Deep learning improves salt deposits segmentation in seismic data.
problem Segmenting salt deposits in seismic reflection data for hydrocarbon exploration.
method A novel deep learning approach combining U-Net with ResNeXt-50 encoder, Spatial-Channel Squeeze & Excitation, Lovasz loss, CoordConv, and Hypercolumn methods.
result Achieved 27th place in Kaggle competition for salt deposits segmentation.
CNN improves salt body interpretation in seismic imaging.
problem Manual salt body interpretation is time-consuming and prone to bias.
method U-Net and ResNet with ELU activation and Lovász-Softmax loss.
result CNN predictions match manual interpretations well, especially in weak reflection areas.
The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters.…
SALT models combine ARHMM and SLDS for efficient, interpretable time-series analysis.
problem Efficient modeling of systems with time-varying dynamics and long-range dependencies.
method Switching autoregressive low-rank tensor models parameterized with a low-rank factorization.
result SALT models provide a balance of interpretability and efficiency, outperforming ARHMMs and SLDSs.
Neural network enhances seismic imaging in salt-prone areas.
problem Improving velocity model building for faster FWI convergence.
method 3D convolutional, de-convolutional, and max-pooling neural network architecture with data augmentations and regularization.
result Proposed neural network generates salt body probability cubes for FWI regularization.
Bank deposits are analyzed as having dual characteristics, akin to quantum physics.
problem Understanding the dual nature of bank deposits.
method Application of quantum physics concepts to bank deposits.
result Bank deposits have a hybrid nature, combining debt and equity features.
Analysis finds no evidence of banks managing deposit run risk prior to 2023 Regional Banking Crisis.
problem Determining factors for deposit run risk management before a regional banking crisis.
method Cross-sectional analysis of interest rate and equity use by banks.
result No evidence of banks managing deposit run risk via their balance sheet.
The definition of deposit substitutes in Philippine tax law fails to consider the maturity of a debt instrument. This makes it possible for long-term bonds to be considered as deposit substitutes if they meet the 20-lender rule, taxable at 20% final tax. However, long-term debt instruments cannot realistically function…
Paper models non-maturing deposits using a Lévy-driven Ornstein-Uhlenbeck process.
problem Managing non-maturing deposits as a major funding source for banks.
method Develops a multivariate Lévy-driven Ornstein-Uhlenbeck process with three sources of randomness.
result Models rare but severe events in deposit volumes with positive probability.
SALT combines geometric and model-based alignment for domain adaptation.
problem Aligning source and target domains for unsupervised domain adaptation.
method SALT treats alignment as an auxiliary task, leveraging subspace geometry and gradient-based optimization.
result SALT achieves comparable or better performance than state-of-the-art methods.
Machine learning predicts rock properties from routine core analysis.
problem Predict rock properties like porosity and permeability from routine core analysis.
method Developed and compared machine learning models (NN, SVM, LR).
result Neural network with hidden layers best for all rock properties.
Dual ML approach predicts peak temperatures in AFSD, improving process optimization.
problem Lack of understanding between process parameters and resulting microstructure in AFSD.
method Combines supervised machine learning and physics-informed neural networks.
result Ensemble techniques like gradient boosting outperform other SML methods in predicting peak temperatures.
Commercial banks and other depository institutions in some countries are required to hold in reserve against deposits made by their customers at their Central Bank or Federal Reserve. Although some countries have been eliminated it, this requirement is useful as one of many Central Bank's regulation made to control rat…
This paper examines SVB's failure and its impact on bank stocks.
problem SVB failure and its contagion effects on bank stocks.
method Analyzed bank-specific vulnerabilities and stock performance.
result Uninsured deposits and unrealized losses were key factors in SVB's impact.
Investigates the impact of narrow banking on macroeconomics.
problem The risks and benefits of a full reserve requirement on demand deposits.
method Extended Goodwin-Keen model with time deposits and central bank reserves; numerical examples.
result Narrow banking does not reduce economic growth but improves financial stability.
