Study investigates impact of labeler socio-cultural background on affect detection model performance.
problem Impact of labeler socio-cultural background on affect detection model performance.
method Investigates the impact of labeler socio-cultural background on affect detection model performance.
result Differences in labeler background impact the performance of affect detection models.
Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification where previous work has shown that it can be beneficial to incorporate a wide variety …
This paper improves route choice models by incorporating contextual factors.
problem Existing route choice models lack consideration of dynamic contextual conditions.
method Knowledge distillation from Stated Choice Experiments in Immersive Virtual Environment.
result High-fidelity route choice models with increased predictive power.
Study examines active travel in Chicago communities, revealing mixed perceptions.
problem Transport disadvantage and lack of active mobility in underserved communities.
method Focus groups, qualitative discourse analysis, quantitative text-mining (topic modeling, sentiment analysis).
result Residents view active travel as both necessity and symbol of privilege, influenced by local culture.
We evaluate the hedging performance of a high-order compact finite difference scheme from [4] for option pricing in Bates model. We compare the scheme's hedging performance to standard finite difference methods in different examples. We observe that the new scheme outperforms a standard, second-order central finite dif…
ADS explains object differences by quantifying and removing underlying properties.
problem Explaining differences between two object images.
method Align-Deform-Subtract (ADS) framework that uses semantic alignments and iterative quantification/removal of differences.
result ADS provides disentangled error measures explaining object differences in terms of underlying properties.
Proposes Population Difference Criterion for visually observed subpopulation differences.
problem Statistical significance of visually observed subpopulation differences in high-dimensional and high-signal contexts.
method Balanced permutation approach and bootstrap confidence interval for quantifying uncertainty.
result Balanced permutation approach is more powerful in high-signal contexts.
Typically options with a path dependent payoff, such as Target Accumulation Redemption Note (TARN), are evaluated by a Monte Carlo method. This paper describes a finite difference scheme for pricing a TARN option. Key steps in the proposed scheme involve tracking of multiple one-dimensional finite difference solutions,…
Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.
problem Improving accuracy in time series prediction.
method Combines difference-attention LSTM and error-correction LSTM in a cascade approach.
result Improves prediction accuracy in time series.
Different neural networks learn similar mappings with different weights.
problem Understanding shared representations across neural networks with varying weights.
method Shared response model and orthogonal transformations.
result Different neural networks encode the same input examples as different orthogonal transformations of an underlying shared representation.
The paper analyzes how the one-dimensional Wasserstein distance captures pointwise density differences in finite samples.
problem Uncertainty in identifying density differences when supports overlap and densities have substantial pointwise differences.
method Analysis using the Poisson process and neural spike train decoding.
result The one-dimensional Wasserstein distance highlights meaningful density differences related to both rate and support.
We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributions. The method operates by finding the projection that maximizes the Wasserstein divergence between the resulting univariate populations. Relying on the Cramer-Wold device, it requires no assumptions about th…
A new method for feature fusion in U-Net decoders using difference-based gating.
problem Precise fusion of high-level semantics and low-level details in U-Net decoder reconstruction.
method Proposes two difference-based gating approaches: Feature-difference gating (FDG) and Entropy-difference gating (EDG).
result Both FDG and EDG methods outperform existing attention-based fusion methods, with EDG showing superior performance.
SDD improves DD for estimating treatment effects by adjusting for confounding.
problem Estimating treatment effects in observational studies with confounding.
method Synthesized Difference in Differences (SDD) using RCT data to infer correct slopes.
result SDD achieves state-of-the-art performance across synthetic and real datasets.
Study of monopoles and q-difference modules correspondence.
problem Finding a correspondence between algebraic and differential geometric objects.
method Analogue of non-abelian Hodge theory for q-difference modules. result Doubly periodic monopoles and parabolic q-difference modules correspond. Manifolds uniquely identified by boundary distance differences.
problem Identifying Riemannian manifolds by their boundary distances.
method Distance difference representation on non-convex boundaries without restrictions.
result Complete Riemannian manifolds uniquely determined by their boundary distances.
