure (WF) to assess power efficiency in any multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) or multiple-input single-output (MISO) cascaded communication system. This paper builds upon the new theory of Waste Factor, which systematically models added wasted power in any cascade for parallel systems such as MISO, SIMO, and MIMO systems, which are prevalent in current wireless networks. Here, we also show the advantage of W compared to conventional metrics for quantifying and analyzing energy efficiency. This work explores the utility of W in assessing energy efficiency in communication channels, within Radio Access Networks (RANs)., Comment: 6 pages, 6 figures

Recent language models use subwording mechanisms to handle Out-of-Vocabulary(OOV) words seen during test time and, their generation capacity is generally measured using perplexity, an intrinsic metric. It is known that increasing the subword granularity results in a decrease of perplexity value. However, the study of how subwording affects the understanding capacity of language models has been very few and only limited to a handful of languages. To reduce this gap we used 6 different tokenization schemes to pretrain relatively small language models in Nepali and used the representations learned to finetune on several downstream tasks. Although byte-level BPE algorithm has been used in recent models like GPT, RoBERTa we show that on average they are sub-optimal in comparison to algorithms such as SentencePiece in finetuning performances for Nepali. Additionally, similar recent studies have focused on the Bert-based language model. We, however, pretrain and finetune sequential transformer-based language models., Comment: 11 pages

The task of Spell Correction(SC) in low-resource languages presents a significant challenge due to the availability of only a limited corpus of data and no annotated spelling correction datasets. To tackle these challenges a small-scale word-based transformer LM is trained to provide the SC model with contextual understanding. Further, the probabilistic error rules are extracted from the corpus in an unsupervised way to model the tendency of error happening(error model). Then the combination of LM and error model is used to develop the SC model through the well-known noisy channel framework. The effectiveness of this approach is demonstrated through experiments on the Nepali language where there is access to just an unprocessed corpus of textual data., Comment: 8 pages

Granular materials are ubiquitous in nature and industry; their mechanical behavior has been of academic and engineering interest for centuries. One of the reasons for their rather complex mechanical behavior is that stresses exerted on a granular material propagate only through contacts between the grains. These contacts can change as the packing evolves. This makes any deformation and mechanical response from a granular packing a function of the nature of contacts between the grains and the material response of the material the grains are made of. We present a study in which we isolate the role of the grain material in the contact forces acting between two particles sliding past each other. We use hydrogel particles and find that a viscoelastic material model, in which the shear modulus decays with time, coupled with a simple Coulomb friction model captures the experimental results. The results suggest that the particle material evolution itself may play a role in the collective behavior of granular materials., Comment: 11 pages, 9 figures

Visual Inspection with Acetic Acid (VIA) remains the most feasible cervical cancer screening test in resource-constrained settings of low- and middle-income countries (LMICs), which are often performed screening camps or primary/community health centers by nurses instead of the preferred but unavailable expert Gynecologist. To address the highly subjective nature of the test, various handheld devices integrating cameras or smartphones have been recently explored to capture cervical images during VIA and aid decision-making via telemedicine or AI models. Most studies proposing AI models retrospectively use a relatively small number of already collected images from specific devices, digital cameras, or smartphones; the challenges and protocol for quality image acquisition during VIA in resource-constrained camp settings, challenges in getting gold standard, data imbalance, etc. are often overlooked. We present a novel approach and describe the end-to-end design process to build a robust smartphone-based AI-assisted system that does not require buying a separate integrated device: the proposed protocol for quality image acquisition in resource-constrained settings, dataset collected from 1,430 women during VIA performed by nurses in screening camps, preprocessing pipeline, and training and evaluation of a deep-learning-based classification model aimed to identify (pre)cancerous lesions. Our work shows that the readily available smartphones and a suitable protocol can capture the cervix images with the required details for the VIA test well; the deep-learning-based classification model provides promising results to assist nurses in VIA screening; and provides a direction for large-scale data collection and validation in resource-constrained settings.

