Bio-inspired balance controller for a humanoid robot

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Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

Humans use tools to complete impact-aware tasks, such as hammering a nail or playing tennis. The postures adopted to use these tools can significantly influence the performance of these tasks, where the force or velocity of the hand holding a tool plays a ...

Compared with traditional in-pipe robots, tensegrity robots have exhibited many advantages such as light-weight, compliant, collapsible, low-cost, and rapidly manufacturable characteristics. However, published tensegrity in-pipe robots still have limited l ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of O(ϵ-2) to achieve an ϵ-stationary solut ...

Social insects, such as ants, termites, and honeybees, have evolved sophisticated societies where collaboration and division of labor enhance survival of the whole colony, and are thus considered “superorganisms”. Historically, studying behaviors involving ...

As the demand for high channel counts and high-resolution recordings of neural activity continues to grow, the increased power and data rate generated impose hard constraints on the telemetry capabilities of wireless implantable neural interfaces. To addre ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations by integrating physical laws into the learning process. This work leverages PINNs to simulate gravitational collapse, a critical phenomenon in astro ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

The Birman-Williams theorem gives a connection between the collection of unstable periodic orbits (UPOs) contained within a chaotic attractor and the topology of that attractor, for three-dimensional systems. In certain cases, the fractal dimension of a ch ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

The first two chapters develop ML-based models that generate conditional SDF estimates efficiently using conditioning information sets, resulting in minimal pricing errors and superior Sharpe ratios compared to traditional conditional asset pricing models. ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

Prediction is a vital component of motion planning for autonomous vehicles (AVs). By reasoning about the possible behavior of other target agents, the ego vehicle (EV) can navigate safely, efficiently, and politely. However, most of the existing work overl ...

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability include heuristics-based metrics or retrosynthesis models which predict a synthetic pathway. By contrast, an explicit approach anchors ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Bio-inspired balance controller for a humanoid robot

Chat with Graph Search

Image steganalysis using active learning and hyperparameter optimization

An Optimal Control Formulation of Tool Affordance Applied to Impact Tasks

SUTBot: A Soft Umbrella-like Tensegrity Robot with Elastic Struts for In-pipe Locomotion

Robust NAS under adversarial training: benchmark, theory, and beyond

Diffusion Stochastic Optimization for Min-Max Problems

Biohybrid Superorganisms—On the Design of a Robotic System for Thermal Interactions With Honeybee Colonies

A Reconfigurable High-Dynamic Range ∆Σ Front-End with Event-Based Decimation for Bandwidth-Efficient Implantable Neural Interfaces

Topics in statistical physics of high-dimensional machine learning

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

The topology of a chaotic attractor in the Kuramoto-Sivashinsky equation

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Machine Learning Applications to Conditional Asset Pricing: Attention, CNNs, and Neural Tangent Kernels

Incorporating Projective Geometry into Deep Learning

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs