24 research outputs found
Analyzing Granger causality in climate data with time series classification methods
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
DualVAE: Controlling Colours of Generated and Real Images
Colour controlled image generation and manipulation are of interest to
artists and graphic designers. Vector Quantised Variational AutoEncoders
(VQ-VAEs) with autoregressive (AR) prior are able to produce high quality
images, but lack an explicit representation mechanism to control colour
attributes. We introduce DualVAE, a hybrid representation model that provides
such control by learning disentangled representations for colour and geometry.
The geometry is represented by an image intensity mapping that identifies
structural features. The disentangled representation is obtained by two novel
mechanisms:
(i) a dual branch architecture that separates image colour attributes from
geometric attributes, and (ii) a new ELBO that trains the combined colour and
geometry representations. DualVAE can control the colour of generated images,
and recolour existing images by transferring the colour latent representation
obtained from an exemplar image. We demonstrate that DualVAE generates images
with FID nearly two times better than VQ-GAN on a diverse collection of
datasets, including animated faces, logos and artistic landscapes
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge
Large scale Vision-Language (VL) models have shown tremendous success in
aligning representations between visual and text modalities. This enables
remarkable progress in zero-shot recognition, image generation & editing, and
many other exciting tasks. However, VL models tend to over-represent objects
while paying much less attention to verbs, and require additional tuning on
video data for best zero-shot action recognition performance. While previous
work relied on large-scale, fully-annotated data, in this work we propose an
unsupervised approach. We adapt a VL model for zero-shot and few-shot action
recognition using a collection of unlabeled videos and an unpaired action
dictionary. Based on that, we leverage Large Language Models and VL models to
build a text bag for each unlabeled video via matching, text expansion and
captioning. We use those bags in a Multiple Instance Learning setup to adapt an
image-text backbone to video data. Although finetuned on unlabeled video data,
our resulting models demonstrate high transferability to numerous unseen
zero-shot downstream tasks, improving the base VL model performance by up to
14\%, and even comparing favorably to fully-supervised baselines in both
zero-shot and few-shot video recognition transfer. The code will be released
later at \url{https://github.com/wlin-at/MAXI}.Comment: Accepted at ICCV 202
Temporal graph mining and distributed processing
With the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamics of the network. Understanding the dynamics of the network is very important because it encapsulates the way we communicate, socialize, consume information and get influenced. To this end, in this PhD thesis, we focus on analyzing an interaction network to understand how the underlying network is being used. We define interaction network as a sequence of time-stamped interactions E over edges of a static graph G=(V, E). Interaction networks can be used to model many real-world networks for example, in a social network or a communication network, each interaction over an edge represents an interaction between two users, e.g., emailing, making a call, re-tweeting, or in case of the financial network an interaction between two accounts to represent a transaction.
We analyze interaction network under two settings. In the first setting, we study interaction network under a sliding window model. We assume a node could pass information to other nodes if they are connected to them using edges present in a time window. In this model, we study how the importance or centrality of a node evolves over time. In the second setting, we put additional constraints on how information flows between nodes. We assume a node could pass information to other nodes only if there is a temporal path between them. To restrict the length of the temporal paths we consider a time window in this approach as well. We apply this model to solve the time-constrained influence maximization problem. By analyzing the interaction network data under our model we find the top-k most influential nodes. We test our model both on human-human interaction using social network data as well as on location-location interaction using location-based social network(LBSNs) data. In the same setting, we also mine temporal cyclic paths to understand the communication patterns in a network. Temporal cycles have many applications and appear naturally in communication networks where one person posts a message and after a while reacts to a thread of reactions from peers on the post. In financial networks, on the other hand, the presence of a temporal cycle could be indicative of certain types of fraud. We provide efficient algorithms for all our analysis and test their efficiency and effectiveness on real-world data.
