Deep learning model predicts story points in agile projects.
problem Estimating effort for user stories in agile projects.
method Combines LSTM and Recurrent Highway Network for end-to-end story point prediction.
result Model outperforms existing baselines and alternatives in effort estimation.
Framework tracks evolving news stories across multiple sources.
problem Tracking evolving news stories across diverse sources and formats.
method Cross-domain story tracking approach using entity graphs and learning-to-rank.
result Outperforms state-of-the-art methods for real-time story tracking.
Crowd-powered system flags misinformation for fact checking.
problem Reduce the spread of fake news and misinformation on social media.
method Flexible temporal point process representation and scalable online algorithm Curb for optimal fact checking selection.
result Our scalable algorithm Curb can effectively reduce the spread of fake news and misinformation.
Proposes models to generate more interesting story endings.
problem Generating diverse and interesting story endings for a given context.
method Trains models to focus on keyphrases and promotes non-generic words.
result Models generate more diverse and interesting story endings.
This work recommends personalized search stories to users based on their interests.
problem Personalized search story recommendation within search engines.
method Deep reinforcement learning architecture trained by imitation learning and reinforcement learning.
result Empirically demonstrated effectiveness on real-world data sets.
Neural story generation gains common sense through targeted training.
problem Lack of common sense reasoning in neural-generated stories.
method Multi-task learning with auxiliary datasets for common sense grounding.
result Improved common sense reasoning and state-of-the-art perplexity.
Modeling true and false news diffusion in social networks using homogeneity.
problem Difficulties in distinguishing true from false news in social networks.
method Proposes a Bayesian nonparametric model that incorporates homogeneity of news stories to predict their genuineness.
result Homogeneity values of news stories strongly correlate with their genuineness and content.
Develops dual story for risk apportionment, interpreting preferences between lottery pairs.
problem Understanding preferences between different lottery pairs.
method Specifying model-free preferences towards nested classes of lottery pairs, developing dual story.
result Intuitive interpretation and full characterization of dual counterparts of prudence and temperance.
The paper tackles entity induction and reference in visual storytelling.
problem Coherence in visual storytelling through proper entity introduction and reference.
method Building an entity skeleton, using an encoder-decoder framework, and proposing a glocal hierarchical attention model.
result The proposed models outperform the baseline in automatic evaluation metrics and human preference.
We give a different perspective on the (by now) classic Basmajian identity, and point out some related results, both in the setting of hyperbolic manifolds, and in the polyhedral setting \emph{without} any group acting. In the new version we give more geometric and combinatorial applications of the main ideas.
Paper proposes a deep learning architecture for generating long stories from images.
problem Maintaining context in long event sequences for visual storytelling.
method Hierarchical deep learning architecture with encoder-decoder networks and natural language descriptions.
result Our method outperforms state-of-the-art techniques on automatic evaluation metrics.
The Lady Maisry ballads afford us a framework within which to segment a storyline into its major components. Segments and as a consequence nodal points are discussed for nine different variants of the Lady Maisry story of a (young) woman being burnt to death by her family, on account of her becoming pregnant by a forei…
Bitcoin fails to prove safe haven status during pandemic.
problem Determining if Bitcoin is a reliable safe haven asset during crises.
method Quantile correlations of Bitcoin with S&P500, VIX, and gold.
result Gold is a better safe haven during crises, not Bitcoin.
Generates coherent storybooks from plain text using diffusion models.
problem Ensuring coherency in a sequence of images for storytelling applications.
method Combines pre-trained LLM and text-guided Latent Diffusion Model for zero-shot generation.
result Outperforms state-of-the-art image editing baselines in generating coherent storybooks.
Models of bags of words typically assume topic mixing so that the words in a single bag come from a limited number of topics. We show here that many sets of bag of words exhibit a very different pattern of variation than the patterns that are efficiently captured by topic mixing. In many cases, from one bag of words to…
Study of polynomial strata using braid groups and translation surfaces.
problem Understanding the monodromy of polynomial strata.
method Using infinite-area translation surfaces and braid groups.
result Determine the monodromy of polynomial strata in the braid group.