New method corrects least-squares temporal difference for better lambda-return estimation.
problem Improving lambda-return estimation in reinforcement learning.
method Uncorrected least-squares temporal difference with a correction method.
result Enhanced accuracy in temporal difference learning.
Study of 2d gauged linear sigma models to derive difference equations and spectral data.
problem Understanding monopole solutions and their spectral data in 2d gauged models.
method Analyzing ground states and cohomology of supercharges to derive difference modules and equations.
result Derived novel difference equations for brane amplitudes and hemisphere partition functions.
Study on tilings of the plane with two types of tiles of varying areas.
problem Classifying tilings with minimal interface length.
method Analysis of isoperimetric configurations for different lattice types and tile areas.
result Three distinct tilings configurations found based on tile area ratio.
We introduce a framework to study the effective objectives at different time scales of financial market microstructure. The financial market can be regarded as a complex adaptive system, where purposeful agents collectively and simultaneously create and perceive their environment as they interact with it. It has been s…
Study Berry connections for 2d GLSMs, linking to cohomology theories.
problem Quantise ground states of 2d (2,2) GLSMs on a circle. method Relate periodic monopole solutions to difference modules and vector bundles with filtrations.
result Derive novel difference equations for brane amplitudes and vortex partition functions.
Paper presents a new method to detect process differences at the trace level using mutual fingerprints.
problem Low-level granularity in process variant analysis leads to many false differences.
method Develops a mutual fingerprint technique to encode entire process traces for comparison.
result Mutual fingerprint method reveals significant differences not detected by existing techniques.
Pareto MTL finds optimal solutions for multiple tasks with different trade-offs.
problem Finding a single optimal solution for multiple conflicting tasks.
method Formulate multi-task learning as multiobjective optimization, decompose into subproblems, solve in parallel.
result Generates well-representative Pareto optimal solutions for different trade-offs.
ES and FD gradients converge as optimization dimension grows.
problem Understanding the relationship between Evolution Strategies and Finite Differences gradients.
method Analyzing the convergence of gradients as the optimization dimension increases.
result ES and FD gradients converge as the dimension of the vector under optimization increases.
Neuroimaging research has predominantly drawn conclusions based on classical statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity, including cross-validation, pattern classification, and sparsity-inducing regression. These t…
Study shows significant differences in recommendation bias between model-based and memory-based algorithms.
problem Recommendation bias disparity across different algorithms and item categories.
method Examined bias disparity in a range of collaborative recommendation algorithms and item categories.
result Significant differences found between model-based and memory-based algorithms.
This work examines the sensitivity of energy distance to mean differences compared to covariance differences.
problem The sensitivity of energy distance to mean differences compared to covariance differences when distributions are close.
method Analyzes the energy distance in the case where distributions are close, focusing on sensitivity to mean and covariance differences.
result Energy distance is more sensitive to mean differences than covariance differences when distributions are close.
This study reveals efficient finite-difference computation for gradient regularization in deep learning.
problem Improving generalization performance in deep learning through gradient regularization.
method Analyzes and reveals a specific finite-difference computation that reduces computational cost and improves generalization performance.
result Finite-difference computation strengthens the implicit bias towards rich regimes and enhances generalization performance.
MB-DQN uses different backup lengths for improved reinforcement learning.
problem Improving reinforcement learning with multi-step returns.
method Integrates multi-step returns into bootstrapped DQN with different backup lengths.
result MB-DQN maintains advantages of different backup lengths.
Ghost points affect stability in finite difference schemes for diffusion equations.
problem Impact of ghost points on stability of finite difference schemes.
method Exploration of explicit Euler finite difference scheme with ghost points on diffusion equation.
result Stability of the scheme is affected by ghost points.
Develops a method to estimate network difference in high-dimensional time series data.
problem Estimating network differences in high-dimensional data can be unreliable.
method Uses an L1 penalty on the difference of inverse spectral densities to estimate network differences.
result Establishes consistency of the method for sparse network differences.
