Study dynamic assortment and positioning of products with varying display effects.
problem Dynamic assortment and positioning of products with varying display effects.
method Design round-based learning algorithms for both multiplicative and general position effects models, and develop efficient subroutines for optimization.
result First regret-optimal characterization for both models, with matching upper and lower bounds.
Proves effective linear volume growth for 3-manifolds with positive scalar curvature.
problem Volume growth of three-manifolds with positive scalar curvature.
method Utilizes the technique of μ-bubbles and almost-splitting theorem.
result Proves effective linear volume growth for 3-manifolds with non-negative Ricci curvature and uniformly positive scalar curvature.
Study on m-positivity in Kähler manifolds with new Monge-Ampère-type equation.
problem Exploring m-positivity in Kähler manifolds and its geometric applications. method Generalizing pseudo-effective and big Bott-Chern cohomology classes, proposing a new Monge-Ampère-type equation.
result Proof of a form of uniqueness for solutions of the Monge-Ampère-type equation.
Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.
problem Understanding disposition effect in short vs long exposure positions and systematic risk.
method Generalized Odean measures, introduced Value metric, implemented dispositionEffect R package.
result Short positions exhibit weaker disposition effect than long positions under narrow framing, reversing in integrated framing.
Study functional confounders in causal inference, enabling estimable effects.
problem Causal inference challenges with functional confounders violating positivity.
method Functional interventions, functional positivity, gradient fields, Level-set Orthogonal Descent Estimation (LODE).
result Valid causal effect estimation under certain conditions.
Study examines Bitcoin's volatility and returns using stochastic volatility model.
problem Characterizing Bitcoin as a financial asset and its volatility patterns.
method Asymmetric stochastic volatility model applied to Bitcoin data from 2013-2019.
result Bitcoin shows weak post-holiday effects and no asymmetry effect in returns and volatility.
Solves open problems on curved projective varieties.
problem Structure theorems for curved projective varieties.
method Supplements and proposes open problems.
result Provides new insights into structure of curved projective varieties.
Introduce Collapsed Effective Operators for higher-order structures.
problem Existing spectral operators decompose topology into separate ranks, leaving practitioners to fuse information back to vertices.
method Introduce Collapsed Effective Operators via Schur complementation of a graded Laplacian.
result Preserves positive semi-definiteness, lowers system energy under higher-order connectivity.
Curved 10-manifolds with torus symmetry are spheres or complex projective spaces.
problem Characterizing positively curved manifolds with torus symmetry.
method Analyzing actions of 3-dimensional tori on closed, simply connected 10-manifolds. result Closed, simply connected, positively curved 10-manifolds with T3-symmetry are homotopy spheres or complex projective spaces. CATR rationalizes text data to stabilize causal effect estimation.
problem Observational positivity violation in high-dimensional text data.
method Confounding-Aware Token Rationalization (CATR) selects necessary subset of tokens.
result CATR yields more accurate and stable causal effect estimates.
This study uses complex networks to analyze influential spreaders and their effects on different market sectors.
problem Existing methods failed to distinguish between positive and negative influences of market sectors.
method LIEST (Local Influential Effects for Specific Target) method using complex network analysis.
result LIEST effectively distinguishes positive and negative influences of market sectors during different periods.
MiFID II impacts European stock liquidity and price formation.
problem Impact of MiFID II on European stock liquidity and price formation.
method Analyzed effects of MiFID II on European stock markets, focusing on intraday and closing auction liquidity and tick size changes.
result Closing auction volumes increased and price formation became more efficient after MiFID II.
There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the exis…
We point out that a 4-dimensional topological manifold with an Alexandrov metric (of curvature bounded below) and with an effective, isometric action of the circle or the 2-torus is locally smooth. This observation implies that the topological and equivariant classifications of compact, simply connected Riemannian 4-ma…
Study analyzes factors affecting capital adequacy in Bangladesh's banks.
problem Factors influencing capital adequacy in commercial banks in Bangladesh.
method Fixed Effect, Random Effect, and Pooled Ordinary Least Square (POLS) methods.
result Several independent variables significantly affect capital adequacy, with specific relationships between leverage, liquidity risk, and other factors.
Study introduces a new framework for policy learning without positivity assumption.
problem Learning optimal treatment assignment policies from observational data with constraints.
method Incremental propensity score policies and semiparametric efficiency theory.
result Validated framework's performance through numerical experiments.