We study nonthermal production of heavy dark matter from the dynamics of the background scalar field during a first-order phase transition, predominantly from bubble collisions. In scenarios where bubble walls achieve runaway behavior and get boosted to very high energies, we find that it is possible to produce dark matter with mass several orders of magnitude above the symmetry breaking scale or the highest temperature ever reached by the thermal plasma. We also demonstrate that the existing formalism for calculating particle production from bubble dynamics in a first-order phase transition is not gauge invariant, and can lead to spurious results. While a rigorous and complete resolution of this problem is still lacking, we provide a practical prescription for the computation that avoids unphysical contributions and should provide reliable order-of-magnitude estimates of this effect. Furthermore, we point out the importance of three-body decays of the background field excitations into scalars and gauge bosons, which provide the dominant contributions at energy scales above the scale of symmetry breaking. Using our improved results, we find that scalar, fermion, and vector dark matter are all viable across a large range of mass scales, from O(10) TeV to a few orders of magnitude below the Planck scale, and the corresponding phase transitions can be probed with current and future gravitational wave experiments., Comment: 41 pages, 3 figures

We propose a novel nonorthogonal multiple access (NOMA) scheme referred as adaptive constellation multiple access (ACMA) which addresses key limitations of existing NOMA schemes for beyond 5G wireless systems. Unlike the latter, that are often constrained in choices of allocation of power, modulations and phases to allow enough separation of clusters from users combined signals, ACMA is power, modulation and phase agnostic forming unified constellations instead where distances of all possible neighbouring points are optimized. It includes an algorithm at basestation (BS) calculating phase offsets for users signals such that, when combined, it gives best minimum Euclidean distance of points from all possibilities. The BS adaptively changes the phase offsets whenever system parameters change. We also propose an enhanced receiver using a modified maximum likelihood (MML) method that dynamically exploits information from the BS to blindly estimate correct phase offsets and exploit them to enhance data rate and error performances. Superiority of this scheme, which may also be referred to as AC NOMA, is verified through extensive analyses and simulations., Comment: 5 pages, 6 figures, Submission to an IEEE Journal

This paper presents an innovative and cost-effective approach to understanding the Barkhausen effect through the design and implementation of an educational kit. The Barkhausen effect, characterized by Barkhausen noise (BN) during magnetization changes in soft magnetic materials, is explored for its application in probing hysteresis properties and magnetization dynamics. The study investigates scaling properties, categorizing ferromagnetic materials based on scaling exponents. The primary contribution is the introduction of a practical and accessible kit for hands-on Barkhausen Effect demonstrations, revolutionizing the educational experience. This kit enables students to not only comprehend the intricacies of BN but also calculate the scaling constant ($\tau$) for Soft Iron samples. The paper demonstrates the successful construction of the kit, its signal amplification capabilities, and data collection accuracy, showcasing its potential for widespread educational use.

Explanations on relational data are hard to verify since the explanation structures are more complex (e.g. graphs). To verify interpretable explanations (e.g. explanations of predictions made in images, text, etc.), typically human subjects are used since it does not necessarily require a lot of expertise. However, to verify the quality of a relational explanation requires expertise and is hard to scale-up. GNNExplainer is arguably one of the most popular explanation methods for Graph Neural Networks. In this paper, we develop an approach where we assess the uncertainty in explanations generated by GNNExplainer. Specifically, we ask the explainer to generate explanations for several counterfactual examples. We generate these examples as symmetric approximations of the relational structure in the original data. From these explanations, we learn a factor graph model to quantify uncertainty in an explanation. Our results on several datasets show that our approach can help verify explanations from GNNExplainer by reliably estimating the uncertainty of a relation specified in the explanation., Comment: Published in Proceedings of 2023 IEEE Conference on Big Data

The standard approach to verify representations learned by Deep Neural Networks is to use them in specific tasks such as classification or regression, and measure their performance based on accuracy in such tasks. However, in many cases, we would want to verify more complex properties of a learned representation. To do this, we propose a framework based on a probabilistic first-order language, namely, Hybrid Markov Logic Networks (HMLNs) where we specify properties over embeddings mixed with symbolic domain knowledge. We present an approach to learn parameters for the properties within this framework. Further, we develop a verification method to test embeddings in this framework by encoding this task as a Mixed Integer Linear Program for which we can leverage existing state-of-the-art solvers. We illustrate verification in Graph Neural Networks, Deep Knowledge Tracing and Intelligent Tutoring Systems to demonstrate the generality of our approach., Comment: 6 pages, Proceedings of 23rd IEEE International Conference on Data Mining 2023 (ICDM'23)