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.
SPT predicts age and mass of red giants from spectra.
This study uses deep learning to infer stellar parameters from short TESS and K2 observations.
Geodesic X-ray transform proves injective for smooth one-forms on gas giant manifolds.
For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally finds an Approximate NewTon (ANT) direction, which is sent to the main driver. The main driver, then, …
Study the geometry of gas giant planets to infer their internal structure.
Study of red blood cells using elastic surface theory.
This paper presents the recurrent estimation of distributions (RED) for modeling real-valued data in a semiparametric fashion. RED models make two novel uses of recurrent neural networks (RNNs) for density estimation of general real-valued data. First, RNNs are used to transform input covariates into a latent space to …
ReD improves LLM inference efficiency at fixed budget, reducing attempts and cost.
Generative ML learns optimal pursuit trajectories in pursuit-evasion games.
RED CoMETS improves multivariate time series classification accuracy.
Mathematical model describes how red blood cells return to equilibrium.
If we pick random points uniformly in and connect each point to its nearest neighbors, then it is well known that there exists a giant connected component with high probability. We prove that in it suffices to connect every point to points chosen randomly among its $…
Quantization and reduction for coisotropic A-branes on Hamiltonian manifolds.
Compressing giant neural networks has gained much attention for their extensive applications on edge devices such as cellphones. During the compressing process, one of the most important procedures is to retrain the pre-trained models using the original training dataset. However, due to the consideration of security, p…
Proposes RPG-RT for red-teaming T2I models without internal access.
New framework assesses LLM security risks in BFSI.
In this paper, we introduce an insurance ruin model with adaptive premium rate, thereafter refered to as restructuring/refraction, in which classical ruin and bankruptcy are distinguished. In this model, the premium rate is increased as soon as the wealth process falls into the red zone and is brought back to its regul…
Graph neural networks denote a group of neural network models introduced for the representation learning tasks on graph data specifically. Graph neural networks have been demonstrated to be effective for capturing network structure information, and the learned representations can achieve the state-of-the-art performanc…
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
Scenario discovery is the process of finding areas of interest, known as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions, i.e., inputs of the simulation model, where the system is unstable. Subgroup discovery methods are commonly used for scenario discovery. They find…
After shortly recalling the construction of the Khovanov-Kuperberg algebras, we give a characterisation of indecomposable web-modules. It says that a web-module is indecomposable if and only if one can deduce it directly from the Kuperberg bracket (via a Schur lemma argument). The proofs relies on the construction of i…
We introduce \textcolor{red}{general} new techniques for computing the geometric index of a link in the interior of a solid torus . These techniques simplify and unify previous ad hoc methods used to compute the geometric index in specific examples \textcolor{red}{ and allow the simple computation of geometric i…
RED detects sleep EEG events using deep neural networks, outperforming previous methods.
We find a Sasaki-Einstein metric from a CFT state in AdS5.
We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(m*s+m*log(m)) time complexity, where m is the number of training examples, and s the average number of non-zer…
Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared …
Gradient descent with error feedback performs better than vanilla when features are rare.
Analyzes geodesic lengths in sparse networks, deriving a distribution.
New AI approach improves quantum device calibration by leveraging prior scientific discoveries.
In this paper, we obtain analytical expression for the distribution of the occupation time in the red (below level ) up to an (independent) exponential horizon for spectrally negative Lévy risk processes and refracted spectrally negative Lévy risk processes. This result improves the existing literature in which only…
Subspace clustering aims to cluster unlabeled data that lies in a union of low-dimensional linear subspaces. Deep subspace clustering approaches based on auto-encoders have become very popular to solve subspace clustering problems. However, the training of current deep methods converges slowly, which is much less effic…
Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for and development of label-free methodologies to classify cells is strong…
GShard enables scaling of large neural networks with automatic sharding and lightweight APIs.
We extend the empirical results published in article "Empirical Evidence on Arbitrage by Changing the Stock Exchange" by means of machine learning and advanced econometric methodologies based on Smooth Transition Regression models and Artificial Neural Networks.
New method predicts quasar continuum near Lyman-α with high precision and accuracy.
Paper develops an attention mechanism for long-term scientific impact prediction.
CQL (ReDS) learns from varied driving behaviors, improving offline RL performance.
The paper uses a simulator and optimisation to defend against cyber threats.
This paper studies parabolic quasi-Coxeter elements in complex reflection groups and their combinatorial properties.
AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy c…
A new watermarking method corrects bias in language models using maximal coupling.
Humans reason with concepts and metaconcepts: we recognize red and green from visual input; we also understand that they describe the same property of objects (i.e., the color). In this paper, we propose the visual concept-metaconcept learner (VCML) for joint learning of concepts and metaconcepts from images and associ…
Let (M,ω) be a symplectic manifold, and (Σ,σ) a closed connected symplectic 2-manifold. We construct a weakly symplectic form {ω^{D}}_{(Σ, σ)} on the space of immersions Σ\to M that is a special case of Donaldson's form. We show that the restriction of {ω^{D}}_{(Σ,σ)} to any orbit of the group of Hamiltonian symplectom…
Anti-ELAB protests affected Hong Kong firms' stock prices, especially those linked to pan-democrats.
New methods correct spectral distortions using known analyte concentrations.
We propose to describe the variety of galaxies from SDSS by using only one affine parameter. To this aim, we build the Principal Curve (P-curve) passing through the spine of the data point cloud, considering the eigenspace derived from Principal Component Analysis of morphological, physical and photometric galaxy prope…
Results of the application of pattern recognition techniques to the problem of identifying Giant Radio Sources (GRS) from the data in the NVSS catalog are presented and issues affecting the process are explored. Decision-tree pattern recognition software was applied to training set source pairs developed from known NVS…