Study reveals complex, multi-scale relationships between NYSE and BSE indexes.
problem Understanding the causal and correlation dynamics between NYSE and BSE indexes.
method Multi-scale analysis of monthly closing price indexes over 300 months.
result NYSE Granger causes BSE with a 9-month lag, and BSE reflects NYSE fluctuations with a smaller lag.
This paper studies the interplay between the N=2 gauge theories in three and four dimensions that have a geometric description in terms of twisted compactification of the six-dimensional (2,0) SCFT. Our main goal is to construct the three-dimensional domain walls associated to any three-dimensional cobordism. We find t…
Generator network replicates AAM's face recognition neuron responses.
problem Replicating AAM's face recognition neuron responses using a deep generative model.
method Using a variational auto-encoder, learned generator network from face images generated by AAM, capturing shape variations without explicit shape model.
result Inferred latent variables of the learned generator network have strong linear relationship with AAM's shape and appearance variables.
The twisted face-pairing construction of our earlier papers gives an efficient way of generating, mechanically and with little effort, myriads of relatively simple face-pairing descriptions of interesting closed 3-manifolds. The corresponding description in terms of surgery, or Dehn-filling, reveals the twist construct…
Paper simplifies calculating causation probabilities and ranks root causes.
problem Computational challenges in assessing causal relationships.
method Algorithmic simplifications and novel methodological framework for Root Cause Analysis.
result Significantly reduces computational complexity for calculating causation probabilities.
3D quantum trace map connects 3-manifold quantizations.
problem Quantization of 3-manifold character varieties.
method Study of stated skein modules and face suspensions.
result Existence of 3D quantum trace map proved.
The paper tackles fairness in algorithmic decision making using causal models.
problem Ensuring algorithmic decision making systems do not discriminate based on protected attributes.
method Introduces two definitions of group fairness (FACE and FACT) grounded in causal models and uses Rubin-Neyman potential outcomes framework for robust estimation.
result Demonstrates the effectiveness of FACE and FACT in detecting discrimination in real-world data sets.
NGAT predicts long-term stock trends using graph attention networks.
problem Lack of effective corporate relationship graph comparison methods and model complexity in stock prediction.
method Developed a Node-level Graph Attention Network (NGAT) for corporate relationship graphs.
result Demonstrated the effectiveness of NGAT across two datasets.
For sutured 3-manifolds M, there is a sutured Thurston norm due to Scharlemann. We show how depth one foliations of M and corresponding fibrations and the usual Thurston norm on the double of M are useful tools for computing this norm. In many examples, the faces of the unit ball of the sutured norm are related to cone…
The study explores statistical methods to interpret radiological models and identify key features.
problem Interpreting complex radiological models for clinical use.
method Exploration of statistical techniques to assess relationships between radiomic features.
result Identification of key relationships and features for improved interpretability.
A fundamental aspect of biological information processing is the ubiquity of sequence-function relationships -- functions that map the sequence of DNA, RNA, or protein to a biochemically relevant activity. Most sequence-function relationships in biology are quantitative, but only recently have experimental techniques f…
New method for causal discovery using peeling algorithms for various data types.
problem Challenges in causal discovery due to unmeasured confounders.
method Two peeling algorithms (bottom-up and top-down) for causal discovery with generalized structural equation models.
result Valid discovery of causal relationships and parent-child effects in diverse data types.
Cluster-DAGs improve causal discovery with prior knowledge.
problem Finding cause-effect relationships from high-dimensional data.
method Cluster-DAGs as prior knowledge framework, modified constraint-based algorithms Cluster-PC and Cluster-FCI.
result Cluster-PC and Cluster-FCI outperform baselines without prior knowledge.
Optimizes liquidity provision intervals for profitable AMM participation.
problem Financial losses from poor liquidity provision intervals and reallocation costs.
method Developed a tractable stochastic optimization problem.
result Computes optimal liquidity provision intervals for profitable liquidity concentration.
This research improves asset life prediction by integrating deep learning with mixture distributions.
problem Predicting residual useful life for assets with multiple failure modes.
method Integrates mixture (log)-location-scale distribution with deep learning.
result Proposed models outperform existing methods in predicting residual useful life.
Study examines financial performance determinants of Kenyan microfinance banks.
problem Competition from commercial banks threatens microfinance banks' financial performance.
method Descriptive research design with secondary data analysis.
result Operational efficiency, capital adequacy, and firm size positively correlate with financial performance.
Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to…
Study optimal contracts for pandemic risk, offering fixed shares and prevention mechanisms.
problem Optimal delegation contracts in the face of pandemic shutdown risk.
method Dynamic principal-agent model with exogenous early termination risk.
result Explicit characterization of optimal wage and action for prevention mechanisms.
Propose a new 3d quantum trace map that agrees with Garoufalidis and Yu's construction and extends to certain manifolds with ideal triangulated boundaries.
problem Relationship between two constructions of 3d quantum trace maps.
method Propose a new 3d quantum trace map.
result Proposed 3d quantum trace map agrees with Garoufalidis and Yu's construction and extends to certain manifolds with ideal triangulated boundaries.
Novel framework combines tree-based discretization and ILP matching for causal inference.
problem Challenges in identifying causal relationships from observational data.
method Combines tree-based discretization and ILP matching for causal inference.
result Yields computational efficiency and less biased ATT estimates.
CAN learns conditional and interventional distributions from unlabeled data.
problem Learning conditional and interventional distributions from unlabeled data.
method CAN framework with LGN and CIGN architectures, equipped with an intervention mechanism.
result CAN generates both interventional and conditional samples without needing the causal graph.
Bayesian approach models match and non-match score distributions over continuous covariates.
problem Complex evaluation of model performance over continuous covariates in biometric verification.
method Generative model of score distributions, mixture models, local basis functions, Bayesian inference.
result Accurate and effective method for studying model performance over continuous covariates.
Optimizes master faces for 2D and 3D face verification using evolutionary algorithms and neural networks.
problem Impersonation attacks using master faces for face-based identity authentication.
method Evolutionary algorithm in latent space of StyleGAN, neural network to direct search, 2D and 3D face reconstruction.
result Obtains high impersonation rates with fewer master faces for 2D and 3D face verification.
Face recognition systems are vulnerable to composite face reconstruction attacks.
problem Vulnerability of face recognition systems to composite face reconstruction attacks.
method Assumed attacker uses composite face parts to reconstruct faces faster and more efficiently.
result Current face recognition systems are extremely vulnerable to random search attacks.
Model improves CVR estimation in recommender systems by mitigating bias and overlooking causal relationships.
problem Data sparsity and sample selection bias in CVR estimation.
method Entire Space Counterfactual Multitask Model (ESCM2) incorporating counterfactual risk minimizer. result Significantly enhances recommendation performance by effectively mitigating bias and overlooking causal relationships.
Unified model for age-invariant face recognition with photorealistic face synthesis.
problem Reliable face recognition across ages remains challenging due to significant intra-class variations.
method Unified deep architecture for cross-age face synthesis and recognition, continuous face rejuvenation/aging, disentangled age-invariant face representations.
result Superior performance on CAFR and other cross-age datasets, promising generalizability to unconstrained face recognition.
Paper tackles domain generalization by minimizing domain-based covariance.
problem Training data and test data have different distributions, leading to poor generalization.
method Find a central subspace minimizing domain-based covariance while preserving functional relationships.
result The proposed method achieves better generalization performance on unseen test datasets.
This paper converts speech to match a face image and vice versa.
problem Matching speech to a face image and vice versa.
method Proposes a model with speech converter, face encoder/decoder, and voice encoder.
result Trained model converts speech to match a face image and generates a face image that matches the voice of input speech.
Universal adversarial patches prevent face detection in various frameworks.
problem Preventing face detection in state-of-the-art face detection systems.
method Investigated the phenomenon of patches that suppress face detection and proposed optimization-based approaches for automatic design.
result Universal adversarial patches can prevent face detection without introducing false positives.
Paper shows affine constraint is unnecessary for high-dimensional data.
problem The necessity of an affine constraint in affine subspace clustering.
method Theoretical and empirical analysis of conditions for correctness of affine subspace clustering methods.
result Affine constraint has negligible effect on clustering performance for high-dimensional data.
A deep learning strategy improves recommendation accuracy by leveraging trust and distrust relationships.
problem Data scarcity and cold-start problem in recommender systems.
method Social deep pairwise learning with a ranking loss function and social negative sampling.
result The proposed model achieves an 11.49% improvement over state-of-the-art methods.
Generates high-resolution faces based on attributes.
problem Creating realistic face images with user-specified attributes.
method Conditional CycleGAN, handling unpaired data and attribute control.
result Produces realistic face images with user-controlled attributes.