Replication study shows Deep-SE still not as effective as previously thought for agile effort estimation.
problem Improving accuracy in estimating agile software development effort.
method Close replication of Deep-SE using additional data and comparison with multiple baselines.
result Deep-SE outperforms only a few cases, suggesting more work is needed.
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficie…
The study of geometric objects over Riemann surfaces using Dynkin diagrams.
problem Classifying geometric objects over Riemann surfaces.
method Developing a global theory of Lie groups over Riemann surfaces and using Dynkin diagrams for classification.
result A theory of Dynkin diagrams for classifying geometric objects.
Users can anticipate follower preferences by balancing feedback exploitation and exploration.
problem How users can anticipate their followers' preferences based on feedback.
method Theoretical analysis and practical algorithms for sequential decision making and utility maximization.
result Users need to balance exploitation and exploration to succeed in anticipating follower preferences.
A textbook on machine learning explaining patterns, predictions, and actions.
problem Understanding patterns in data for predictions and actions.
method Explains foundations of decision making, representation, optimization, and generalization.
result Equips readers with tools to reason about actions and their consequences.
New DNN architecture for indoor localization in multi-story buildings.
problem Scalable indoor localization in complex multi-story buildings.
method Stacked autoencoder and feed-forward classifier for multi-label classification.
result Near state-of-the-art performance with lower complexity and energy consumption.
Study on detecting change points in features for agnostic learning.
problem Detecting change points in features that become relevant over time.
method Proposed an approach to provably determine change points in an agnostic supervised learning setting.
result Efficient method with same asymptotic performance as original approach.
This paper explores using neural networks to improve Airbnb search performance.
problem Plateaued gains from gradient boosted decision tree model in search ranking.
method Applied neural networks to improve search performance.
result Shows elements useful in applying neural networks to a real-life product.
This thesis investigates belief propagation's performance in graphical models with loops.
problem Belief propagation's performance and convergence guarantees in models with loops are uncertain.
method Investigates how model parameters affect belief propagation's performance, convergence, and approximation quality.
result Model parameters influence the number of fixed points, convergence properties, and approximation quality of belief propagation.
Study shows stock prices influence news more than the other way around.
problem Understanding the interdependency between stock market and financial news.
method Time series analysis using five classification models.
result Stock prices have a greater impact on news contents than the other way around.
Study of knot polynomials for twist satellites, generalizing cabling.
problem Lifting decomposition rule to superpolynomials for positive and negative twistings.
method General decomposition of satellite's colored HOMFLY polynomial in terms of original knot's contributions.
result Knot polynomials for twist satellites are related to Vogel's universality.
Surveying isoparametric hypersurfaces in spheres, focusing on Élie Cartan's techniques.
problem Classifying isoparametric hypersurfaces in spheres.
method Historical survey and focus on Élie Cartan's techniques.
result Attention to Élie Cartan's techniques in the case of four principal curvatures.
Interactive storytelling connects documents with user constraints.
problem Creating coherent stories from diverse documents.
method Interactive constraints and distance measures based on topic distributions.
result Interactive storytelling outperforms existing methods on multiple datasets.
This paper and its sequel prove a generalization of the usual gluing theorem for two index 1 pseudoholomorphic curves u_+ and u_- in the symplectization of a contact 3-manifold. We assume that for each embedded Reeb orbit gamma, the total multiplicity of the negative ends of u_+ at covers of gamma agrees with the total…
New mSpinh-manifolds studied for their properties.
problem Understanding new manifold classifications.
method Exploring mSpinh-manifolds as a new category. result Highlights of mSpinh-manifolds discussed. This paper is an expansion of my lecture for David Epstein's birthday, which traced a logical progression from ideas of Euclid on subdividing polygons to some recent research on invariants of hyperbolic 3-manifolds. This `logical progression' makes a good story but distorts history a bit: the ultimate aims of the chara…
Tangle machines are a topologically inspired diagrammatic formalism to describe information flow in networks. This paper begins with an expository account of tangle machines motivated by the problem of describing `covariance intersection' fusion of Gaussian estimators in networks. It then gives two examples in which ta…
This paper constructs real algebraic maps that are topologically special generic maps.
problem Constructing smooth maps in differential topology and real algebraic geometry.
method Constructs real algebraic maps that are topologically special generic maps.
result Real algebraic maps are topologically special generic maps.