The study bounds the effective diameter of graphs with positive Ollivier curvature.
problem Bounding the effective diameter of graphs with positive Ollivier curvature.
method Introducing reflective graphs and proving discrete Bonnet Myers theorem.
result The effective diameter bound is attained only for specific graphs.
This paper derives radial fields on manifolds of symmetric positive definite matrices.
problem Lack of an expression for radial fields on manifolds of symmetric positive definite matrices.
method Derives an expression for radial fields on manifolds of symmetric positive definite matrices.
result Derives an expression for radial fields on manifolds of symmetric positive definite matrices.
Positive-unlabeled (PU) learning trains a binary classifier using only positive and unlabeled data. A common simplifying assumption is that the positive data is representative of the target positive class. This assumption rarely holds in practice due to temporal drift, domain shift, and/or adversarial manipulation. Thi…
Neural networks learn patterns in random data, improving downstream performance.
problem Understanding what deep networks learn with random labels.
method Analytical and empirical study of convolutional and fully connected networks pre-trained on random labels.
result Pre-trained networks on random labels transfer faster to real datasets, despite specialization effects.
BiPE blends intra-segment and inter-segment encodings for better length extrapolation.
problem Improving length extrapolation in language models.
method Bilevel Positional Encoding (BiPE) that separates intra-segment and inter-segment encodings.
result BiPE enhances length extrapolation across various text modalities.
Proves effective positive mass theorem for AF manifolds and singular spaces.
problem Proves positive mass theorem for AF manifolds with singularities.
method Dimension reduction techniques, bypassing N. Smale's regularity theorem.
result Effective positive mass theorem for AF manifolds of dimension n≤8 with singularities. A gap in the proof of the main result in reference [1] in our original submission propagated into the constructions presented in the first version of our manuscript. In this version we give an alternative proof for the existence of Riemannian metrics with positive Ricci curvature on an infinite subfamily of closed, sim…
The paper proposes a method to find subgroups with significant treatment effects in noisy data.
problem Estimating the causal effects of interventions on noisy outcomes.
method A machine-learning method specifically optimized for finding subgroups with significant effects, designed to maximize the probability of obtaining a statistically significant positive treatment effect.
result The proposed method yields higher power in detecting subgroups affected by the treatment compared to standard tree-based tools.
Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by {dropout} and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set tre…
TMLE improves causal effect estimation in missing data scenarios with various positivity violations.
problem Estimating causal effects in studies with missing data and positivity violations.
method Targeted Maximum Likelihood Estimation (TMLE) with various missing data methods.
result Complete cases with TMLE incorporating an outcome-missingness model exhibit lower bias and greater robustness against positivity violations.
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.
We study positivity in the conjecture proposed by Lejmi and Székelyhidi on finding effective necessary and sufficient conditions for solvability of the inverse σk equation, or equivalently, for convergence of the inverse σk-flow. In particular, for the inverse σn−1-flow we partially verify their conjecture b…
Novel framework identifies pump-specific deterioration rates using Bayesian hierarchical hazard modeling and causal discovery.
problem Challenges in asset management due to heterogeneous deterioration rates in pump equipment.
method Bayesian hierarchical hazard modeling with causal discovery, GPU-accelerated No-U-Turn Sampling (NUTS), and DirectLiNGAM.
result Identified striking heterogeneity in deterioration rates, with negative effects 400 times larger than positive effects.
We prove that the Euler characteristic of an even-dimensional compact manifold with positive (nonnegative) sectional curvature is positive (nonnegative) provided that the manifold admits an isometric action of a compact Lie group G with principal isotropy group H and cohomogeneity k such that $k - (\rank G - \ran…
A new method for PU learning improves classification error on CIFAR-10.
problem Learning from positive and unlabeled data in practical applications.
method A simple yet effective data augmentation method based on consistency regularization.
result Achieves an averaged improvement of 3.40 points in classification error on CIFAR-10.
The study shows interest rates impact investment and funding negatively but positively on dividend decisions.
problem The effect of interest rates on financial decisions like investment, funding, and dividend.
method Correlation coefficient analysis and descriptive methods.
result Interest rates have a negatively insignificant effect on investment and funding decisions, but positively moderate effect on dividend decisions.
