A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved scaffold generation for protein motifs using SE(3) flow matching.
New method learns diverse protein scaffolds for motif design.
New method uses diffusion models to generate proteins with specific motifs.
Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules retaining a particular scaffold as a substructure. On this account, our present work proposes a scaff…
We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components. We explain how some of the existing ideas on runtime monitors for perception systems can be seen as a specific instance of logical scaffolds. Furthermore, we describe how logical scaffolds ma…
Paper analyzes Scaffold algorithm for federated learning, proving linear speed-up with stochastic gradients.
Scaffolding sets improve predictor correctness across subsets.
Method finds motifs in knowledge graphs, revealing their structure.
MotiFiesta learns network motifs efficiently.
Federated Averaging (FedAvg) has emerged as the algorithm of choice for federated learning due to its simplicity and low communication cost. However, in spite of recent research efforts, its performance is not fully understood. We obtain tight convergence rates for FedAvg and prove that it suffers from `client-drift' w…
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. Small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of…
Paper proposes DTW-SOM for visual exploration of time-series motifs.
Researchers create exact minimal surfaces with helical motifs in biological structures.
New clustering methods use motifs to organize networks.
Behaviors of several laboratory animals can be modeled as sequences of stereotyped behaviors, or behavioral motifs. However, identifying such motifs is a challenging problem. Behaviors have a multi-scale structure: the animal can be simultaneously performing a small-scale motif and a large-scale one (e.g. \textit{chewi…
MDF represents time series motifs as images for improved classification.
odeN efficiently approximates multiple temporal motifs in large networks.
Complex systems, such as airplanes, cars, or financial markets, produce multivariate time series data consisting of a large number of system measurements over a period of time. Such data can be interpreted as a sequence of states, where each state represents a prototype of system behavior. An important problem in this …
Synthetic Petri Dish predicts neural architecture performance faster.
Paper discovers manoeuvres from vehicle telematics data.
A motif-based framework identifies local spillover structures in financial markets.
Generative graph models create instances of graphs that mimic the properties of real-world networks. Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity patterns known as network motifs. Different types of graphs contain diff…
TDS provides exact samples for conditional distributions in diffusion models.
PGEL learns embeddings to diversify protein motifs while maintaining biological function.
Model improves robustness of neural network sequences without transition failures.
New method clusters weighted directed networks using motifs.
New method discovers time series motifs under DTW, significantly reducing computations.
Proposes a motif-preserving Graph Neural Network for financial default prediction.
In this paper, we introduce the notion of motif closure and describe higher-order ranking and link prediction methods based on the notion of closing higher-order network motifs. The methods are fast and efficient for real-time ranking and link prediction-based applications such as web search, online advertising, and re…
Generalized belief propagation converges to optimal solutions on graphs with motifs.
Time Series Motif Discovery (TSMD) is defined as searching for patterns that are previously unknown and appear with a given frequency in time series. Another problem strongly related with TSMD is Word Segmentation. This problem has received much attention from the community that studies early language acquisition in ba…
Self-taught optimizer improves code generation using language models.
Paper constructs motifs from planar tilings for DP weaves and polycatenanes.
The paper models musical motif transformations in Beethoven's works.
TF-MoDISco (Transcription Factor Motif Discovery from Importance Scores) is an algorithm for identifying motifs from basepair-level importance scores computed on genomic sequence data. This technical note focuses on version v0.5.6.5. The implementation is available at https://github.com/kundajelab/tfmodisco/tree/v0.5.6…
New model generates larger molecules more effectively.
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning ov…
The discovery of time series motifs has emerged as one of the most useful primitives in time series data mining. Researchers have shown its utility for exploratory data mining, summarization, visualization, segmentation, classification, clustering, and rule discovery. Although there has been more than a decade of exten…
When each data point is a large graph, graph statistics such as densities of certain subgraphs (motifs) can be used as feature vectors for machine learning. While intuitive, motif counts are expensive to compute and difficult to work with theoretically. Via graphon theory, we give an explicit quantitative bound for the…
New approach learns latent motifs in networks for mesoscale structure analysis.
Co-Diffusion predicts drug-target affinity by learning latent manifolds and diffusion, improving generalization.
KCoreMotif clusters large networks efficiently by exploiting k-core decomposition and motifs.
As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic error…
The -skeleton of the canonical cubulation of into unit cubes is called the {\it canonical scaffolding} . In this paper, we prove that any smooth, compact, closed, -dimensional submanifold of with trivial normal bundle can be continuously isotoped by an amb…
In this paper we study topological surfaces as gridded surfaces in the 2-dimensional scaffolding of cubic honeycombs in Euclidean and hyperbolic spaces.
We study soft persistence (existence in subsequent temporal layers of motifs from the initial layer) of motif structures in Triangulated Maximally Filtered Graphs (TMFG) generated from time-varying Kendall correlation matrices computed from stock prices log-returns over rolling windows with exponential smoothing. We ob…
This paper classifies periodic weaves and their universal cover, extending Tait's conjectures.
Paper defines and analyzes mathematical equivalence of periodic tangles.