Extends virtual link theory to arbitrary dimensions.
problem Defining and studying virtual links in higher dimensions.
method Geometric and combinatorial approaches, including diagrams and Gauss codes.
result Many classical link invariants extend to virtual links.
Fox-Milnor theorem extended to knots in thickened surfaces.
problem Extending classical knot theory to knots in thickened surfaces.
method Using Milnor torsion to prove a Fox-Milnor theorem for knots in a thickened surface.
result A Fox-Milnor theorem for concordant knots in a thickened surface.
We define a generalization of virtual links to arbitrary dimensions by extending the geometric definition due to Carter et al. We show that many homotopy type invariants for classical links extend to invariants of virtual links. We also define generalizations of virtual link diagrams and Gauss codes to represent virtua…
Study algebraic concordance groups for non-trivial links.
problem Invariance of signature, Fox-Milnor condition, and Blanchfield pairing under concordance.
method Defined algebraic concordance groups using generalized Seifert matrices.
result Recovery of invariance of signature, Fox-Milnor condition, and Blanchfield pairing for concordant links.
New results on algebraic knots with Brieskorn polynomials.
problem Understanding cobordisms of algebraic knots defined by Brieskorn polynomials.
method Analyzing Fox--Milnor type relations, decomposing algebraic cobordism classes, and studying cyclic suspensions.
result Spherical algebraic knots associated with Brieskorn polynomials have infinite order in the knot cobordism group.
Study on equivariant Q-sliceness for strongly invertible knots.
problem Understanding Q-sliceness for strongly invertible knots.
method Constructive and obstructive approaches using Fox-Milnor condition and equivariant concordance.
result Klein amphichiral knots are equivariant Q-slice in a single Q-homology 4-ball.
For proper subsets U of {1,2,...,31} we define and construct U-regular isotopy invariants of the Carter-Rieger-Saito movies (representing knotted surfaces) and use these to show that there are ambient isotopic knotted surfaces represented by movies M1 and M2 for which movie-moves of type 31 are required to get from M1 …
Predicting movie box office success using historical data and modern computing.
problem Manual prediction of movie revenue is difficult due to many exogenous variables.
method Use modern computing power and historical data to model movie revenue.
result Predicted movie revenues can be used for planning production and distribution stages.
Movie moves connect singular link cobordisms in 4D space.
problem Tackling the concept of singular link cobordisms in 4D space.
method Presenting a set of movie moves to connect any two movies of isotopic singular link cobordisms.
result A set of movie moves sufficient to connect any two movies of isotopic singular link cobordisms.
A model predicts user movie preferences based on novelty-seeking traits.
problem Accurately predicting user movie preferences for competitive websites.
method DFNSM model uses demographic, genre, and novelty-seeking data.
result DFNSM outperforms previous models in movie recommendation accuracy.
We present a grid diagram analogue of Carter, Rieger and Saito's smooth movie theorem. Specifically, we give definitions for grid movies, grid movie isotopies and present a definition of grid planar isotopy as a particular subset of the grid diagram moves: stabilization, destabilization and commutation. We show that gr…
This study compares feature extraction methods using Neural Networks and Latent Dirichlet Allocation for movie synopses.
problem Extracting meaningful features from movie synopses for pattern detection and recommendation.
method Employed Latent Dirichlet Allocation for topic modeling and Neural Networks for distributed paragraph representations.
result Latent Dirichlet Allocation can provide meaningful features for movie synopses, comparable to Neural Networks.
Paper classifies movie genres using multimodal data.
problem Challenging task of multi-label movie genre classification.
method Created dataset from video clips, subtitles, synopses, and posters. Extracted features using various descriptors. Evaluated using different classifiers and late fusion strategy.
result Best F-Score result of 0.628 achieved by combining LSTM on synopses and CNN on movie trailer frames.
A movie multilayer network model captures narration from script, subtitles, and content.
problem Discovering content and stories in movies using network models.
method Developed a multilayer network model using visual and textual semantic cues.
result Demonstrated the effectiveness of the model on the Star Wars saga.
Hybrid VAE improves movie recommendation accuracy.
problem Improving personalized recommendations in online marketplaces.
method Combining movie embeddings from a sibling VAE network with user ratings for movie recommendation.
result Empirical evidence shows VAE network benefits from incorporating movie embeddings.
