Telecomunicaciones, información y comunicación

An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs

- Relevance and utility are two frequently used measures to evaluate the effectiveness of an information retrieval (IR) system. Relevance emphasizes the aboutness of a result to a query, while utility...

Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management

- Intensity control is a class of continuous-time dynamic optimization problems with many important applications in Operations Research including queueing and revenue management. In this study, we...

Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry

- Reconstructing accurate surfaces from sparse multi-view images remains challenging due to severe geometric ambiguity and occlusions. Existing generalizable neural surface reconstruction methods...

Language Reconstruction with Brain Predictive Coding from fMRI Data

- Many recent studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous language. However, there is a lack of neurological basis...

TetraBFT: Reducing Latency of Unauthenticated, Responsive BFT Consensus

- This paper presents TetraBFT, a novel unauthenticated Byzantine fault tolerant protocol for solving consensus in partial synchrony, eliminating the need for public key cryptography and ensuring...

On Propositional Dynamic Logic and Concurrency

- Dynamic logic is a powerful approach to reasoning about programs and their executions, obtained by extending classical logic with modalities that can express program executions as formulas. However,...

GaNI: Global and Near Field Illumination Aware Neural Inverse Rendering

- In this paper, we present GaNI, a Global and Near-field Illumination-aware neural inverse rendering technique that can reconstruct geometry, albedo, and roughness parameters from images of a scene...

Attacks Meet Interpretability (AmI) Evaluation and Findings

- To investigate the effectiveness of the model explanation in detecting adversarial examples, we reproduce the results of two papers, Attacks Meet Interpretability: Attribute-steered Detection of...

CROP: Conservative Reward for Model-based Offline Policy Optimization

- Offline reinforcement learning (RL) aims to optimize a policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing offline RL challenges...

A Survey on Deep Learning Techniques for Action Anticipation

- The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numerous methods have...

A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network

- Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional workload bursts, which makes workload prediction...

Incentivizing Honesty among Competitors in Collaborative Learning and Optimization

- Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single entity's data. However, in many cases, potential...

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation

- Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their local and uniform...

Detection of Anomalous Network Nodes via Hierarchical Prediction and Extreme Value Theory

- Continuously evolving cyber-attacks against industrial networks reduce the effectiveness of signature-based detection methods. Once malware has infiltrated a network (for example, entering via an...

M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

- Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different...

scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation

- In this paper, we propose a scalable approximate multiplier design, scaleTRIM, that approximates the multiplication operation using fitted linear functions, also referred to as linearization. We show...

Privacy Against Agnostic Inference Attacks in Vertical Federated Learning

- A novel form of inference attack in vertical federated learning (VFL) is proposed, where two parties collaborate in training a machine learning (ML) model. Logistic regression is considered for the...

Computational performance of the MMOC in the inverse design of the Doswell frontogenesis equation

- Inverse design of transport equations can be addressed by using a gradient-adjoint methodology. In this methodology numerical schemes used for the adjoint resolution determine the direction of...

Networks of Moore Machines

- A product of Moore machines with feedback published in 1962 by Juris Hartmanis and a class of primitive recursive functions on finite sequences published in a textbook by Roza Peter originally in...

The Wasserstein transform

- We introduce the Wasserstein Transform (WT), a general unsupervised framework for updating distance structures on given data sets with the purpose of enhancing features and denoising. Our framework...

Universality of first-order methods on random and deterministic matrices

- General first-order methods (GFOM) are a flexible class of iterative algorithms which update a state vector by matrix-vector multiplications and entrywise nonlinearities. A long line of work has...