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We introduce multi-split continuous functions between topological spaces, a weaker form of continuity that generalizes split continuity while being stable under compositions. We will define the associated star multifunction and pre-multi-split multifunctions. Moreover, we will prove that multi-split continuity naturally emerges as the continuity property of selections of finite usco maps, relating their study to set-valued analysis. Finally, we introduce split homeomorphisms and split homeomorphic spaces, showing that for compact, regular Hausdorff spaces, split homeomorphisms characterize deformations with cuts and subsequent re-glues., Comment: 26 pages, 0 figures

Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in human-computer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES's highlights is the use of a novel adaptive windowing/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within an event frame, across a wide range of eye movement patterns. EyeTrAES then applies lightweight image processing functions over accumulated event frames from just a single eye to perform pupil segmentation and tracking. We show that these methods boost pupil tracking fidelity by 6+%, achieving IoU~=92%, while incurring at least 3x lower latency than competing pure event-based eye tracking alternatives [38]. We additionally demonstrate that the microscopic pupillary motion captured by EyeTrAES exhibits distinctive variations across individuals and can thus serve as a biometric fingerprint. For robust user authentication, we train a lightweight per-user Random Forest classifier using a novel feature vector of short-term pupillary kinematics, comprising a sliding window of pupil (location, velocity, acceleration) triples. Experimental studies with two different datasets demonstrate that the EyeTrAES-based authentication technique can simultaneously achieve high authentication accuracy (~=0.82) and low processing latency (~=12ms), and significantly outperform multiple state-of-the-art competitive baselines., Comment: 32 pages,15 figures

Test time adaptation (TTA) equips deep learning models to handle unseen test data that deviates from the training distribution, even when source data is inaccessible. While traditional TTA methods often rely on entropy as a confidence metric, its effectiveness can be limited, particularly in biased scenarios. Extending existing approaches like the Pseudo Label Probability Difference (PLPD), we introduce ETAGE, a refined TTA method that integrates entropy minimization with gradient norms and PLPD, to enhance sample selection and adaptation. Our method prioritizes samples that are less likely to cause instability by combining high entropy with high gradient norms out of adaptation, thus avoiding the overfitting to noise often observed in previous methods. Extensive experiments on CIFAR-10-C and CIFAR-100-C datasets demonstrate that our approach outperforms existing TTA techniques, particularly in challenging and biased scenarios, leading to more robust and consistent model performance across diverse test scenarios. The codebase for ETAGE is available on https://github.com/afsharshamsi/ETAGE.

Human activity recognition (HAR) is essential in healthcare, elder care, security, and human-computer interaction. The use of precise sensor data to identify activities passively and continuously makes HAR accessible and ubiquitous. Specifically, millimeter wave (mmWave) radar is promising for passive and continuous HAR due to its ability to penetrate non-metallic materials and provide high-resolution wireless sensing. Although mmWave sensors are effective at capturing macro-scale activities, like exercising, they fail to capture micro-scale activities, such as typing. In this paper, we introduce mmDoppler, a novel dataset that utilizes off-the-shelf (COTS) mmWave radar in order to capture both macro and micro-scale human movements using a machine-learning driven signal processing pipeline. The dataset includes seven subjects performing 19 distinct activities and employs adaptive doppler resolution to enhance activity recognition. By adjusting the radar's doppler resolution based on the activity type, our system captures subtle movements more precisely. mmDoppler includes range-doppler heatmaps, offering detailed motion dynamics, with data collected in a controlled environment with single as well as multiple subjects performing activities simultaneously. The dataset aims to bridge the gap in HAR systems by providing a more comprehensive and detailed resource for improving the robustness and accuracy of mmWave radar activity recognition., Comment: 16 pages. arXiv admin note: text overlap with arXiv:2309.11957

TALYS calculations were performed to obtain the theoretical proton capture cross-sections on the p-nuclei. A short review on the status of related experimental studies was also conducted. Some basic properties such as Q-values, Coulomb barrier, Gamow peak, Gamow Window, and decay properties of the parent and daughter nuclei were studied. Various experimental parameters, e.g., beam energy, beam current, targets, and detectors, used in experimental investigations reported in the literature, were tabulated. The results of the TALYS calculations in the Gamow region were compared with the corresponding experimental values wherever available. This study is expected to facilitate the planning of future experiments., Comment: 13 pages, 4 figures