It is well-known by the work of Hsiang and Kleiner that every closed oriented positively curved 4-dimensional manifold with an effective isometric S^1-action is homeomorphic to S^4 or CP^2. As stated, it is a topological classification. The primary goal of this paper is to show that it is indeed a diffeomorphism classi…
New DKPP family controls positive and negative dependence in random subsets.
problem Challenges in seamlessly bridging probabilistic models for positive and negative dependence.
method Introduced DKPP family and developed computational methods for probabilistic operations and inference.
result Controllability of positive and negative dependence demonstrated through numerical experiments.
We use a local argument to prove if an r-dimensional torus acts isometrically and effectively on a connected n-dimensional manifold which has positive kth-intermediate Ricci curvature at some point, then r≤⌊2n+k⌋. This symmetry rank bound generalizes those established by Gr…
The paper finds a surprising positive correlation between upstreamness and downstreamness in global value chains.
problem The puzzling positive correlation between upstreamness and downstreamness in industries and countries.
method Analysis of a simple model of random Input/Output tables and experiments on empirical data.
result Upstreamness and downstreamness of the same industrial sector/country are positively correlated with a slope close to +1.
New method estimates treatment effects from treated and unlabeled units.
problem Estimating ATEs with missing data and weak supervision.
method Develops semiparametric efficient estimators for ATE in PU learning.
result Constructs estimators that achieve semiparametric efficiency bounds.
We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contribu…
Buyer--seller relationships among firms can be regarded as a longitudinal network in which the connectivity pattern evolves as each firm receives productivity shocks. Based on a data set describing the evolution of buyer--seller links among 55,608 firms over a decade and structural equation modeling, we find some evide…
Let M and N be two compact complex manifolds. We show that if the tautological line bundle OTM∗(1) is not pseudo-effective and OTN∗(1) is nef, then there is no non-constant holomorphic map from M to N. In particular, we prove that any holomorphic map from a compact complex mani…
The visibility transformation embeds data position into signature features for efficient pattern recognition.
problem Embedding absolute position into signature features for efficient pattern recognition.
method The visibility transformation is put on a theoretical footing and used to embed absolute position into signature features efficiently.
result The generated feature set simplifies pattern recognition by accommodating nonlinear functions of absolute and relative values.
We introduce two types of ordinal pattern dependence between time series. Positive (resp. negative) ordinal pattern dependence can be seen as a non-paramatric and in particular non-linear counterpart to positive (resp. negative) correlation. We show in an explorative study that both types of this dependence show up in …
SDAMI enhances interpretable high-dimensional regression with sparse deep learning and footprint principle.
problem Personalized models for small samples and high-dimensional features with interpretability.
method Sparse Deep Additive Model with Interactions (SDAMI) combining sparsity-driven feature selection and deep subnetworks.
result SDAMI successfully identifies pure interactions with near-zero false positive rates.
We describe the positive cone and the pseudo-effective cone of a non-Kählerian surface. We use these results for two types of applications: - Describe the set σ(X) of possible total Ricci scalars associated with Gauduchon metrics of fixed volume 1 on a fixed non-Kähhlerian surface, and decide whether the assignment $…
Extends expected value framework for cost-sensitive causal decision-making.
problem Optimizing operational decision-making with cost-sensitive causal classification.
method Introduces a cost-sensitive decision boundary based on estimated individual treatment effects, positive outcome probability, and cost parameters.
result Effective in maximizing expected causal profit, outperforming cost-insensitive ranking approach.
PiNGDA learns beneficial noise for graph augmentation stability.
problem Challenges in generating effective and stable graph augmentations.
method PiNGDA uses positive-incentive noise to scientifically analyze and generate beneficial graph augmentations.
result PiNGDA improves GCL performance by learning beneficial noise on graph topology and attributes.
Paper proposes sparse classification method for high-dimensional data.
problem Sparse classification in high-dimensional data with positive-confidence samples.
method Developed a novel sparse-penalization framework using L1, SCAD, and MCP penalties for convex and non-convex shrinkage.
result Proved near minimax-optimal sparse recovery rates under Restricted Strong Convexity condition.
The study of stable minimal surfaces in Riemannian 3-manifolds (M,g) with non-negative scalar curvature has a rich history. In this paper, we prove rigidity of such surfaces when (M,g) is asymptotically flat and has horizon boundary. As a consequence, we obtain an effective version of the positive mass theorem …