This paper introduces DPI, a new metric to assess data-copying risk in tabular data.
problem Measuring privacy risk of data-copying in tabular generative models.
method Proposes Data Plagiarism Index (DPI) for evaluating data-copying risk.
result DPI identifies data-copying threats to tabular data models, highlighting privacy and fairness issues.
Visual spoofing bypasses spam filters and plagiarism detection.
problem Vulnerability in spam filters that can be exploited by visually similar but differently encoded characters.
method Replaces characters with visually similar but differently encoded characters from a different alphabet.
result Spammers can create messages that bypass existing spam filters.
This article was withdrawn by the arXiv.org administrators since it plagiarizes math.AT/0401211.
This article was withdrawn by the arXiv.org administrators since it plagiarizes math.GT/0011056.
funcGNN uses graph neural networks to estimate program similarity efficiently.
problem Estimating accurate program similarity for software engineering tasks.
method funcGNN trains on labeled CFG pairs to predict GED between unseen programs using effective embedding vectors.
result funcGNN achieves lower error rate (0.00194) and is 23 times faster than traditional methods.
How can we measure whether a natural language generation system produces both high quality and diverse outputs? Human evaluation captures quality but not diversity, as it does not catch models that simply plagiarize from the training set. On the other hand, statistical evaluation (i.e., perplexity) captures diversity b…
We introduce a Maximum Entropy model able to capture the statistics of melodies in music. The model can be used to generate new melodies that emulate the style of the musical corpus which was used to train it. Instead of using the n−body interactions of (n−1)−order Markov models, traditionally used in automatic mus…
Machine learning conferences face ethical issues in review process.
problem Ethical issues in the review process of machine learning conferences.
method Study of recruitment issues, double-blind process infringements, fraudulent behaviors, biases, and appendix phenomenon.
result Highlighting the need for awareness in the machine learning community.
A new stock index model simplifies high-dimensional stock data.
problem Reflecting the overall stock market activity in high-dimensional data.
method Manifold learning and feature detection on discrete Laplace-Beltrami operator.
result The MF index series approximates the stock market better and has lower risk.
A new clustering evaluation index based on density estimation.
problem Improving internal clustering evaluation indices.
method The index is a mixture of Ambiguous and Similarity sub-indices, calculated using density estimation.
result The new index significantly outperforms other internal clustering evaluation indices.
Tree Index evaluates cluster quality by creating decision trees from data.
problem Evaluating the quality of cluster results from various techniques.
method Tree Index creates a decision tree from clustered data, combining entropy and depth of leaves.
result Tree Index discriminates between sensible and non-sensible clusters on brain dataset.
We propose an approach to explain fluctuations in time intervals of financial markets data from the view point of the Gini index. We show the explicit form of the Gini index for a Weibull distribution which is a good candidate to describe the first passage time of foreign exchange rate. The analytical expression of the…
Index structures are important for efficient data access, which have been widely used to improve the performance in many in-memory systems. Due to high in-memory overheads, traditional index structures become difficult to process the explosive growth of data, let alone providing low latency and high throughput performa…
Improved stock index analysis using fuzzy parameters and machine learning.
problem Analyzing the S&P 500 stock index with long-term dependence.
method Combining fuzzy theory and machine learning to modify the Barndorff-Nielsen and Shephard model.
result The new model effectively captures the stochastic dynamics of the stock index time series.
DFR models dynamic distributional data with weighted Fréchet means.
problem Regression of distribution-valued responses over time.
method Dynamic Fréchet Regression (DFR) with index-aware weighting and feature selection.
result Improved predictive accuracy and feature recovery over existing methods.
Multifractal analysis and extensive statistical tests are performed upon intraday minutely data within individual trading days for four stock market indexes (including HSI, SZSC, S&P500, and NASDAQ) to check whether the indexes (instead of the returns) possess multifractality. We find that the mass exponent τ(q) is l…
Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
problem Robust stock index forecasting
method Modified Transformer architecture with Shifted Data Augmentation
result Best performance on benchmark datasets
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs). Computations using sparse matrices obtained by pruning parameters, however, exhibit vastly different parallelism depending on the index representation scheme. As a result, fine-grained pruning ha…
A new method tracks index using topological data analysis for sparse portfolios.
problem Sparse index tracking with robust risk management.
method Topological learning via Vietoris-Rips filtration for sparse regularization.
result The method outperforms state-of-the-art techniques in various market conditions.