Finally, given that many of the algorithms we study have huge computational demands, we also studied distributed graph processing algorithms. An important aspect of distributed graph processing is to correctly partition the graph data between different machine. A lot of research has been done on efficient graph partitioning strategies but there is no one good partitioning strategy for all kind of graphs and algorithms. Choosing the best partitioning strategy is nontrivial and is mostly a trial and error exercise. To address this problem we provide a cost model based approach to give a better understanding of how a given partitioning strategy is performing for a given graph and algorithm.Con el reciente crecimiento de las redes sociales y el deseo humano de interactuar con el mundo digital, una gran cantidad de datos de interacci贸n humano-a-humano o humano-a-dispositivo se generan cada segundo. Con el auge de los dispositivos IoT, las interacciones dispositivo-a-dispositivo tambi茅n est谩n en alza. Todas estas interacciones no son m谩s que una representaci贸n de como la red subyacente conecta distintas entidades en el tiempo. Modelar estas interacciones en forma de red de interacciones presenta una gran cantidad de oportunidades 煤nicas para descubrir patrones interesantes y entender la dinamicidad de la red. Entender la dinamicidad de la red es clave ya que encapsula la forma en la que nos comunicamos, socializamos, consumimos informaci贸n y somos influenciados. Para ello, en esta tesis doctoral, nos centramos en analizar una red de interacciones para entender como la red subyacente es usada. Definimos una red de interacciones como una sequencia de interacciones grabadas en el tiempo E sobre aristas de un grafo est谩tico G=(V, E). Las redes de interacci贸n se pueden usar para modelar gran cantidad de aplicaciones reales, por ejemplo en una red social o de comunicaciones cada interacci贸n sobre una arista representa una interacci贸n entre dos usuarios (correo electr贸nico, llamada, retweet), o en el caso de una red financiera una interacci贸n entre dos cuentas para representar una transacci贸n. Analizamos las redes de interacci贸n bajo m煤ltiples escenarios. En el primero, estudiamos las redes de interacci贸n bajo un modelo de ventana deslizante. Asumimos que un nodo puede mandar informaci贸n a otros nodos si estan conectados utilizando aristas presentes en una ventana temporal. En este modelo, estudiamos como la importancia o centralidad de un nodo evoluciona en el tiempo. En el segundo escenario a帽adimos restricciones adicionales respecto como la informaci贸n fluye entre nodos. Asumimos que un nodo puede mandar informaci贸n a otros nodos solo si existe un camino temporal entre ellos. Para restringir la longitud de los caminos temporales tambi茅n asumimos una ventana temporal. Aplicamos este modelo para resolver este problema de maximizaci贸n de influencia restringido temporalmente. Analizando los datos de la red de interacci贸n bajo nuestro modelo intentamos descubrir los k nodos m谩s influyentes. Examinamos nuestro modelo en interacciones humano-a-humano, usando datos de redes sociales, como en ubicaci贸n-a-ubicaci贸n usando datos de redes sociales basades en localizaci贸n (LBSNs). En el mismo escenario tambi茅n minamos cam铆nos c铆clicos temporales para entender los patrones de comunicaci贸n en una red. Existen m煤ltiples aplicaciones para c铆clos temporales y aparecen naturalmente en redes de comunicaci贸n donde una persona env铆a un mensaje y despu茅s de un tiempo reacciona a una cadena de reacciones de compa帽eros en el mensaje. En redes financieras, por otro lado, la presencia de un ciclo temporal puede indicar ciertos tipos de fraude. Proponemos algoritmos eficientes para todos nuestros an谩lisis y evaluamos su eficiencia y efectividad en datos reales. Finalmente, dado que muchos de los algoritmos estudiados tienen una gran demanda computacional, tambi茅n estudiamos los algoritmos de procesado distribuido de grafos. Un aspecto importante de procesado distribuido de grafos es el de correctamente particionar los datos del grafo entre distintas m谩quinas. Gran cantidad de investigaci贸n se ha realizado en estrategias para particionar eficientemente un grafo, pero no existe un particionamento bueno para todos los tipos de grafos y algoritmos. Escoger la mejor estrategia de partici贸n no es trivial y es mayoritariamente un ejercicio de prueba y error. Con tal de abordar este problema, proporcionamos un modelo de costes para dar un mejor entendimiento en como una estrategia de particionamiento act煤a dado un grafo y un algoritmo
Temporal graph mining and distributed processing
Cotutela Universitat Polit猫cnica de Catalunya i Universit茅 Libre de BruxellesWith the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamics of the network. Understanding the dynamics of the network is very important because it encapsulates the way we communicate, socialize, consume information and get influenced. To this end, in this PhD thesis, we focus on analyzing an interaction network to understand how the underlying network is being used. We define interaction network as a sequence of time-stamped interactions E over edges of a static graph G=(V, E). Interaction networks can be used to model many real-world networks for example, in a social network or a communication network, each interaction over an edge represents an interaction between two users, e.g., emailing, making a call, re-tweeting, or in case of the financial network an interaction between two accounts to represent a transaction.
