New method predicts propagation losses at 169 MHz for smart metering.
problem Radio planning for 169 MHz smart metering networks.
method Support Vector Machine techniques for classification and regression.
result Good accuracy achieved at low computational cost and minimal measurement effort.
Deep learning identifies wireless interference sources efficiently.
problem Interference source identification in 2.4 GHz ISM Band.
method Deep learning algorithms trained on 10 MHz band samples.
result CNN architecture reduces training time by 60% with minimal accuracy loss.
Cubature on Wiener space [Lyons, T.; Victoir, N.; Proc. R. Soc. Lond. A 8 January 2004 vol. 460 no. 2041 169-198] provides a powerful alternative to Monte Carlo simulation for the integration of certain functionals on Wiener space. More specifically, and in the language of mathematical finance, cubature allows for fast…
A detailed study of the notions of convexity for a hypersurface in a Finsler manifold is carried out. In particular, the infinitesimal and local notions of convexity are shown to be equivalent. Our approach differs from Bishop's one in his classical result (Bishop, Indiana Univ Math J 24:169-172, 1974) for the Riemanni…
We show that the shortfall risk of binomial approximations of game (Israeli) options converges to the shortfall risk in the corresponding Black--Scholes market considering Lipschitz continuous path-dependent payoffs for both discrete- and continuous-time cases. These results are new also for usual American style option…
Cubature methods, a powerful alternative to Monte Carlo due to Kusuoka~[Adv.~Math.~Econ.~6, 69--83, 2004] and Lyons--Victoir~[Proc.~R.~Soc.\\Lond.~Ser.~A 460, 169--198, 2004], involve the solution to numerous auxiliary ordinary differential equations. With focus on the Ninomiya-Victoir algorithm~[Appl.~Math.~Fin.~15, 1…
Paper shows large models are vulnerable to poisoning attacks.
problem Vulnerability of large machine learning models to poisoning attacks.
method Analyzed isotropic random feature vectors and geometric median as robust gradient aggregator rule.
result Linear and logistic regressions with D≥169H2/P2 parameters are vulnerable to arbitrary manipulation by poisoners. PHAZE framework uses zkML and hashing for fast, verifiable LHC trigger decisions.
problem Inefficient inference on large machine learning models for LHC trigger performance.
method Cryptographic techniques like hashing and zkML for low latency, certifiable inference.
result Achieves nanosecond-order latency for LHC triggers, enabling dynamic low-level triggers.
Study compares handcrafted and deep neural network features for OCT image classification.
problem Classifying OCT images for disease detection.
method Comparison of Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP), DenseNet-169, and ResNet50.
result Deep neural network methods outperform handcrafted features with higher accuracy and better underrepresented class performance.
The paper extends optimal investment and consumption strategies to include jumps in asset prices.
problem Optimal investment and consumption with downside risk constraint in jump-diffusion models.
method Extends results to jump-diffusion setting, finds explicit optimal strategy under certain constraints.
result Explicit optimal strategy can be found in subset of admissible strategies under positive jumps.
In an earlier paper [Acta Mathematica, v. 176, 1996, 145-169, alg-geom/9505024 ] the present authors and Dennis Sullivan constructed the universal direct system of the classical Teichmüller spaces of Riemann surfaces of varying genus. The direct limit, which we called the universal commensurability Teichmüller space, $…
Tensor networks improve b-jet classification in high-energy physics.
problem Classifying jets from b-quarks in proton-proton collisions.
method Quantum-inspired machine learning using tensor networks.
result Optimized classification of b-jets with improved precision and speed.
A hardware-based reservoir computing system predicts time series with high speed and accuracy.
problem Processing time-dependent signals with high speed and accuracy.
method A hardware-based reservoir computing system using a field-programmable gate array (FPGA) for both the reservoir and output layers.
result Achieves comparable accuracy to software approaches but with a superior real-time prediction rate up to 160 MHz.
Study shows variability in CNN predictions for medical images, suggesting ensemble averaging to reduce it.
problem Variability in CNN predictions for medical images due to data ordering during training.
method Reproduced CheXNet results with random seeds to identify variability in predictions.
result Averaging predictions from multiple models reduces variability by nearly 70%.
Researchers propose a new SSL risk decomposition method to evaluate and improve self-supervised learning models.
problem Self-supervised learning evaluation is limited to a single metric, providing little insight into model performance and improvement.
method Proposes an SSL risk decomposition that considers four error components: approximation, representation usability, probe generalization, and encoder generalization.
result Analysis of 169 SSL vision models reveals the main sources of error and provides insights for improving SSL models in specific settings.
