Blog
Hexcute: A Tile-based Programming Language with Automatic Layout and Task-Mapping Synthesis
Apprendimento profondo (DL) workloads mainly run on accelerators like GPUs. Recent DL quantization techniques demand a new matrix multiplication operator with mixed input data types, further complicating GPU optimization. Prior high-level compilers like Triton lack the expressiveness to implement key optimizations like...
Identifying Process Improvement Opportunities through Process Execution Benchmarking
Benchmarking functionalities in current commercial process mining tools allow organizations to contextualize their process performance through high-level performance indicators, such as completion rate or throughput time. Tuttavia, they do not suggest any measures to close potential performance gaps. To address this limitation,...
Cohort Revenue & Retention Analysis: A Bayesian Approach
We present a Bayesian approach to model cohort-level retention rates and revenue over time. We use Bayesian additive regression trees (BART) to model the retention component which we couple with a linear model for the revenue component. This method is flexible enough...
ReGraph: A Tool for Binary Similarity Identification
Binary Code Similarity Detection (BCSD) is not only essential for security tasks such as vulnerability identification but also for code copying detection, yet it remains challenging due to binary stripping and diverse compilation environments. Existing methods tend to adopt increasingly complex neural...
Mass-Adaptive Admittance Control for Robotic Manipulators
Handling objects with unknown or changing masses is a common challenge in robotics, often leading to errors or instability if the control system cannot adapt in real-time. In questo documento, we present a novel approach that enables a six-degrees-of-freedom robotic manipulator to...
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security
Edge computing-based Next-Generation Wireless Networks (NGWN)-IoT offer enhanced bandwidth capacity for large-scale service provisioning but remain vulnerable to evolving cyber threats. Existing intrusion detection and prevention methods provide limited security as adversaries continually adapt their attack strategies. We propose a dynamic attack...
Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligence
Federated learning has become a promising distributed learning concept with extra insurance on data privacy. Extensive studies on various models of Federated learning have been done since the coinage of its term. One of the important derivatives of federated learning is hierarchical...
Fast, Space-Optimal Streaming Algorithms for Clustering and Subspace Embeddings
We show that both clustering and subspace embeddings can be performed in the streaming model with the same asymptotic efficiency as in the central/offline setting. Per $(k, z)$-clustering in the streaming model, we achieve a number of words of memory which is...
Robust Causal Inference for EHR-based Studies of Point Exposures with Missingness in Eligibility Criteria
Missingness in variables that define study eligibility criteria is a seldom addressed challenge in electronic health record (EHR)-based settings. It is typically the case that patients with incomplete eligibility information are excluded from analysis without consideration of (implicit) assumptions that are being...
Algebra tensore quasitubale su spazi di Hilbert separabili
The tubal tensor framework provides a clean and effective algebraic setting for tensor computations, supporting matrix-mimetic features like Singular Value Decomposition and Eckart-Young-like optimality results. Underlying the tubal tensor framework is a view of a tensor as a matrix of finite sized...