A shadow diagram is a knot diagram with under-over information omitted; a shadow movie is a sequence of shadow diagrams related by shadow Reidemeister moves. We show that not every shadow movie arises as the shadow of a Reidemeister movie, meaning a sequence of classical knot diagrams related by classical Reidemeister …
Study of equivariant movie moves for involutive links.
problem Equivariant cobordisms between involutive links.
method Equivariant Morse theory and singularity theory.
result 39 equivariant movie moves for isotopic cobordisms.
Machine learning predicts movie genres from summaries with high accuracy.
problem Predicting movie genres from plot summaries.
method Used Naive Bayes, Word2Vec+XGBoost, Recurrent Neural Networks, and Gated Recurrent Units (GRU) for text classification and multi-label problem.
result GRU neural networks achieve the best result with a Jaccard Index of 50.0%, F-score of 0.56, and hit rate of 80.5%.
GWCA analyzes cross-graph correlations for movie retrieval.
problem Cross heterogeneous graph comparison in movie retrieval.
method Spectral graph filtering, Wasserstein metric learning, generalized eigenvalue decomposition.
result Surprise consistency in learning processes and closed-form solution.
We present a marked analogue of Carter and Saito's movie theorem. Our definition of marking was chosen to coincide with the markings that arise in link Floer homology. In order to deal with complications arising from certain isotopies, we define three equivalence relations for marked surfaces and work over an equivalen…
A new method for better recommendation by accounting for hidden factors.
problem Hidden factors affecting both watched movies and ratings.
method Two-stage probabilistic models to remove bias due to confounding.
result Improved recommendation and stable performance against interventions.
Study uses EEG and ML to predict movie ratings with 72% accuracy.
problem Predicting consumer preferences for movie trailers.
method EEG and machine learning techniques to analyze brain responses to movie trailers.
result Predicted movie ratings with 72% accuracy.
Improved video and movie description using multitask learning.
problem Lack of training data and poor generalization in video captioning.
method Multitask learning encoder-decoder framework for video sequences.
result Improved performance on multi-caption and single-caption datasets.
Study analyzes IMDB movie comments and Twitter data using machine learning and vector space techniques.
problem Sentiment analysis of IMDB movie comments and Twitter data.
method Created a vector space in KNIME Analytics platform, used Decision Trees, Naïve Bayes, and SVM algorithms for classification.
result SVM algorithm provided the best classification results for both IMDB movie comments and Twitter data sets.
The paper compares and improves aggregators for relational probabilistic models.
problem Predicting gender from movie ratings is challenging due to varying numbers of movies per user and users per movie.
method The paper compares existing aggregators and proposes new ones, showing their empirical superiority.
result New aggregators and model modifications outperform existing ones.
New condition for Alexander polynomial factorization in 4D knots.
problem Alexander polynomial factorization for 2-knots in S 4 S^4 S 4 . method Alternative notion of ribbon 2-knots to prove factorization condition.
result Topological condition for factorization of Alexander polynomial.
Improved multimodal learning with Gated Multimodal Units.
problem Finding an intermediate representation from multiple data sources.
method Gated neural networks for multimodal fusion.
result GMU outperformed single-modality approaches and other fusion strategies.
Solves the film scheduling and staggered showtimes problem for movie theaters.
problem Maximize attendance and revenue by scheduling films with staggered showtimes.
method Binary integer linear optimization to find optimal schedules for each cluster of neighboring locations.
result Optimal scheduling cannot be done for all locations at once, but must be done for each cluster.
Adapting momentum from optimization to reinforcement learning.
problem Improving the convergence and stability of reinforcement learning algorithms.
method Introducing Momentum Value Iteration (MoVI) by incorporating an average of consecutive state-action value functions, inspired by the concept of momentum in optimization.
result MoVI improves the convergence and stability of reinforcement learning algorithms, as demonstrated by experiments on Atari games.
The paper proposes a new method for user-movie recommendation systems.
problem Improving recommendation accuracy in collaborative filtering.
method Uses Empirical Bayes with Reversible Jump Markov Chain in a Bayesian setup.
result Demonstrates improved hyper-parameter tuning and recommendation accuracy.
New approach uses selection bias to improve movie recommendation accuracy.
problem Challenges in recommending items due to missing data.
method Computational variational approach exploiting selection bias.
result Improves rating estimation from small user populations.
Generative methods for creating new items for user groups.
problem Creating new items for groups of users with varying preferences.
method Formalized joint problem, used VAE latent space for item generation and user group prediction.
result Generated items similar to highly desirable unobserved items.