We analyze interaction network under two settings. In the first setting, we study interaction network under a sliding window model. We assume a node could pass information to other nodes if they are connected to them using edges present in a time window. In this model, we study how the importance or centrality of a node evolves over time. In the second setting, we put additional constraints on how information flows between nodes. We assume a node could pass information to other nodes only if there is a temporal path between them. To restrict the length of the temporal paths we consider a time window in this approach as well. We apply this model to solve the time-constrained influence maximization problem. By analyzing the interaction network data under our model we find the top-k most influential nodes. We test our model both on human-human interaction using social network data as well as on location-location interaction using location-based social network(LBSNs) data. In the same setting, we also mine temporal cyclic paths to understand the communication patterns in a network. Temporal cycles have many applications and appear naturally in communication networks where one person posts a message and after a while reacts to a thread of reactions from peers on the post. In financial networks, on the other hand, the presence of a temporal cycle could be indicative of certain types of fraud. We provide efficient algorithms for all our analysis and test their efficiency and effectiveness on real-world data.
Finally, given that many of the algorithms we study have huge computational demands, we also studied distributed graph processing algorithms. An important aspect of distributed graph processing is to correctly partition the graph data between different machine. A lot of research has been done on efficient graph partitioning strategies but there is no one good partitioning strategy for all kind of graphs and algorithms. Choosing the best partitioning strategy is nontrivial and is mostly a trial and error exercise. To address this problem we provide a cost model based approach to give a better understanding of how a given partitioning strategy is performing for a given graph and algorithm.Con el reciente crecimiento de las redes sociales y el deseo humano de interactuar con el mundo digital, una gran cantidad de datos de interacci贸n humano-a-humano o humano-a-dispositivo se generan cada segundo. Con el auge de los dispositivos IoT, las interacciones dispositivo-a-dispositivo tambi茅n est谩n en alza. Todas estas interacciones no son m谩s que una representaci贸n de como la red subyacente conecta distintas entidades en el tiempo. Modelar estas interacciones en forma de red de interacciones presenta una gran cantidad de oportunidades 煤nicas para descubrir patrones interesantes y entender la dinamicidad de la red. Entender la dinamicidad de la red es clave ya que encapsula la forma en la que nos comunicamos, socializamos, consumimos informaci贸n y somos influenciados. Para ello, en esta tesis doctoral, nos centramos en analizar una red de interacciones para entender como la red subyacente es usada. Definimos una red de interacciones como una sequencia de interacciones grabadas en el tiempo E sobre aristas de un grafo est谩tico G=(V, E). Las redes de interacci贸n se pueden usar para modelar gran cantidad de aplicaciones reales, por ejemplo en una red social o de comunicaciones cada interacci贸n sobre una arista representa una interacci贸n entre dos usuarios (correo electr贸nico, llamada, retweet), o en el caso de una red financiera una interacci贸n entre dos cuentas para representar una transacci贸n. Analizamos las redes de interacci贸n bajo m煤ltiples escenarios. En el primero, estudiamos las redes de interacci贸n bajo un modelo de ventana deslizante. Asumimos que un nodo puede mandar informaci贸n a otros nodos si estan conectados utilizando aristas presentes en una ventana temporal. En este modelo, estudiamos como la importancia o centralidad de un nodo evoluciona en el tiempo. En el segundo escenario a帽adimos restricciones adicionales respecto como la informaci贸n fluye entre nodos. Asumimos que un nodo puede mandar informaci贸n a otros nodos solo si existe un camino temporal entre ellos. Para restringir la longitud de los caminos temporales tambi茅n asumimos una ventana temporal. Aplicamos este modelo para resolver este problema de maximizaci贸n de influencia restringido temporalmente. Analizando los datos de la red de interacci贸n bajo nuestro modelo intentamos