Huda Al-Saedi, Pedram Salehpour, Seyyed Hadi Aghdasi
Robot navigation refers to a robot’s ability to determine its position within a reference frame and plan a path to a target location. Visual navigation, which relies on visual sensors such as cameras, is one approach to this problem. Among visual navigation methods, Visual Teach and Repeat (VT&R) techniques are commonly used. To develop an effective robot navigation framework based on the VT&R method, accurate and fast depth estimation of the scene is essential. In recent years, event cameras have garnered significant interest from machine vision researchers due to their numerous advantages and applicability in various environments, including robotics and drones. However, the main gap is how these cameras are used in a navigation system. The current research uses the attention-based UNET neural network to estimate the depth of a scene using an event camera. The attention-based UNET structure leads to accurate depth detection of the scene. This depth information is then used, together with a hybrid deep neural network consisting of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), for robot navigation. Simulation results on the DENSE dataset yield an RMSE of 8.15, which is an acceptable result compared to other similar methods. This method not only provides good accuracy but also operates at high speed, making it suitable for real-time applications and visual navigation methods based on VT&R.
Duong Thi Kim Chi, Nguyen Thi Mai Trang, Tran Ba Minh Son
et al.
Accurate time-series classification (TSC) remains a fundamental challenge in deep learning due to the complexity and variability of temporal patterns. While recurrent neural networks (RNNs) such as LSTM and GRU have shown promise in modeling sequential dependencies, they often suffer from limitations like vanishing gradients and high computational cost when handling long sequences. To overcome these issues, convolutional neural networks (CNNs), particularly the Inception architecture, have emerged as powerful alternatives due to their ability to capture multiscale local patterns efficiently. In this study, we propose InceptionResNet, a hybrid deep learning framework that integrates the residual learning mechanism of ResNet into the InceptionTime architecture. By replacing the fully convolutional network (FCN) shortcut module in InceptionFCN with ResNet-50, the model gains deeper representational capacity and improved gradient flow during training. We conduct extensive experiments on the UCR-85 benchmark dataset, comparing our model against state-of-the-art approaches, including InceptionTime, InceptionFCN, ResNet, FCN, and MLP. The results show that InceptionResNet achieves superior accuracy on 49 of 85 datasets, demonstrating its robustness and effectiveness in handling diverse and complex time series data. This work highlights the potential of integrating multiscale feature extraction and deep residual learning to advance the performance of TSC models in practical applications.
Social Cognitive Career Theory (SCCT) has been extensively employed to elucidate the enduring gender differences in mathematics-intensive fields, with a particular emphasis on the complex interplay of motivational factors and extra-personal influences contributing to the underrepresentation of women. Although a plethora of empirical studies corroborate SCCT, three crucial aspects for refinement have come to the fore. First, the theory should place a more substantial emphasis on how cultural and contextual diversity influences academic choices. Second, given the dynamic nature of motivation, which evolves over time, more longitudinal analyses are imperative to capture their temporal trajectory, in contrast to the predominantly cross-sectional empirical studies. Finally, considering the intricate interplay between emotion and motivation, integrating the dimension of emotion into SCCT would significantly augment its explanatory power and provide a more comprehensive understanding of academic selection processes.
We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothesis-driven signal generation with reinforcement learning and strict out-of-sample testing. The framework enforces strict information set discipline, employs rolling window validation across 34 independent test periods, maintains complete interpretability through natural language hypothesis explanations, and incorporates realistic transaction costs and position constraints. Validating five market microstructure patterns across 100 US equities from 2015 to 2024, the system yields modest annualized returns (0.55%, Sharpe ratio 0.33) with exceptional downside protection (maximum drawdown -2.76%) and market-neutral characteristics (beta = 0.058). Performance exhibits strong regime dependence, generating positive returns during high-volatility periods (0.60% quarterly, 2020-2024) while underperforming in stable markets (-0.16%, 2015-2019). We report statistically insignificant aggregate results (p-value 0.34) to demonstrate a reproducible, honest validation protocol that prioritizes interpretability and extends naturally to advanced hypothesis generators, including large language models. The key empirical finding reveals that daily OHLCV-based microstructure signals require elevated information arrival and trading activity to function effectively. The framework provides complete mathematical specifications and open-source implementation, establishing a template for rigorous trading system evaluation that addresses the reproducibility crisis in quantitative finance research. For researchers, practitioners, and regulators, this work demonstrates that interpretable algorithmic trading strategies can be rigorously validated without sacrificing transparency or regulatory compliance.