We study the space of linear difference equations with periodic coefficients and (anti)periodic solutions. We show that this space is isomorphic to the space of tame frieze patterns and closely related to the moduli space of configurations of points in the projective space. We define the notion of combinatorial Gale tr…
We prove that the N-colored Jones polynomial for the torus knot T_{s,t} satisfies the second order difference equation, which reduces to the first order difference equation for a case of T_{2,2m+1}. We show that the A-polynomial of the torus knot can be derived from this difference equation. Also constructed is a q-hyp…
Temporal difference learning explained through gradient splitting, improving convergence times.
problem Learning value functions in Markov Decision Processes with linear approximations.
method Interpreting TD learning as gradient splitting and applying convergence proofs from gradient descent.
result Improved convergence times for TD learning, especially with a minor variation.
We prove that functions defined on a lattice in a finite dimensional torus with bounded finite differences can be smoothly extended to the whole torus, and relate the bounds on the extension's derivatives with bounds on the original function's finite differences.
Paper directly estimates structural difference between SEMs from samples.
problem Estimating change in causal relationships between two conditions.
method Principled algorithm that recovers structural difference SEM in O(d^2 log p) samples.
result Method outperforms state-of-the-art and validates usefulness in medical domain.
This research explores how different discrete diffusion kernels affect graph generation quality.
problem The impact of different discrete diffusion kernels on graph generation quality.
method Developed a family of discrete diffusion kernels that converge to different Bernoulli priors.
result The quality of generated graphs is sensitive to the prior used, challenging previous intuitions.
Different optimizer choices lead to different financial model predictions.
problem The impact of optimizer choice on neural network models in financial time series.
method Analysis of large-scale volatility forecasting for S&P 500 stocks using various model-training-pipeline pairs.
result Optimizer choice reshapes non-linear response profiles and temporal dependence in financial models, leading to different functional outcomes.
Introduces a new geometry based on difference angles, showing unique properties.
problem Defining angles independently of circles or rotations.
method Axiomatic system for difference angles, defining new geometric constructs.
result Explicit confirmation of the concurrency of the parabolic Miquel configuration.
Researchers develop methods to prevent GANs from generating certain types of images.
problem Pre-trained GANs sometimes produce undesirable samples.
method Post-editing GANs to prevent certain types of outputs, using three algorithms.
result Our algorithms effectively prevent GANs from generating certain types of images while maintaining high quality.
Two different formulas for macro F1 lead to significant differences in classification evaluation.
problem Evaluation discrepancies in binary, multi-class, and multi-label classification problems.
method Comparison of two formulas for macro F1 metric.
result The two formulas can result in up to a 0.5 difference and different classifier rankings.
Paper finds conditions for different norms to produce same billiard paths.
problem Conditions for different norms to define the same billiard reflection law.
method Extending previous works by Milena Radnović and Serge Tabachnikov, the paper establishes conditions for two different non-symmetric norms to define the same billiard reflection law.
result Conditions for two different norms to define the same billiard reflection law.
We introduce and compare new variability measures based on risk quantiles.
problem Comparing variability measures in risk management.
method Developed a framework for one-parameter families of inter-Expected Shortfall differences and inter-expectile differences.
result Characterized symmetric and comonotonic variability measures as mixtures of inter-Expected Shortfall differences.
Unified recurrent networks reveal differences in complexity levels of grammars.
problem Understanding the complexity and behavior of recurrent networks.
method Connecting recurrent networks with deterministic finite automata and formal grammars.
result Unified recurrent networks improve performance and match grammars from different complexity levels.
Bayesian methods improve DiD analysis for ATT estimation.
problem Estimating ATT in DiD designs with improved accuracy.
method Semiparametric Bayesian outcome regression and doubly robust adjustment.
result Bayesian methods provide strong finite-sample performance.
The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S\&P 500 (USA), the IPC (Mexico) and the VIX (volatility index US…
ONN learns different representations for different operations to improve user response prediction.
problem Improving user response prediction in online advertising and recommendation systems.
method Proposes Operation-aware Neural Networks (ONN) to learn different representations for different operations.
result ONN consistently outperforms state-of-the-art models in both offline and online environments.
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats. Predicting infection from symptom data is critical, though using symptom data from varied studies in aggregate is challenging because the data is collected in diff…