A computer vision approach improves neutral particle detection in particle flow algorithms.
problem Optimal reconstruction of particle content and kinematics in calorimeter images.
method Computer vision techniques applied to calorimeter images, using deep learning and super-resolution.
result Significantly improved reconstruction of neutral particle calorimeter energy deposits.
We propose a simple model of the banking system incorporating a game feature where the evolution of monetary reserve is modeled as a system of coupled Feller diffusions. The Markov Nash equilibrium generated through minimizing the linear quadratic cost subject to Cox-Ingersoll-Ross type processes creates liquidity and …
Study on Spanish households' investment choices in housing, deposits, and stocks.
problem Investment decisions of Spanish households in housing, deposits, and stocks.
method Theoretical model considering indivisible and illiquid housing assets, financial constraints, and actual choices compared.
result Households underinvest in stocks and deposits compared to optimal choices, but mortgage investments are efficient.
A new greedy method tackles ℓ0,∞ sparse coding for better image processing.
problem Imbalanced sparsity in ℓ0 and ℓ1 norms for image processing. method Greedy matching pursuit for ℓ0,∞ norm optimization. result Efficient method for ℓ0,∞ sparse coding and dictionary learning. A novel RNN uses stigmergy for temporal input encoding.
problem Temporal input encoding in RNNs.
method Stigmergy-based computational memory in RNN design.
result Stigmergy enables coherent temporal input encoding.
Framework optimizes expensive manufacturing processes efficiently.
problem Optimizing input parameters for advanced manufacturing methods.
method Bayesian optimization with tailored acquisition function and parallel acquisition.
result Framework efficiently finds optimal parameters with minimal process cost.
Paper examines constraints on cryptocurrency networks to improve liquidity and capital costs.
problem Improving liquidity in cryptocurrency networks with limited capital deposits.
method Introduces constraints to bound loss in default scenarios and simplifies network structure.
result Achieves optimal tradeoff between liquidity and capital costs in payment networks.
We present an extended version of the recently proposed "LLOB" model for the dynamics of latent liquidity in financial markets. By allowing for finite cancellation and deposition rates within a continuous reaction-diffusion setup, we account for finite memory effects on the dynamics of the latent order book. We compute…
We discuss the portfolio optimization problem with the obligatory deposits constraint. Recently it has been shown that as a consequence of this nonlinear constraint, the solution consists of an exponentially large number of optimal portfolios, completely different from each other, and extremely sensitive to any changes…
Machine learning detects new minerals at Mars rover landing sites.
problem Detecting rare minerals at Mars rover landing sites.
method Hierarchical Bayesian classifier trained on CRISM spectral data.
result Akaganeite, silica, and jarosite found in Jezero crater floor and NE Syrtis.
Study analyzes profitability and efficiency of Chinese banks, finding state-owned banks superior.
problem Analyzing efficiency and profitability of Chinese banks over time.
method Used Data envelopment analysis (Super-SBM-UND-VRS based DEA) model considering non-performing loans as undesired output.
result State-owned banks and Rural/City Commercial Banks have better profitability super-efficiency than Joint-stock Banks.
BYOV combines SSL and Bayesian methods for uncertainty estimation.
problem Model uncertainty in applications.
method Combines Bootstrap Your Own Latent (BYOL) and Bayes by Backprop (BBB).
result BYOV improves model calibration and reliability with various augmentations.
Once upon a time there was a classical financial world in which all the Libors were equal. Standard textbooks taught that simple relations held, such that, for example, a 6 months Libor Deposit was replicable with a 3 months Libor Deposits plus a 3x6 months Forward Rate Agreement (FRA), and that Libor was a good proxy …
Paper proposes a technique to detect and predict sources of contaminants in complex systems.
problem Difficulty in identifying sources of contaminants in coupled natural and human systems.
method Developed a technique for simultaneous source detection and prediction.
result Outperforms other approaches in detecting potential groundwater contamination.