In recent years, model compression techniques have gained significant attention as a means to reduce the computational and memory requirements of deep neural networks. Knowledge distillation and pruning are two prominent approaches in this domain, each offering unique advantages in achieving model efficiency. This paper investigates the combined effects of knowledge distillation and two pruning strategies, weight pruning and channel pruning, on enhancing compression efficiency and model performance. The study introduces a metric called “Performance Efficiency” to evaluate the impact of these pruning strategies on model compression and performance. Our research is conducted on the popular datasets CIFAR-10 and CIFAR-100. We compared diverse model architectures, including ResNet, DenseNet, EfficientNet, and MobileNet. The results emphasize the efficacy of both weight and channel pruning in achieving model compression. However, a significant distinction emerges, with weight pruning showing superior performance across all four architecture types. We realized that the weight pruning method better adapts to knowledge distillation than channel pruning. Pruned models show a significant reduction in parameters without a significant reduction in accuracy.
I am utilizing a brand-new simulation function that has previously been developed by eminent mathematicians and that uses fuzzy metric-like spaces to establish new fixed point theorems. Here, this is demonstrated that the current conclusion is unquestionably a unified one that can generalize earlier current results. To further demonstrate the relevance of my findings, a few additional theorems and corollaries are demonstrated. Additionally, several excellent examples are provided to show how useful my findings are. I provide an application of my major finding in the conclusion.
We establish new conformable fractional Hermite-Hadamard (H–H) Mercer type inequalities for harmonically convex functions using the concept of support line. We introduce two new conformable fractional auxiliary equalities in the Mercer sense and apply them to differentiable functions with harmonic convexity. We also use Power-mean, Hölder’s and improved Hölder inequality to derive new Mercer type inequalities via conformable fractional integrals. The accuracy and superiority of the offered technique are clearly depicted through impactful visual illustrations. We also use our technique to derive new estimates for hypergeometric functions and special means of real numbers that are more precise than existing ones. Some applications are provided as well. Our results generalize and extend some existing ones in the literature.
Anton A. Alyakin, Joshua Agterberg, Hayden S. Helm
et al.
Abstract Random graphs are statistical models that have many applications, ranging from neuroscience to social network analysis. Of particular interest in some applications is the problem of testing two random graphs for equality of generating distributions. Tang et al. (Bernoulli 23:1599–1630, 2017) propose a test for this setting. This test consists of embedding the graph into a low-dimensional space via the adjacency spectral embedding (ASE) and subsequently using a kernel two-sample test based on the maximum mean discrepancy. However, if the two graphs being compared have an unequal number of vertices, the test of Tang et al. (Bernoulli 23:1599–1630, 2017) may not be valid. We demonstrate the intuition behind this invalidity and propose a correction that makes any subsequent kernel- or distance-based test valid. Our method relies on sampling based on the asymptotic distribution for the ASE. We call these altered embeddings the corrected adjacency spectral embeddings (CASE). We also show that CASE remedies the exchangeability problem of the original test and demonstrate the validity and consistency of the test that uses CASE via a simulation study. Lastly, we apply our proposed test to the problem of determining equivalence of generating distributions in human connectomes extracted from diffusion magnetic resonance imaging at different scales.
We demonstrate the continuous broadband microwave receivers based on AC Stark shifts and Floquet States of Rydberg levels in a cesium atomic vapor cell. The resonant transition frequency of two adjacent Rydberg states 78$S_{1/2}$ and 78$P_{1/2}$ is tuned based on AC Stark effect of 70~MHz Radio frequency (RF) field that is applied outside the vapor cell. Meanwhile, the Rydberg states also exhibit Floquet even-order sidebands that are used to extend the bandwidths further. We achieve microwave electric field measurements over 1.172~GHz of continuous frequency range. The sensitivity of the Rydberg receiver with heterodyne technique in the absence of RF field is 280.2~nVcm$^{-1}$Hz$^{-1/2}$, while it is dramatically decreased with tuning the resonant transition frequency in the presence of RF field. Surprisingly, the sensitivity can be greatly improved if the microwave field couples the Floquet sideband transition. The achieving of continuous frequency and high sensitivity microwave detection will promote the application of Rydberg receiver in the radar technique and wireless communication.
Eduardo Arrufat-Pié, Mario Estévez-Báez, José Mario Estévez-Carreras
et al.
This study investigates the use of empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs) for the spectral analysis of EEG signals in healthy individuals and its possible biological interpretations. Unlike traditional EEG analysis, this approach does not require the establishment of arbitrary band limits. The study uses a multivariate EMD algorithm (APIT-MEMD) to extract IMFs from the EEG signals of 34 healthy volunteers. The first six IMFs are analyzed using two different methods, based on FFT and HHT, and the results compared using the ANOVA test and the Bland–Altman method for agreement test. The outcomes show that the frequency values of the first six IMFs fall within the range of classic EEG bands (1.72–52.4 Hz). Although there was a lack of agreement in the mean weighted frequency values of the first three IMFs between the two methods (>3 Hz), both methods showed similar results for power spectral density (<5% normalized units, %, of power spectral density). The HHT method is found to have better frequency resolution than APIT-MEMD associated with FTT that produce less overlapping between IMF3 and 4 (<i>p</i> = 0.0046) and it is recommended for analyzing the spectral properties of IMFs. The study concludes that the HHT method could help to avoid the assumption of strict frequency band limits, and that the potential impact of EEG physiological phenomenon on mode-mixing interpretation, particularly for the alpha and theta ranges, must be considered in future research.
