A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
R2-B2 optimizes game interactions with recursive reasoning.
New framework converts multi-objective to single-objective optimisation.
Innovative warping labeling for twisted knots and braids.
A new estimator improves training of probabilistic models with latent Gaussian variables.
In [R2] and [RO] the Arnold conjecture for closed symplectic manifolds with trivial second homotopy group was proved. This proof used surgery and cobordism theory. Here we give a purely cohomological proof of this result.
In this study, we introduce Euler-Lagrange and Hamiltonian equations on (R2; g; J) being a model of para-Kaehlerian Space Forms. Finally, some geometrical and physical results on the related mechanic systems have been discussed.
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
We propose a class of intrinsic Gaussian processes (in-GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irregular shaped spaces arising as subsets or submanifolds of R, R2, R3 and beyond. For example, in-GPs can accommodate spatial domains arising…
Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels. In this p…
Study examines how body segments respond to random vibrations.
Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa, producing highly informative training data on the fly. However, the self-play t…
Deep learning framework predicts surface texture parameters and their uncertainties.
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is c…
ANN model predicts zinc leaching filter cake moisture accurately.
The paper establishes a formula for enumeration of curves of arbitrary genus in toric surfaces. It turns out that such curves can be counted by means of certain lattice paths in the Newton polygon. The formula was announced earlier in http://arxiv.org/abs/math.AG/0209253. The result is established with the help of the …
A new boosting method reduces overfitting and negative transfer in transfer learning.
For quantitative structure-property relationship (QSPR) studies in chemoinformatics, it is important to get interpretable relationship between chemical properties and chemical features. However, the predictive power and interpretability of QSPR models are usually two different objectives that are difficult to achieve s…
Particulate matter pollution is one of the deadliest types of air pollution worldwide due to its significant impacts on the global environment and human health. Particulate Matter (PM2.5) is one of the important particulate pollutants to measure the Air Quality Index (AQI). The conventional instruments used by the air …
Study confirms link between unemployment and real GDP growth in developed countries.
Droughts, with their increasing frequency of occurrence, continue to negatively affect livelihoods and elements at risk. For example, the 2011 in drought in east Africa has caused massive losses document to have cost the Kenyan economy over $12bn. With the foregoing, the demand for ex-ante drought monitoring systems is…
Generative model downgrades coarse satellite images to fine resolution.
We show experimentally that the accuracy of a trained neural network can be predicted surprisingly well by looking only at its weights, without evaluating it on input data. We motivate this task and introduce a formal setting for it. Even when using simple statistics of the weights, the predictors are able to rank neur…
Tornadoes are the most violent of all atmospheric storms. In a typical year, the United States experiences hundreds of tornadoes with associated damages on the order of one billion dollars. Community preparation and resilience would benefit from accurate predictions of these economic losses, particularly as populations…
Bluetooth data predicts depression severity, showing 18.8% extra variance.
Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diag…
Fetal brain imaging is a cornerstone of prenatal screening and early diagnosis of congenital anomalies. Knowledge of fetal gestational age is the key to the accurate assessment of brain development. This study develops an attention-based deep learning model to predict gestational age of the fetal brain. The proposed mo…
Network analysis improves stock return forecasting.
The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.
The paper predicts U.S. nonfarm employment using machine learning.
The results of data mining endeavors are majorly driven by data quality. Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness. This paper is based on the prem…
This note has an experimental nature and contains no new theorems. We introduce certain moves for classical knot diagrams that for all the very many examples we have tested them on give a monotonic complete simplification. A complete simplification of a knot diagram D is a sequence of moves that transform D into a diag…
Model financial dynamics using 2-manifold geometries, revealing the torus as best for cyclical data.
Hybrid model forecasts Bitcoin prices better than standard LSTM.
Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the det…
The evolution of inflation, p(t), and unemployment, UE(t), in Japan has been modeled. Both variables were represented as linear functions of the change rate of labor force, dLF/LF. These models provide an accurate description of disinflation in the 1990s and a deflationary period in the 2000s. In Japan, there exists a …
Machine learning models predict the behavior of negatively buoyant jets from wastewater.
Quantum kernel improves solar irradiance forecasting.
Study uses machine learning to optimize seismic design parameters.
The paper improves model-based reinforcement learning by using multi-timestep objectives.
Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear…
In-BO optimizes complex constrained domains using SIn-GP surrogate models.
Paper proposes a neural network method for fast, interpretable AR model estimation.
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinat…
Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear, global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However, so far for the references we know, few of which propos…
Study identifies personality traits from dance movements in music.
Market valuation duration is 175 years, but drops to 46 years during crises.
Hybrid model outperforms benchmarks in financial forecasting.
Machine learning models perform better with location coordinates alone, not Moran Eigenvectors.