FunBaT extends Tucker decomposition to handle continuous-indexed tensor data.
problem Handling continuous-indexed tensor data that doesn't fit traditional Tucker decomposition.
method FunBaT treats continuous-indexed data as interactions between a core tensor and a group of latent functions modeled by Gaussian processes (GP). It converts each GP into a state-space prior and uses advanced message-passing techniques for scalable inference.
result FunBaT effectively handles real-world data with continuous indexes, demonstrating its advantage in synthetic and real-world applications.
We present a new model for credit index derivatives, in the top-down approach. This model has a dynamic loss intensity process with volatility and jumps and can include counterparty risk. It handles CDS, CDO tranches, Nth-to-default and index swaptions. Using properties of affine models, we derive closed formulas for t…
Study XRP network, propose Flow Index to analyze transaction frequencies.
problem Analyze transaction frequencies in XRP network.
method Analyze XRP transaction history, propose Flow Index.
result Flow Index reveals bow-tie/walnut structure in XRP network.
Quantum SVM improves financial data classification.
problem Classifying financial data using quantum machine learning.
method Application of quantum kernels to financial data, specifically DSEx Broad Index.
result Empirical quantum advantage demonstrated for financial data classification.
New method accurately reconstructs Russell 3000 index, revealing crowded portfolios.
problem Crowding in index portfolios during reconstitution events.
method Developed a Python package for accurate index reconstruction using CRSP US Stock data.
result Annual Russell 3000 portfolios are more crowded than quarterly ones, suggesting lower transaction costs.
This paper reviews and analyzes various modeling approaches for financial index tracking.
problem Efficient replication of market index performance in financial markets.
method Categorization into three frameworks: optimization, statistical, and machine learning; empirical study on S&P 500 dataset.
result Optimization-based models deliver the most precise index tracking, statistical-based models achieve the strongest return-risk balance, and data-driven models provide competitive performance.
Adaptive framework improves NB accuracy by fusing two index categories.
problem Challenges in attribute weighted NB, especially fusion of two indexes.
method Proposes ATFNB framework using switching factor to fuse two index categories.
result ATFNB outperforms basic NB and state-of-the-art models.
The study examines the index of MOTS in Kerr-Newman-de Sitter spacetime and its relation to mass and charge.
problem Investigating the index of MOTS in Kerr-Newman-de Sitter spacetime.
method Analyzing the spatial cross section of the cosmological horizon in the Kerr-Newman-de Sitter spacetime, proving index bounds and establishing area-charge estimates.
result Established bounds on the index of MOTS and a connection between MOTS with index one and General Relativity.
Paper introduces a new robust method for estimating Pareto tail index from grouped data.
problem Limited robust methods for estimating Pareto tail index from grouped data.
method Method of Truncated Moments (MTuM)
result Inferential justification and validation of MTuM through simulation study.
Investment strategies involving cryptocurrencies and VIX INDEX show positive impact in market performance.
problem Investment strategies involving cryptocurrencies and VIX INDEX.
method Parameter estimation on raw data, comparison of two different portfolios, and analysis of different market conditions.
result VIX INDEX positively impacts the investment portfolio of cryptocurrencies in both standard and downward markets.
Obesity is an important concern in public health, and Body Mass Index is one of the useful (and proliferant) measures. We use Convolutional Neural Networks to determine Body Mass Index from photographs in a study with 161 participants. Low data, a common problem in medicine, is addressed by reducing the information in …
Refined 3D index uses surgery and gradings to distinguish 3-manifolds.
problem Distinguishing 3-manifolds and gauge theories phases.
method Dehn surgery presentation, ideal triangulation, and enhanced flavor symmetries.
result Invariance of refined index under various transformations.