Armando M. V. Corro, Carlos M. C. Riveros, Marcelo L. Ferro
In this paper, we provide a class of surfaces called ϵ-isothermic surface in the pseudo-Euclidean 3-space and we introduce the pseudo-Calapso equation. We prove that for each ϵ-isothermic surface, we can associate two solutions to the pseudo-Calapso equation. In particular, we associate solutions to the Calapso, Zoomeron and Davey-Stewartson III equations. In sequence, we classify the Dupin surfaces in pseudo-Euclidean 3-space having distinct principal curvatures and provide explicit coordinates for such surfaces.
As application of the theory, we obtain explicit solutions to the pseudo-Calapso equation and from these solutions, we provide new explicit solutions of the Zoomeron and Davey-Stewartson III equations. Moreover, we also provide explicit solutions to these equations that depend on ϵ2−holomorphic functions.
Gabriel Obed Fosu, Joseph M. Opong, Bright E. Owusu
et al.
The continual wearing of road surfaces results to crack and holes called potholes. These road surface irregularities often elongate travel time. In this paper, a second-order macroscopic traffic model is therefore proposed to account for these road surface irregularities that affect the smooth flow of vehicular traffic. Though potholes do vary in shape and size, for simplicity the paper assumes that all potholes have conic resemblances. The impact of different sized potholes on driving is experimented using fundamental diagrams. Besides, the width of these holes, driver reaction time amid these irregularities also determine the intensity of the flow rate and vehicular speed. Moreover, a local cluster analysis is performed to determine the effect of a small disturbance on flow. The results revealed that the magnitude of amplification on a road surface with larger cracks is not as severe as roads with smaller size holes, except at minimal and jam density where all amplifications quickly fade out.
Yuki Yamagishi, Kazumi Saito, Kazuro Hirahara
et al.
Abstract It is expected that the pronounced decrease in b-value of the Gutenberg–Richter law for some region during some time interval can be a promising precursor in forecasting earthquakes with large magnitudes, and thus we address the problem of automatically identifying such spatio-temporal change points as several clusters consisting of earthquakes whose b-values are substantially smaller than the total one. For this purpose, we propose a new method consisting of two phases: tree construction and tree separation. In the former phase, we employ one of two different declustering algorithms called single-link and correlation-metric developed in the field of seismology, while in the later phase, we employ a variant of the change-point detection algorithm, developed in the field of data mining. In the later phase, we also employ one of two different types of objective functions, i.e., the average magnitude which is inversely proportional to the b-value, and the likelihood function based on the Gutenberg–Richter law. Here note that since the magnitudes of most earthquakes are relatively small, we formulate our problem so as to produce one relatively large cluster and the other small clusters having substantially larger average magnitudes or smaller b-values. In addition, in order to characterize some properties of our proposed methods, we present a method of analyzing magnitude correlation over an earthquake network. In our empirical evaluation using earthquake catalog data covering the whole of Japan, we show that our proposed method employing the single-link strategy can produce more desirable results for our purpose in terms of the improvement of weighted sums of variances, average logarithmic likelihoods, visualization results, and magnitude correlation analyses.
Matrix regularity is a key to various problems in applied mathematics. The sufficient conditions, used for checking regularity of interval parametric matrices, usually fail in case of large parameter intervals. We present necessary and sufficient conditions for regularity of interval parametric matrices in terms of boundary parametric hypersurfaces, parametric solution sets, determinants, real spectral radiuses. The initial n-dimensional problem involving K interval parameters is replaced by numerous problems involving 1<= t <= min(n-1, K) interval parameters, in particular t=1 is most attractive. The advantages of the proposed methodology are discussed along with its application for finding the interval hull solution to interval parametric linear system and for determining the regularity radius of an interval parametric matrix.
J. Bedrossian, A. Blumenthal, Samuel Punshon-Smith
We put forward a new method for obtaining quantitative lower bounds on the top Lyapunov exponent of stochastic differential equations. Our method combines (i) a new identity connecting the top Lyapunov exponent to a Fisher information-like functional of the stationary density of the Markov process tracking tangent directions with (ii) a novel, quantitative version of Hörmander’s hypoelliptic regularity theory in an L1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L^1$$\end{document} framework which estimates this (degenerate) Fisher information from below by a Wlocs,1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$W^{s,1}_{\mathrm {loc}}$$\end{document} Sobolev norm. This method is applicable to a wide range of systems beyond the reach of currently existing mathematically rigorous methods. As an initial application, we prove the positivity of the top Lyapunov exponent for a class of weakly-dissipative, weakly forced stochastic differential equations; in this paper we prove that this class includes the Lorenz 96 model in any dimension, provided the additive stochastic driving is applied to any consecutive pair of modes.