Unsupervised learning of viewpoints in news documents.
problem Learning different sides of stories in text documents.
method Applying CorrLDA2 to learn topic-viewpoint relations.
result Learned topic groups are contextually coherent and correctly associate with viewpoints.
New algorithms tackle multi-agent problems with hybrid action spaces.
problem Applying deep reinforcement learning to multi-agent problems with discrete-continuous hybrid action spaces.
method Proposed two novel algorithms: Deep MAPQN and Deep MAHHQN, using centralized training and decentralized execution.
result Empirical results show both algorithms significantly outperform existing methods.
4-manifolds from hyperbolic knots have curious metrics.
problem Understanding metrics on 4-manifolds from hyperbolic knot complements.
method Constructing 4-manifolds using hyperbolic knot complements.
result Curious metric properties of 4-manifolds constructed from hyperbolic knots.
The study investigates the consistency of k-means clustering under finite expectation assumptions.
problem Consistency of k-means clustering under finite expectation assumptions. method Investigates the conditions under which k-means clustering is consistent, considering finite expectation instead of finite variance. result Inconsistency can arise due to extreme cluster imbalance, leading to some clusters having few points.
Study of knotted defects in smectic liquid crystals using topological knot theory.
problem Understanding the topological structure of knotted defects in smectic liquid crystals.
method Investigation of screw and edge dislocations, focusing on their radial surface structure and knot fibration.
result Established a connection between smectic defects and knot theory, revealing the topological knotting of defects.
In this note we show that for any hyperbolic surface S, the number of geodesics of length bounded above by L in the mapping class group orbit of a fixed closed geodesic with a single double point is asymptotic to L raised to the dimension of the Teichmuller space of S. Since closed geodesics with one double point fall …
The study finds PK cone metrics on complex manifolds near hyperplane arrangements.
problem Finding metrics on complex manifolds near singularities.
method Analyzing flat torsion-free meromorphic connections on \(\mathbb{C}^n\) with simple poles at hyperplanes.
result Metric completion of certain connections yields PK cone metrics on \(\mathbb{C}^n\).
Proves super-version of index theorem from algebraic cobordism invariants.
problem Cobordism invariants in supersymmetric quantum mechanics.
method Trace methods for deformation quantization.
result Recovery of cobordism invariant using trace methods.
A machine learning surrogate model predicts earthquake-induced building responses.
problem Expensive FE model simulations for earthquake damage estimation.
method SVD-based earthquake characterization and machine learning model training.
result Deep neural network provides most accurate predictions of building responses.
An elementary introduction to Khovanov construction of superpolynomials. Despite its technical complexity, this method remains the only source of a definition of superpolynomials from the first principles and therefore is important for development and testing of alternative approaches. In this first part of the review …
AI models solved the Kaczmarz algorithm's worst-case complexity.
problem Finding the worst-case complexity of the Kaczmarz algorithm.
method Combining AI models to analyze the Kaczmarz algorithm's performance.
result Discovered the worst-case complexity of the Kaczmarz algorithm.
Article explores Thurston's circle packing theorem in 3-manifold geometry.
problem Understanding Thurston's circle packing theorem in 3-manifold geometry.
method Analyzes the Koebe-Andre'ev-Thurston Theorem and its relation to Thurston's circle packing theorem.
result Illustrates the significance of Thurston's circle packing theorem in 3-manifold geometry.
Visualizes board connections for socially responsible investing insights.
problem Understanding corporate governance and sustainability through board connections.
method Data Visualization tool to reveal connections between Directors and Executives.
result Strength of tool in investigating corporate governance and sustainability.
Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.