Quantum Signal Processing reduces derivative pricing quantum resource requirements.
problem Efficiently pricing financial derivatives on quantum computers.
method Quantum Signal Processing (QSP) to encode payoffs directly into quantum amplitudes.
result Significantly reduces quantum resources (T-gates and qubits) for practical derivative contracts.
BagNet classifies images using local features, making decisions clearer and more understandable.
problem Understanding how deep neural networks make decisions is difficult.
method BagNet is a variant of ResNet-50 that classifies images based on local features without spatial ordering.
result BagNet achieves high accuracy on ImageNet (87.6% top-5 for 33 x 33 px features).
New solutions to SU(n+1) Toda system found on compact Riemann surfaces with cone singularities.
problem Solving SU(n+1) Toda system with cone singularities on compact Riemann surfaces.
method Character n-ensembles and toric curves on compact Riemann surfaces.
result Established a correspondence between character n-ensembles and toric solutions to SU(n+1) system with cone singularities.
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
problem Analyzing algorithmically-rejected trading events for insights into market dynamics.
method Public dataset of 6,660 algorithmically-rejected trading events, linked to post-rejection price and liquidity trajectories.
result First window of a planned series of datasets extending the time horizon and enabling regime-stratified analysis.
The paper classifies solutions to a specific Toda system around a singular source.
problem Characterizing solutions to a particular Toda system around a singular point.
method Utilizing an ordinary differential equation and properties of SU(n+1) Toda system, the paper precisely characterizes solutions.
result Characterization of solutions to the SU(n+1) Toda system around a singular source.
ASKAP observes a region of the Galactic plane, identifying 3963 radio sources.
problem Characterizing radio sources in the Galactic plane using ASKAP observations.
method 912 MHz observations with 15 ASKAP antennas, source characterization using CAESAR.
result Differential source counts in agreement with previous surveys, spectral index estimation for sources.
This study evaluates common trading models and finds moving averages crossovers outperforming others.
problem Evaluating the effectiveness of common technical trading models.
method Created original versions of popular models and tested their performance on popular stocks and indexes.
result Moving averages crossovers outperformed other models, but machine learning models did not.
CAESAR source finder improves automated source extraction for ASKAP surveys.
problem Automated source extraction challenges in ASKAP surveys.
method Extended CAESAR source finder for compact and extended sources.
result Improved algorithm performances and scalability for future ASKAP surveys.
IVUS-Net automatically segments IVUS images for quicker diagnosis of cardiovascular diseases.
problem Automatically delineating lumen and media-adventitia borders in IVUS images.
method Proposes IVUS-Net, a fully convolutional network followed by post-processing.
result IVUS-Net outperforms state-of-the-art methods by 4% to 20% in HD distance.
Physics-informed neural network identifies and characterizes surface cracks in metals.
problem Identifying and characterizing surface-breaking cracks in metals using ultrasound.
method Physics-informed neural network (PINN) trained with ultrasonic surface wave data and adaptive activation functions.
result PINN accurately estimates the speed of sound and identifies crack locations in metals.
Massive MIMO is a variant of multiuser MIMO where the number of base-station antennas M is very large (typically 100), and generally much larger than the number of spatially multiplexed data streams (typically 10). Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal band…
Graph neural network optimizes energy-efficient precoding for massive MIMO systems.
problem Energy bottleneck in massive MIMO systems due to high DAC complexity and power consumption.
method Proposes a graph neural network to directly output precoded quantized vectors from channel matrix and transmit symbols.
result Significant increase in achievable sum rate with reduced DAC power consumption.
Machine learning improves subseasonal forecasts for Western U.S. climate.
problem Improving long-term forecasts for drought and wet weather extremes in the Western U.S.
method Ensemble of two regression models using meteorological data and historical measurements.
result Ensemble skill exceeds top competitor's for temperature and precipitation forecasts.
Machine learning predicts one-year mortality with high accuracy.
problem Predicting one-year mortality for patients with life-limiting illnesses.
method Applied five machine learning techniques: SVM, KNN, Gradient Boosting, Random Forest, and MLP.
result Gradient Boosting Classifier achieved the highest AUC ROC of 0.911.
This study analyzes financial equity research reports to identify frequently asked questions and automates 80% of them.
problem Insufficient empirical analysis of questions answered in financial equity research reports.
method Analyzed 72 financial equity research reports, classifying sentences into 169 unique question archetypes. Used public corporate reports to classify questions' potential for automation.
result Approximately 80% of financial equity research reports can be automated, with 78.7% of questions automatable.