We introduce an efficient discretization of a novel fractional-order adaptive exponential (FrAdEx) integrate-and-fire model, which is used to study the fractional-order dynamics of neuronal activities. The discretization is based on extension of L1-type methods that can accurately handle the exponential growth and the spiking mechanism of the model. This new method is implicit and uses adaptive time stepping to robustly handle the stiff system that arises due to the exponential term. The implicit nonlinear system can be solved exactly, without the need for iterative methods, making the scheme efficient while maintaining accuracy. We present a complete error model for the numerical scheme that can be extended to other integrate-and-fire models with minor changes. To show the feasibility of our approach, the numerical method has been rigorously validated and used to investigate several different spiking oscillations of the model. We observed that the fractional-order model is capable of predicting biophysical activities, which are interpreted through phase diagrams describing the transition from one firing type to another. This simple model shows significant promise, as it has sufficient expressive dynamics to reproduce several features qualitatively from a biophysical dynamical perspective., Comment: 27 pages, 9 figures. v1: changed title, updated references

Molecular transport maintains cellular structures and functions. For example, lipid and protein diffusion sculpts the dynamic shapes and structures on the cell membrane that perform essential cellular functions, such as cell signaling. Temperature variations in thermal equilibrium rapidly change molecular transport properties. The coefficient of lipid self-diffusion increases exponentially with temperature in thermal equilibrium, for example. Hence, in the noisy cellular environment, where temperatures can fluctuate widely due to local heat generation, maintaining cellular homeostasis through molecular transport is hard in thermal equilibrium. In this paper, using both molecular and lattice-based modeling of membrane transport, we show that the presence of active transport originating from the cell's cytoskeleton can make the self-diffusion of the molecules on the membrane robust to temperature fluctuations. The resultant temperature-independence of self-diffusion keeps the precision of cellular signaling invariant over a broad range of ambient temperatures, allowing cells to make robust decisions. We have also found that the Kawasaki algorithm, the widely used model of lipid transport on lattices, predicts incorrect temperature dependence of lipid self-diffusion in equilibrium. We propose a new algorithm that correctly captures the equilibrium properties of lipid self-diffusion and reproduces experimental observations., Comment: The movie is available in the ancillary folder

Precise ego-motion measurement is crucial for various applications, including robotics, augmented reality, and autonomous navigation. In this poster, we propose mmPhase, an odometry framework based on single-chip millimetre-wave (mmWave) radar for robust ego-motion estimation in mobile platforms without requiring additional modalities like the visual, wheel, or inertial odometry. mmPhase leverages a phase-based velocity estimation approach to overcome the limitations of conventional doppler resolution. For real-world evaluations of mmPhase we have developed an ego-vehicle prototype. Compared to the state-of-the-art baselines, mmPhase shows superior performance in ego-velocity estimation.

This study overviews and extends a recently developed stochastic finite-temperature Kohn-Sham density functional theory to study warm dense matter using Langevin dynamics, specifically under periodic boundary conditions. The method's algorithmic complexity exhibits nearly linear scaling with system size and is inversely proportional to the temperature. Additionally, a novel linear-scaling stochastic approach is introduced to assess the Kubo-Greenwood conductivity, demonstrating exceptional stability for DC conductivity. Utilizing the developed tools, we investigate the equation of state, radial distribution, and electronic conductivity of Hydrogen at a temperature of 30,000K. As for the radial distribution functions, we reveal a transition of Hydrogen from gas-like to liquid-like behavior as its density exceeds $4 g/cm^3$. As for the electronic conductivity as a function of the density, we identified a remarkable isosbestic point at frequencies around 7eV, which may be an additional signature of a gas-liquid transition in Hydrogen at 30,000K.

In the landscape of next-generation cellular networks, a projected surge of over 12 billion subscriptions foreshadows a considerable upswing in the network's overall energy consumption. The proliferation of User Equipment (UE) drives this energy demand, urging 5G deployments to seek more energy-efficient methodologies. In this work, we propose SmartMME, as a pivotal solution aimed at optimizing Base Station (BS) energy usage. By harnessing and analyzing critical network states-such as UE connections, data traffic at individual UEs, and other pertinent metrics-our methodology intelligently orchestrates the BS's power states, making informed decisions on when to activate or deactivate the BS. This meticulous approach significantly curtails the network's overall energy consumption. In a bid to validate its efficiency, we seamlessly integrated our module into Network Simulator-3 (ns-3), conducting extensive testing to demonstrate its prowess in effectively managing and reducing net energy consumption. As advocates of collaborative progress, we've opted to open-source this module, inviting the engagement and feedback of the wider research community on GitHub.