This paper is trying to unveil general statistical characteristic of financial; time series data that is subjected to several financial time series data present in Indonesia, e.g. individual index such as stock price of PT. TELKOM, stock price of PT HM SAMPOERNA, and compiled stock price index (Jakarta Stock Exchange I…
Deep learning predicts market sensitivities for cost-effective index tracking.
problem Costly and impractical replication of index funds.
method Learning to predict market sensitivities using deep learning models.
result Significant reduction in prediction errors compared to historical methods.
A blockchain protocol uses bandit algorithms to dynamically price transactions.
problem Maximizing revenue from decentralized blockchain Indexers competing for queries.
method Dynamic pricing using Gaussian bandits for multiple agents.
result Improved revenue through dynamic pricing in a decentralized blockchain environment.
Develops a new cluster validity index to find multiple optimal cluster numbers.
problem Finding the optimal number of clusters in real-world data with varying densities, sizes, and shapes.
method A new correlation-based cluster validity index that yields multiple local peaks.
result The new index finds multiple optimal cluster numbers in various scenarios.
We study the Cauchy data spaces of the strongly Callias-type operators using maximal domain on manifolds with non-compact boundary, with the aim of understanding the Atiyah-Patodi-Singer index and elliptic boundary value problems.
New validity index for fuzzy-possibilistic c-means clustering.
problem Conflicting results in determining the optimal number of clusters due to noisy data points and outliers.
method Introducing a new validity index (FP index) for fuzzy-possibilistic c-means clustering.
result FP index works well in datasets with varying cluster shapes and densities.
Bank transactions help predict macroeconomic indexes faster and more accurately.
problem Lag in macroeconomic index availability and autoregressive models' limitations in complex scenarios.
method Use financial transactions data to estimate macroeconomic indexes using neural networks and smart sampling.
result Neural network approach outperforms baseline methods on hand-crafted features based on transactions.
Training a neural network for a classification task typically assumes that the data to train are given from the beginning. However, in the real world, additional data accumulate gradually and the model requires additional training without accessing the old training data. This usually leads to the catastrophic forgettin…
Study creates a global living index to assess quality of life.
problem Long-term impacts of global economic changes on living conditions.
method Machine learning framework combining socio-economic factors.
result Developed a practical tool for policymakers to identify areas needing improvement.
Deep single-index Fréchet regression for metric space-valued outputs
problem Predicting outputs in non-Euclidean spaces
method DeSI (Deep Single-Index Fréchet Regression)
result Interpretable index direction for inputs
Extensions to given-data Sobol' index estimators for large models.
problem Efficiently compute Sobol' indices for models with many inputs.
method General definition, streaming algorithm, heuristic filtering.
result Comparable accuracy and lower memory usage for large models.
Investment horizon approach has been used to analyze indexes of Polish stock market.Optimal time horizon for each return value is evaluated by fitting appropriate function form of the distribution. Strong asymmetry of gain-loss curves is observed for WIG index, whereas gain and loss curves look similar for WIG20 and fo…
This paper introduces a nonparametric copula-based index for detecting the strength and monotonicity structure of linear and nonlinear statistical dependence between pairs of random variables or stochastic signals. Our index, termed Copula Index for Detecting Dependence and Monotonicity (CIM), satisfies several desirab…
Improved FDR control for sparse financial index tracking.
problem Maintaining FDR control in high-dimensional financial data with strong variable dependencies.
method Expanding T-Rex framework to handle overlapping groups of correlated variables with nearest neighbors penalization.
result Accurately tracks the S&P 500 index using only a small number of stocks.
A generalization of Callias' index theorem for self adjoint Dirac operators with skew adjoint potentials on asymptotically conic manifolds is presented in which the potential term may have constant rank nullspace at infinity. The index obtained depends on the choice of a family of Fredholm extensions, though as in the …
Paper models demand and solvency for index insurance, combining traditional and measurable index-based coverage.
problem Reducing protection gaps for emerging risks.
method Develops a model for demand and solvency conditions, combining traditional and index-based insurance.
result Deduces a product that benefits from both traditional and index-based insurance approaches.
SGD shows distinct phases in learning single-index models, achieving optimal sample complexity and regret.
problem Learning single-index models with SGD in adaptive data settings.
method Stochastic gradient descent (SGD) with an optimal learning rate schedule.
result SGD achieves near-optimal sample complexity and regret guarantees across both burn-in and learning phases.