descubrir los k nodos m谩s influyentes. Examinamos nuestro modelo en interacciones humano-a-humano, usando datos de redes sociales, como en ubicaci贸n-a-ubicaci贸n usando datos de redes sociales basades en localizaci贸n (LBSNs). En el mismo escenario tambi茅n minamos cam铆nos c铆clicos temporales para entender los patrones de comunicaci贸n en una red. Existen m煤ltiples aplicaciones para c铆clos temporales y aparecen naturalmente en redes de comunicaci贸n donde una persona env铆a un mensaje y despu茅s de un tiempo reacciona a una cadena de reacciones de compa帽eros en el mensaje. En redes financieras, por otro lado, la presencia de un ciclo temporal puede indicar ciertos tipos de fraude. Proponemos algoritmos eficientes para todos nuestros an谩lisis y evaluamos su eficiencia y efectividad en datos reales. Finalmente, dado que muchos de los algoritmos estudiados tienen una gran demanda computacional, tambi茅n estudiamos los algoritmos de procesado distribuido de grafos. Un aspecto importante de procesado distribuido de grafos es el de correctamente particionar los datos del grafo entre distintas m谩quinas. Gran cantidad de investigaci贸n se ha realizado en estrategias para particionar eficientemente un grafo, pero no existe un particionamento bueno para todos los tipos de grafos y algoritmos. Escoger la mejor estrategia de partici贸n no es trivial y es mayoritariamente un ejercicio de prueba y error. Con tal de abordar este problema, proporcionamos un modelo de costes para dar un mejor entendimiento en como una estrategia de particionamiento act煤a dado un grafo y un algoritmo.Postprint (published version
Frequency Domain-based Dataset Distillation
This paper presents FreD, a novel parameterization method for dataset
distillation, which utilizes the frequency domain to distill a small-sized
synthetic dataset from a large-sized original dataset. Unlike conventional
approaches that focus on the spatial domain, FreD employs frequency-based
transforms to optimize the frequency representations of each data instance. By
leveraging the concentration of spatial domain information on specific
frequency components, FreD intelligently selects a subset of frequency
dimensions for optimization, leading to a significant reduction in the required
budget for synthesizing an instance. Through the selection of frequency
dimensions based on the explained variance, FreD demonstrates both theoretical
and empirical evidence of its ability to operate efficiently within a limited
budget, while better preserving the information of the original dataset
compared to conventional parameterization methods. Furthermore, based on the
orthogonal compatibility of FreD with existing methods, we confirm that FreD
consistently improves the performances of existing distillation methods over
the evaluation scenarios with different benchmark datasets. We release the code
at https://github.com/sdh0818/FreD.Comment: Accepted at NeurIPS 202
Hierarchical Classification of Research Fields in the "Web of Science" Using Deep Learning
This paper presents a hierarchical classification system that automatically
categorizes a scholarly publication using its abstract into a three-tier
hierarchical label set (discipline, field, subfield) in a multi-class setting.
This system enables a holistic categorization of research activities in the
mentioned hierarchy in terms of knowledge production through articles and
impact through citations, permitting those activities to fall into multiple
categories. The classification system distinguishes 44 disciplines, 718 fields
and 1,485 subfields among 160 million abstract snippets in Microsoft Academic
Graph (version 2018-05-17). We used batch training in a modularized and
distributed fashion to address and allow for interdisciplinary and interfield
classifications in single-label and multi-label settings. In total, we have
conducted 3,140 experiments in all considered models (Convolutional Neural
Networks, Recurrent Neural Networks, Transformers). The classification accuracy
is > 90% in 77.13% and 78.19% of the single-label and multi-label
classifications, respectively. We examine the advantages of our classification
by its ability to better align research texts and output with disciplines, to
adequately classify them in an automated way, and to capture the degree of
interdisciplinarity. The proposed system (a set of pre-trained models) can
serve as a backbone to an interactive system for indexing scientific
publications in the future.Comment: Under review in QS