Optimizes chip component placement with self-alignment for SMT technology.
problem Achieving precise component placement on PCB during SMT process.
method Proposed machine learning algorithms (SVR and RFR) to predict component positions and developed non-linear optimization model.
result RFR model outperforms in predicting component positions before reflow.
An investor with constant relative risk aversion and an infinite planning horizon trades a risky and a safe asset with constant investment opportunities, in the presence of small transaction costs and a binding exogenous portfolio constraint. We explicitly derive the optimal trading policy, its welfare, and implied tra…
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.
Split-BN improves SSL by separating BN stats for unlabeled data.
problem SSL benefits from unlabeled data are often lost or even harmful when class mismatch exists.
method Split-BN uses separate BN statistics for unlabeled data.
result Split-BN improves SSL performance in shifted domain settings.
Paper solves a trading game for stocks that jump and diffuse, finding the Kelly rule is optimal.
problem Trading game for stocks that can jump and diffuse.
method Solves a zero-sum game with a payoff kernel, finding the Kelly rule for jump diffusions.
result The Kelly rule is the optimal strategy for outperforming traders in a trading game.
Algorithm improves blockchain bridge efficiency.
problem Efficient cross-chain wealth management.
method Dynamic algorithm to optimize bridge capacities.
result Optimized fund transfers across networks.
We develop an axiomatic theory of balance functions (future value functions) in the theory of interest that is derived from financial considerations and which applies to general regulated payment streams, including continuous payment streams. Balance functions exist and are unique up to an initial choice of deposit and…
vOED-NFs uses normalizing flows to improve Bayesian OED without likelihood evaluations.
problem Optimizing experiments to maximize information gain in model parameters.
method vOED-NFs combines variational approximations with normalizing flows for efficient EIG estimation.
result vOED-NFs achieves lower EIG estimation bias compared to previous methods.
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manip…
Deep learning detects icebergs and ships from SAR data.
problem Detecting icebergs and ships from SAR data for Arctic navigation safety.
method Transfer Learning with a CNN, augmented data, and multiple outputs.
result Significant accuracy boost (logarithmic score 0.1463) in iceberg and ship detection.
In this article we study the shape of a compact surface of constant mean curvature of Euclidean space whose boundary is contained in a round sphere. We consider the case that the boundary is prescribed or that the surface meets the sphere with a constant angle. We study under what geometric conditions the surface must …
Spectral analysis detects structural changes in financial networks.
problem Detecting structural transitions in financial networks to assess systemic risk.
method Ensemble properties of spectral radius of random graph models calibrated on real-world evolving networks.
result The spectral deviation captures ongoing topological changes in financial networks.
In the present work we introduce a novel multi-agent model with the aim to reproduce the dynamics of a double auction market at microscopic time scale through a faithful simulation of the matching mechanics in the limit order book. The agents follow a noise decision making process where their actions are related to a s…
This research finds three meta-indicators for university rankings.
problem Complexity in university ranking systems.
method Interpretable machine learning approach.
result Identified three meta-indicators: time, space, and relationships.
The paper calculates prices for multi-step barrier options under the Black-Scholes model.
problem Calculating prices for multi-step barrier options with varying barriers and time steps.
method Derives a general, explicit expression for option prices using the Black-Scholes model and a multi-step reflection principle.
result Derives a multi-step reflection principle that generalizes the reflection principle of Brownian motion.
The model of this paper gives a convenient strategy that a bank in the federal funds market can use in order to maximize its profit in a contemporaneous reserve requirement (CRR) regime. The reserve requirements are determined by the demand deposit process, modelled as a Brownian motion with drift. We propose a new mod…
We show that any objective risk measurement algorithm mandated by central banks for regulated financial entities will result in more risk being taken on by those financial entities than would otherwise be the case. Furthermore, the risks taken on by the regulated financial entities are far more systemically concentrate…
An exclusion particle model is considered as a highly simplified model of a limit order market. Its price behavior reproduces the well known crossover from over-diffusion (Hurst exponent H>1/2) to diffusion (H=1/2) when the time horizon is increased, provided that orders are allowed to be canceled. For early times a ma…
Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair …