Perceptual ad-blocking is vulnerable to attacks, creating new security risks.
problem Vulnerability of perceptual ad-blocking to attacks and new security risks.
method Analysis and creation of adversarial examples to bypass perceptual ad-blocking.
result Perceptual ad-blocking can be bypassed using adversarial examples, introducing new security risks.
A new framework for personalized ad retrieval in e-commerce search.
problem Difficulty in measuring ads retrieved using multiple signals (e.g. user profiles, clicks).
method Employing historical click data to initialize a hierarchical network representing signals, keys, and ads. Training a model to learn weights of edges and selecting the best edges.
result Framework achieves good performance, improving RPM/CTR.
Study proposes an ad creative selection algorithm considering user fatigue.
problem Ad creative effectiveness varies with user exposure.
method Proposes an algorithm that considers user fatigue in ad selection.
result The proposed algorithm outperforms baseline algorithms in real-world testing.
The paper proposes a method to predict audio ad quality using acoustic features.
problem Improving user experience in online music streaming services by ensuring high quality audio advertisements.
method The paper proposes predicting audio ad quality using acoustic features and a proxy metric called Long Click Rate (LCR). A deep learning model is also introduced.
result The proposed deep learning model outperforms other models trained on hand-crafted features for audio ad quality prediction.
Detects accidental clicks on mobile ads to reduce advertiser costs and improve revenue.
problem Accidental clicks on mobile ads lead to wasted revenue for advertisers and ad networks.
method Collect and analyze dwell time data to identify accidental clicks and estimate thresholds.
result Our method reduces advertiser costs and improves ad click-through rates and revenue.
In markets for online advertising, some advertisers pay only when users respond to ads. So publishers estimate ad response rates and multiply by advertiser bids to estimate expected revenue for showing ads. Since these estimates may be inaccurate, the publisher risks not selecting the ad for each ad call that would max…
Improves CTR prediction by considering spatial and temporal auxiliary ads.
problem Improving CTR prediction in online advertising systems.
method Deep Spatio-Temporal Neural Networks (DSTNs) for CTR prediction.
result DSTNs outperform state-of-the-art methods in CTR prediction.
Sharp Minkowski inequality found for AdS-Melvin spacetime surfaces.
problem Proving a Minkowski-type inequality for surfaces in the AdS-Melvin space.
method Used weighted normal flow to prove inequality for general surfaces.
result Sharp Minkowski inequality holds for all surfaces in AdS-Melvin space.
EENMF improves e-commerce sponsored search efficiency and effectiveness.
problem Improving efficiency and effectiveness of e-commerce sponsored search.
method End-to-end neural matching framework (EENMF) for vector-based ad retrieval and neural pre-ranking.
result Significantly outperforms baseline in real e-commerce traffic.
A framework combines unsupervised and semi-supervised AD using synthetic anomalies.
problem Improving anomaly detection in both unsupervised and semi-supervised settings.
method Proposes a new framework that uses both known and synthetic anomalies for training.
result Synthetic anomalies improve anomaly modeling in low-density regions and provide optimal convergence guarantees.
Percival uses deep learning to block ads in real-time, minimizing performance impact.
problem Real-time ad blocking in browsers with minimal performance overhead.
method Deep learning for image classification integrated into browser's rendering pipeline.
result Percival achieves 96.76% accuracy in blocking ads, comparable to block lists.
Special geometries found in near horizon spacetimes.
problem Understanding the near horizon geometry of extremal Reissner-Nordstrom solutions.
method Analyzing asymptotically AdS2imesS2 spacetimes under null energy conditions. result Asymptotically AdS2imesS2 spacetimes must have special geometries similar to AdS2imesS2. Enhanced ad event prediction boosts performance.
problem Improving ad performance metrics like CTR and CVR.
method New feature engineering approach for ad event prediction.
result Significantly outperforms alternative prediction algorithms.
Landmark AD improves AD's efficiency without sacrificing performance.
problem Computational burden in diffusion-based sensor fusion.
method Inspired by landmark diffusion, proposes Landmark AD.
result Landmark AD offers superior computational efficiency.
New examples of Lie algebras with ad-invariant metrics found.
problem Finding ad-invariant metrics on nonnice nilpotent Lie algebras.
method Introducing single extension method to construct Lie algebras with ad-invariant metrics.
result Explicit examples of nonnice nilpotent Lie algebras with ad-invariant metrics for dimensions > 10 and steps > 2.
Survey of Lie algebras with ad-invariant metrics.
problem None explicitly stated in the abstract.
method Summary of features, notions, and constructions.
result Main research on the topic summarized.
Article shows AdS-quasi-Fuchsian groups' limit sets are not smooth.
problem Smoothness of limit sets of AdS quasi-Fuchsian groups.
method Analyzes limit sets of AdS quasi-Fuchsian groups in PO(n,2).
result Limit sets are never C^1, except for Fuchsian groups.
This study proves energy bounds in specific AdS spacetimes.
problem Proving positive energy theorems in asymptotically locally AdS spacetimes.
method Derived positive energy theorem for spacetimes with compact, Einstein cross-sections.
result First complete proofs of BPS inequalities in AdS and locally AdS spacetimes.
New framework uses EEG to detect brain atrophy in AD, validated on large AD trial.
problem Diagnosis of Alzheimer's disease relies on subjective clinical interpretations.
method Combines Riemannian tangent space mapping and elastic net regression.
result Developed brain atrophy markers validated on large AD trial.
We formulate the variational problem for AdS gravity with Dirichlet boundary conditions and demonstrate that the covariant counterterms are necessary to make the variational problem well-posed. The holographic charges associated with asymptotic symmetries are then rederived via Noether's theorem and `covariant phase sp…
Study on uniqueness of ad-invariant metrics in Lie algebras.
problem Uniqueness of ad-invariant metrics in Lie algebras up to automorphisms.
method Analysis of Lie algebras, cotangent Lie algebras, and specific conditions for uniqueness.
result Uniqueness of ad-invariant metric on T∗g implies solvability of g, but not conversely. Study identifies five AD subtypes using graph diffusion and similarity learning.
problem Identifying homogeneous AD subtypes to improve diagnosis and treatment.
method Unsupervised clustering with graph diffusion and similarity learning.
result Five distinct AD subtypes identified with significant differences in biomarkers and clinical features.
New method constructs AdS 3-manifolds and applies to Higgs bundles and minimal immersions.
problem Understanding AdS 3-manifolds and their connections to Higgs bundles and minimal immersions.
method Developed a new construction method for AdS structures.
result Recovered Tholozan's formula for AdS 3-manifold volumes and characterized representations for minimal immersions.
Constructs G2-orbifolds with ADE-singularities from K3 surfaces.
problem Finding compact G2-orbifolds with specific properties. method Quotients of C2imesT3 related to K3 surfaces. result Examples with exactly one parallel spinor.
This paper examines how ads on LinkedIn affect user behavior over time.
problem Understanding long-term impact of ads on user engagement and revenue.
method Conducted experiments with randomized member buckets to measure short and long-term effects of ads density.
result Long-term impact of ads is much smaller than short-term impact, and different user cohorts react differently over time.
Proves existence of AdS manifold with prescribed metrics on boundary.
problem Prescribing metrics on the boundary of AdS 3-manifolds.
method Using duality between convex space-like surfaces in AdS₃, proves existence of AdS manifold with prescribed metrics.
result Existence of AdS manifold with prescribed metrics on boundary.
AdS uniqueness and black hole energy bounds proven.
problem Proving uniqueness of Anti-de Sitter spacetime and energy bounds for AdS black holes.
method Adapted Wang's proof to static asymptotically locally hyperbolic vacuum metrics and higher-genus horizons.
result Negativity of free energy E−TS for AdS black holes with higher-genus horizons. Optimizes bidding strategies for LinkedIn ads across multiple platforms.
problem Optimizing automated bidding agents for dynamic online marketplaces.
method Developed a general optimization framework for buyer's interest, agnostic to auction mechanisms.
result Automatically guarantees the optimality of budget allocation across ad units and platforms.
Paper proposes bypassing implicit assumption in GM-based AD methods.
problem Lack of anomalous data and implicit assumption in GM-based AD methods.
method Integrating Discriminative idea to GMM for AD tasks (DiGMM).
result Establishes a connection between generative and discriminative models for AD.
Geometries and dual field theories linked by AdS/CFT.
problem Understanding the AdS/CFT correspondence.
method Geometric extremization principles informed by physical considerations.
result Key role of Sasaki-Einstein and GK geometry.
The study constructs AdS manifolds from Gromov-Thurston manifolds.
problem Creating hyperbolic and anti-de Sitter structures from Gromov-Thurston manifolds.
method Explicit correspondence between quasifuchsian AdS manifolds and compact quotients of Ø(2d,2)/U(d,1).
result Existence of quasifuchsian AdS manifolds and hyperbolic ends with specified boundary.
Crooked planes are piecewise linear surfaces that were introduced by Drumm in the early 1990s to construct fundamental domains for properly discontinuous actions of free groups on Minkowski 3-space. In a previous paper, we introduced analogues of these surfaces, called AdS crooked planes, in the 3-dimensional anti-de S…
Meta-Embedding improves CTR predictions for new ads, addressing cold-start and warm-up issues.
problem Improving CTR predictions for new ads with little logging data.
method Meta-learning approach to generate initial embeddings for new ad IDs.
result Meta-Embedding significantly improves CTR predictions for various models, including lightweight and deep learning.
The stability of physical systems depends on the existence of a state of least energy. In gravity, this is guaranteed by the positive energy theorem. For topological reasons this fails for nonsupersymmetric Kaluza-Klein compactifications, which can decay to arbitrarily negative energy. For related reasons, this also fa…
A Q-learning approach optimizes RTB ad campaigns for mobile app installs.
problem Optimizing RTB ad campaigns for mobile app installs with delayed rewards.
method State space based policy trained via Q-learning algorithm to handle delayed install notifications.
result Significant increase in profit and number of efficient campaigns.
Proposes a statistical test for VAE-based anomaly detection reliability.
problem Ensuring reliability of anomaly detection in high-stakes applications.
method Variance Autoencoder (VAE) Test based on selective inference.
result Validates VAE-based anomaly detection with p-values controlling false detection probability.
Paper uses LSTM autoencoder for ADS-B data to detect surveillance aircraft.
problem Detecting surveillance aircraft from ADS-B flight data.
method LSTM-based sequence autoencoder for ADS-B data.
result LSTM autoencoder effectively learns features for detecting surveillance aircraft.
Study of hyperideal polyhedra in anti-de Sitter space.
problem Characterizing hyperideal polyhedra in anti-de Sitter space.
method Defined hyperideal polyhedra as intersections with convex polyhedra in projective model of anti-de Sitter space.
result Hyperideal polyhedra uniquely determined by combinatorics, dihedral angles, and induced metrics on boundary.
Study of AdS backgrounds preserving supersymmetry using generalised geometry.
problem Characterize AdS backgrounds preserving supersymmetry.
method Use generalised geometry to study supersymmetric AdS backgrounds of eleven-dimensional or type II supergravity.
result Show that a class of AdS backgrounds correspond to spaces with weak generalised holonomy.
Transforms hyperbolic to flat data, deriving geometric inequalities.
problem Deriving geometric inequalities in asymptotically AdS hyperbolic spacetimes.
method Constructs transformations preserving physical quantities to relate hyperbolic to flat spacetimes.
result Derives geometric inequalities from flat counterparts.
Study of waves on Reissner-Nordström-AdS black holes, proving uniform boundedness and continuity.
problem Analyzing waves on black holes in AdS spacetimes, focusing on uniform boundedness and continuity.
method Initial data on a spacelike hypersurface, Dirichlet boundary conditions at infinity, proving uniform boundedness and continuity.
result Uniform boundedness and continuity of waves at the Cauchy horizon on Reissner-Nordström-AdS black holes.
Zero-shot anomaly detection method using batch normalization.
problem Adapting anomaly detectors to new normal data distributions without training data.
method Adaptive Centered Representations (ACR) with batch normalization.
result First zero-shot AD results for tabular data and image data.
Study examines how different types of advertising impact brand attitudes.
problem Understanding how advertising types affect brand perceptions.
method Analysis of 575 brands over five years, using national, local, and digital ads, with brand/quarter and industry/week fixed effects.
result Different types of advertising have varying impacts on brand perceptions.
We study the causality relation in the 3-dimensional anti-de Sitter space AdS and its conformal boundary Ein. To any closed achronal subset Λ in Ein_2 we associate the invisible domain E(Λ) from Λ in AdS. We show that if Γ is a torsion-free discrete group of isometries of AdS preserving Λ and is non-elem…
Paper demystifies AD techniques for efficient deep learning.
problem Generalizing backpropagation for complex neural networks.
method Uncovering reverse-mode AD and its connection to delimited continuations, implementing it via operator overloading.
result Efficient reverse-mode AD without auxiliary data structures, combining deep learning and pure library approaches.
Project predicts Alzheimer's progression using neural networks and novel data processing.
problem Difficulty in early identification of Alzheimer's patients.
method Used machine learning, specifically neural networks, and a novel pre-processing technique.
result Neural network model accurately predicts AD progression with high accuracy.
Study on numerical reliability of AD for MaxPool in neural nets.
problem Reliability of automatic differentiation for nonsmooth operations like MaxPool.
method Investigation across precision levels and architectures on various datasets.
result Lower norms of nonsmooth Jacobians help maintain stable learning.
New AD methods improve likelihood estimation for partially observed systems.
problem Estimating likelihood functions for partially observed nonlinear systems.
method Embedding AD particle filter methods in a theoretical framework, developing new algorithms for likelihood maximization.
result Mean squared error significantly lower than existing algorithms.