27 problems identified in automating movie/TV subtitle translation.
problem Challenges in translating movie/TV subtitles.
method Categorized problems into three categories and evaluated translation quality.
result Frontier NLP systems struggle with subtitles and require post-processing.
Fibered knots and disks extend fibrations in 4D space.
problem Extending fibrations from knot complements to disk complements.
method Constructing movies of singular fibrations and identifying sufficient conditions.
result Fibrations extend when disks have exactly two local minima.
Proposes a machine learning method to evaluate creative artifacts.
problem Need for objective and automatic evaluation of creative artifacts.
method Regression-based learning framework considering novelty, influence, value, and unexpectedness.
result Promising results in predicting movie ratings and identifying creative movies.
The Financial Crisis of 2008 is a worldwide financial crisis causing a worldwide economic decline that is the most severe since the 1930s. According to the International Monetary Fund (IMF), the global financial crisis gave impact on USD 3.4 trillion losses from financial institutions around the world between 2007 and …
The paper introduces signatures for virtual knots and applies them to study concordance.
problem Investigating the concordance of virtual knots and their slice genus.
method Defined Tristram-Levine signatures for almost classical knots, used Seifert pairing, and introduced parity projection.
result Established slice obstructions for all virtual knots and determined slice status for almost classical knots.
Predict missing movie ratings or graph embeddings with low rank matrices.
problem Predicting missing entries in a ratings matrix or graph embeddings with known linear relations.
method Low rank matrix completion approach applied to graph embeddings.
result Effective methods for predicting missing entries in matrices and graph embeddings.
New procedures connect braid charts, triplane diagrams, and braid movies for knotted surfaces.
problem Understanding the braid index and bridge index of knotted surfaces in 4D.
method Introducing rainbow diagrams and new procedures for passing among triplane diagrams, braid movies, and braid charts.
result Inequalities relating braid index and bridge index of 2-knots are obtained.
Improved UCB and Thompson Sampling policies for CMAB with probabilistically triggered arms achieve bounded regret.
problem Combinatorial multi-armed bandit problem with probabilistically triggered arms.
method Upper Confidence Bound (UCB) policies and Combinatorial Thompson Sampling (CTS).
result CUCB- κ κ κ and CTS achieve O ( T ) O(\sqrt{T}) O ( T ) gap-independent regret. Study shows pruning datasets can improve machine learning model performance.
problem Improving machine learning model performance through dataset pruning.
method Comparison of different algorithms on unpruned and iteratively pruned datasets.
result Algorithms that perform better on unpruned datasets also perform better on pruned datasets.
AI2V learns user representations by focusing on recent interests.
problem User interests and behavior change over time, affecting recommendation quality.
method Introduces AI2V, a neural attentive model that learns user representations by focusing on recent interests.
result AI2V outperforms other models on various datasets.
The paper extends surface link coloring theory to triplane diagrams and knots.
problem Understanding the topological properties of knots and surfaces in 4-space.
method Translated Niebrzydowski's theory of region colorings to triplane diagrams and movies of knots, providing inequalities and applications.
result Yoshikawa's 2-knots 9 1 9_1 9 1 and 10 2 10_2 1 0 2 are non-invertible. MOVI learns optimal vehicle dispatch policies without models, reducing unserviced requests.
problem Optimizing vehicle dispatch to minimize passenger wait times in dynamic fleets.
method Model-free approach using Deep Q-network (DQN) for decentralized learning and centralized receding-horizon control comparison.
result DQN dispatch policy reduces unserviced requests by 76% compared to no dispatch and 20% compared to RHC.
Deep model tackles matrix completion issues.
problem Matrix completion problems in signal processing and machine learning.
method Deep matrix factorization model with a generic discretization operator.
result Efficacy demonstrated on a real movie rating dataset.
Defines a new homomorphism for strongly invertible knots, proving equivariant algebraic concordance.
problem Equivariant algebraic concordance of strongly invertible knots.
method Defining a homomorphism Φ Φ Φ from equivariant concordance group to a new equivariant algebraic concordance group, proving it lifts known homomorphisms and provides new obstructions. result Obtains a new obstruction to equivariant sliceness and novel lower bounds on equivariant slice genus.
Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distri…
Deep models learn interactions across sets, achieving state-of-the-art performance.
problem Modeling interactions across multiple sets of objects.
method Permutation-equivariant parameter-sharing scheme for deep learning.
result State-of-the-art performance on matrix completion benchmarks and extrapolation tasks.