arXiv research

A new pricing controller handles resource constraints to infer target prices effectively.

HTFM improves mode coverage and tail-statistic recovery for heavy-tailed data.

Clock theorem extended to knotoids and linkoids.

This paper is about the clock number of a knot. First we define the clock number by using states of a knot defined by Kauffman. Next we show that if K is a prime knot, its clock number is greater than or equal to its crossing number. Finally we prove that its clock number is equal to its crossing number if and only if …

We give an algorithmic computation for the height of Kauffman's clock lattice obtained from a knot diagram with two adjacent regions starred and without crossing information specified. We show that this lattice is more familiarly the graph of perfect matchings of a bipartite graph obtained from the knot diagram by over…

Generalizes Kauffman's clock theorem to surfaces.

Paper introduces clock moves for plane graphs and proves Alexander polynomial properties.

State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vec…

Improved stochastic clocks for financial models without increasing trades.

Detect spacetime curvature without rulers and clocks in 3D.

Study efficient pricing for barrier options in stochastic-volatility models with leverage correction.

The paper characterizes discrete Morse functions on knot diagrams and generalizes a clock theorem.

We formulate simple assumptions, implying the Robbins-Monro conditions for the $Q$-learning algorithm with the local learning rate, depending on the number of visits of a particular state-action pair (local clock) and the number of iteration (global clock). It is assumed that the Markov decision process is communicatin…

The Kelly rule fails to maximize growth in a time-changed return setting.

Algorithm estimates clock in network cascades to improve performance.

We introduce a linear space of finitely additive measures to treat the problem of optimal expected utility from consumption under a stochastic clock and an unbounded random endowment process. In this way we establish existence and uniqueness for a large class of utility maximization problems including the classical one…

We introduce a linear space of finitely additive measures to treat the problem of optimal expected utility from consumption under a stochastic clock and an unbounded random endowment process. In this way we establish existence and uniqueness for a large class of utility-maximization problems including the classical one…

Paper analyzes systematic jump risk around the clock using news narratives.

New link polynomials linked to cluster theory.

A new uncertainty principle helps traders better understand market activity.

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possi…

Lower bounds on Bayes risk for realizable models derived using information theory.

New CTBNs with clocks allow for non-exponential survival times.

Optimizer memory affects learning rate sensitivity in shuffle order, impacting fine-tuning noise.

This study compares parallel SMC and MCMC for Bayesian deep learning, showing SMC parallel is faster.

Generative models learn rules at different timescales, revealing a 'innovation window'.

Study examines asymmetry impacts on Japanese stock market volatility modeling and forecasting.

Classifies connections on Galilei manifolds, generalizing known results.

We prove that the variance swap rate (fair strike) equals the price of a co-terminal European-style contract when the underlying is an exponential Markov process, time-changed by an arbitrary continuous stochastic clock, which has arbitrary correlation with the driving Markov process, provided that the payoff function …

Can we distinguish between two wireless transmitters sending exactly the same message, using the same protocol? The opportunity for doing so arises due to subtle nonlinear variations across transmitters, even those made by the same manufacturer. Since these effects are difficult to model explicitly, we investigate lear…

Derives variance kernel for reaction boundary in financial models.

Derives operational-time variance kernel for reaction boundaries in financial markets.

A symmetry-guided definition of time may enhance and simplify the analysis of historical series with recurrent patterns and seasonalities. By enforcing simple-scaling and stationarity of the distributions of returns, we identify a successful protocol of time definition in Finance. The essential structure of the stochas…

This paper builds a model of high-frequency equity returns by separately modeling the dynamics of trade-time returns and trade arrivals. Our main contributions are threefold. First, we characterize the distributional behavior of high-frequency asset returns both in ordinary clock time and in trade time. We show that wh…

This study improves tail risk forecasting by integrating overnight information into semi-parametric models.

Time dilation $\frac{1}{\sqrt{1-v^2}}$ and relative velocity $v$ are observationally indistinguishable in the special theory of relativity, a duality that carries over into the general theory under Fermi coordinates along a curve (in coordinate-independent language, in the tangent Minkowski space along the curve). For …

We investigate triangulations of the two-dimensional sphere and torus with the faces properly colored white and black. We focus on matchings between white triangles and incident vertices. On the torus our objects are perfect pairings, whereas on the sphere this is only true after removing one triangle and its vertices.…

Study on estimating Gaussian mean with missing data in high dimensions.

Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e.g. Bayesian neural networks. Algorithms that implement adaptive ICA converge slower than their nonadaptive counterparts, however, they are cap…

Quarter-hour market bursts predict algorithmic trading and returns in crypto futures.

Efficiently marginalizes over Gaussian Process kernels for better model flexibility and uncertainty.

Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical distributed approaches, synchronous or asynchronous, are based on the parameter server architecture, i.e., worker nodes compute gradients which…

Improves meta-learning efficiency with mixed-mode differentiation.

Bayesian neural networks improve stellar age predictions with reduced uncertainty.

Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome's plausibility. Information measures based on Shannon's concept of entropy include realization information, Kullback-Leibler divergenc…

A new approach to protein language models combines latent space prediction with masked language modeling.

Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned optimizers may similarly outperform current hand-designed optimizers, especially for specific problems. However, learned optimizers are notoriously difficult to…

Study forecasts volatility and risk in electricity markets using matrix-HAR models.