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.
New model for multivariate discrete event data with flexible interactions.
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventiv…
Proposes online learning for Hawkes processes with network structure and event interaction.
SurvFD and SurvSHAP-IQ provide interpretable survival models by analyzing feature interactions.
A new method for embedding sparse high-order interactions.
We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches that assume fully observable interactions, here we consider a scenario where certain interaction events lack information about participants. Ins…
Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process. In many cases, such events can be described as a collection of categorical values that are considered as entities of different types, which we call heterogeneous categor…
A new metric space model for point process excitations uncovers hidden interactions.
Sequential modelling with self-attention has achieved cutting edge performances in natural language processing. With advantages in model flexibility, computation complexity and interpretability, self-attention is gradually becoming a key component in event sequence models. However, like most other sequence models, self…
We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country took action toward country at time ." BPTD discovers overlapping country--community memberships, including the number of latent com…
PHASE dataset simulates complex social interactions in physical environments.
Modeling solar ramping events with spatio-temporal point processes.
stCEG models spatial events using Chain Event Graphs in R.
Agent-based model simulates market dynamics with real-time order matching.
Survey on modeling event sequences through temporal processes.
We study large deviations and rare default clustering events in a dynamic large heterogeneous portfolio of interconnected components. Defaults come as Poisson events and the default intensities of the different components in the system interact through the empirical default rate and via systematic effects that are comm…
With the help of transfer entropy, we analyze information flows between communities of complex networks. We show that the transfer entropy provides a coherent description of interactions between communities, including non-linear interactions. To put some flesh on the bare bones, we analyze transfer entropies between co…
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resul…
Modeling cascading behavior in complex systems using CTBNs.
Paper uncovers causal structures in Hawkes processes with latent subprocesses.
Proposes OC4Seq for detecting anomalies in discrete event sequences.
New benchmark for causal reasoning from human video descriptions.
Study analyzes European energy markets' reactions to 2022 events using Bayesian methods.
CNN improves neutrino event reconstruction in IceCube DeepCore.
Versatile model for High Energy Physics events.
AI classifies tourist events for better service.
Paper shows minimum observation time for network recovery.
Method identifies dual cognitive system interactions using eye-tracking data.
The logic of uncertainty is not the logic of experience and as well as it is not the logic of chance. It is the logic of experience and chance. Experience and chance are two inseparable poles. These are two dual reflections of one essence, which is called co~event. The theory of experience and chance is the theory of c…
Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user…
Novel method uses information theory to measure causal influences during transient neural events.
Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales usually occur simultaneously, namely, they span different intervals in time and space…
We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network. Among other models, the susceptible, infected, recovered () model for interaction events fal…
We introduce a class of hybrid marked point processes, which encompasses and extends continuous-time Markov chains and Hawkes processes. While this flexible class amalgamates such existing processes, it also contains novel processes with complex dynamics. These processes are defined implicitly via their intensity and a…
Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detecting Dark Matter. Using the likelihood-free framework, a new algorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presente…
xVAE models extreme turbulence events in turbulent flows.
We propose a novel class of network models for temporal dyadic interaction data. Our goal is to capture a number of important features often observed in social interactions: sparsity, degree heterogeneity, community structure and reciprocity. We propose a family of models based on self-exciting Hawkes point processes i…
The paper proposes a method to analyze categorical feature interactions in large datasets using graph covariance and LLMs.
Smooth handling of pedestrian interactions is a key requirement for Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS). Such systems call for early and accurate prediction of a pedestrian's crossing/not-crossing behaviour in front of the vehicle. Existing approaches to pedestrian behaviour predictio…
New model captures time and mark inter-dependence in TPPs.
Novel framework for spatio-temporal event analysis using Hawkes processes.
New algorithm predicts spatio-temporal events with improved accuracy.
Paper introduces a novel point process model for graph data using GNNs.
Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the availability of large-scale data from social networks and sensor networks offers an…
Cascading chains of events are a salient feature of many real-world social, biological, and financial networks. In social networks, social reciprocity accounts for retaliations in gang interactions, proxy wars in nation-state conflicts, or Internet memes shared via social media. Neuron spikes stimulate or inhibit spike…
We model GitHub interactions as a temporal knowledge graph for software engineering questions.
Develops a two-level monotonic multistage recommender system for better user-specific prediction.
CBNNs model survival with time-varying interactions, outperforming other methods.