Study finds non-monotonic Value of Information in dynamic multi-market monopoly.
problem Investigates non-monotonicity in Value of Information for a price-setting monopolist.
method Uses a Bayesian inverse problem with Kalman-Bucy-Stratonovich filter in a dynamic discrete model.
result Non-monotonic relationship between signal variance and Value of Information.
A neural network learns phase space properties for time series analysis.
problem Lack of consistency and robustness in estimating embedding parameters.
method Forgetting mechanism neural network to learn phase space properties.
result Neural network approach is competitive or superior to state-of-the-art strategies.
New method for estimating lead-lag times between non-synchronously observed point processes.
problem Estimating lead-lag relationships between non-synchronously observed point processes.
method Formulate lead-lag estimation as CPCF shape estimation; propose kernel density estimation-based lead-lag time estimator.
result Proposed method delivers superior numerical performance and effective lead-lag time estimation.
Faces of quasi-arithmetic Coxeter polytopes are also quasi-arithmetic.
problem Characterizing faces of quasi-arithmetic Coxeter polytopes.
method Proof of quasi-arithmetic property of faces and sufficient condition for arithmetic faces.
result Lower-dimensional faces of quasi-arithmetic Coxeter polytopes are quasi-arithmetic.
In this paper, we study face vectors of simplicial posets that are the face posets of cell decompositions of topological manifolds without boundary. We characterize all possible face vectors of simplicial posets whose geometric realizations are homeomorphic to the product of spheres. As a corollary, we obtain the chara…
Meta-learning for estimating complex conditional distributions.
problem Estimating conditional densities in multimodal distributions.
method Noise contrastive estimation with kernel mean embeddings.
result Meta-learning can share representations across tasks for conditional density estimation.
Study on critical faces convergence in a Poisson point process.
problem Convergence of point processes associated with critical faces in a Čech filtration.
method Established convergence in M0-topology for critical faces above vanishing threshold. result Obtained limit theorems for positive and negative critical faces.
Estimates the capacity of face representations, providing upper bounds for automatic face recognition.
problem Estimating how many identities a face representation can resolve.
method Formulated as packing bounds on a low-dimensional manifold embedded in a deep representation space, accounting for manifold structure and noise.
result Demonstrated upper bounds of 2.7×10^4 and 8.4×10^4 for FaceNet and SphereFace at a FAR of 1%, respectively.
A new search system helps recall faces with probabilistic methods.
problem Challenging face recall due to large and unstructured search space.
method Probabilistic evolutionary approach using skew-normal distribution.
result Greater granularity, regularized, and realistic results in face recall.
Bayesian optimization generates personalized face stimuli for cognitive neuroscience.
problem Lack of personalized face stimuli in cognitive neuroscience studies.
method Combines GANs with Bayesian optimization to identify individual response patterns to faces.
result Algorithm efficiently generates optimal faces maximizing individual subject's response.
Deep learning identifies cervical spondylosis from sEMG signals.
problem Early identification of cervical spondylosis for improved cure rate and reduced costs.
method Convolutional neural network-based multi-channel algorithm.
result Significant improvement in CS identification compared to previous methods.
New method generates fast adversarial faces with high success rate.
problem Vulnerability of face recognition systems to adversarial attacks.
method Fast landmark manipulation method and semantic structure constrained attack.
result 99.86% success rate on state-of-the-art face recognition models.
DepthNets learns 3D face geometry and transformations without supervision.
problem Learning 3D face geometry and transformations from a single image.
method Unsupervised learning of facial keypoints depth, using backpropable loss for 3D transformations.
result DepthNets can predict 3D transformations and re-target faces to new poses or geometries.
ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.
problem Causal inference in high-dimensional multi-omics data, especially when ignoring the hierarchical structure.
method Two-tiered divide-and-conquer strategy with ancestral conditioning sets.
result Achieves polynomial-time complexity and accurately recovers ancestral relationships.
Motivated principally by the low-rank matrix completion problem, we present an extension of the Frank-Wolfe method that is designed to induce near-optimal solutions on low-dimensional faces of the feasible region. This is accomplished by a new approach to generating ``in-face" directions at each iteration, as well as t…
Generates realistic faces from detailed textual descriptions.
problem Face generation from fine-grained textual descriptions.
method Conditional GAN model with DC-GAN and GAN-CLS loss, using CelebA dataset with generated captions.
result Promising results in generating diverse face images from fine-grained textual descriptions.