Vision Transformer Adapters for Generalizable Multitask Learning

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The increase in the number of cores in processors has been an important trend over the past decade. In order to be able to efficiently use such architectures, modern software must be scalable: performance should increase proportionally to the number of all ...

We present an end-to-end trainable Neural Network architecture for stereo imaging that jointly locates and estimates human body poses in 3D. Our method defines a 2D pose for each human in a stereo pair of images and uses a correlation layer with a composit ...

Recent trends of incorporating attention mechanisms in vision have led re- searchers to reconsider the supremacy of convolutional layers as a primary build- ing block. Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed ...

Transformers have been proven a successful model for a variety of tasks in sequence modeling. However, computing the attention matrix, which is their key component, has quadratic complexity with respect to the sequence length, thus making them prohibitivel ...

For several decades, online transaction processing (OLTP) has been one of the main server applications that drives innovations in the data management ecosystem, and in turn the database and computer architecture communities. Recent hardware trends oblige s ...

A fully-labelled dataset of Arabic Sign Language (ArSL) images is developed for research related to sign language recognition. The dataset will provide researcher the opportunity to investigate and develop automated systems for the deaf and hard of hearing ...

The common "one size does not fit all" paradigm isolates transactional and analytical workloads into separate, specialized database systems. Operational data is periodically replicated to a data warehouse for analytics. Competitiveness of enterprises today ...

Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networks that were initially trained on ImageNet, mostly because the learned featu ...

As data analytics is used by an increasing number of applications, data analytics engines are required to execute workloads with increased concurrency, i.e., an increasing number of clients submitting queries. Data management systems designed for data anal ...

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences—motifs that are visually similar across different acquisitions—to infer changes on the parame ...

Today's data deluge enables organizations to collect massive data, and analyze it with an ever-increasing number of concurrent queries. Traditional data warehouses (DW) face a challenging problem in executing this task, due to their query-centric model: ea ...

In this work, we study the use of attention mechanisms to enhance the performance of the state-of-the-art deep learning model in Speech Emotion Recognition (SER). We introduce a new Long Short-Term Memory (LSTM)-based neural network attention model which i ...

The Transaction abstraction is arguably one of the most appealing middleware paradigms. It lies typically between the programmer of a concurrent or distributed application on the one hand, and the operating system with the underlying network on the other h ...

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we ...

Transactional memory (TM) has shown potential to simplify the task of writing concurrent programs. TM shifts the burden of managing concurrency from the programmer to the TM algorithm. The correctness of TM algorithms is generally proved manually. The goal ...

There is a trend towards increased specialization of data management software for performance reasons. The improved performance not only leads to a more efficient usage of the underlying hardware and cuts the operation costs of the system, but also is a ga ...

Model checking transactional memories (TMs) is difficult because of the unbounded number, length, and delay of concurrent transactions, as well as the unbounded size of the memory. We show that, under certain conditions satisfied by most TMs we know of, th ...

Establishing the scalability of a concurrent algorithm a priori, before implementing and evaluating it on a concrete multi-core platform, seems difficult, if not impossible. In the context of search data structures however, according to all practical work ...

Vision Transformer Adapters for Generalizable Multitask Learning

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Universally Scalable Concurrent Data Structures

Joint Human Pose Estimation and Stereo 3D Localization

On the Relationship between Self-Attention and Convolutional Layers

Fast Transformers with Clustered Attention

Transactions Chasing Scalability and Instruction Locality on Multicores

ArASL: Arabic Alphabets Sign Language Dataset

Scaling up Mixed Workloads: a Battle of Data Freshness, Flexibility, and Scheduling

Neural Scene Decomposition for Multi-Person Motion Capture

Scaling up analytical queries with column-stores

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

Sharing Data and Work Across Concurrent Analytical Queries

Context-Aware Attention Mechanism for Speech Emotion Recognition

Democratizing Transactional Programming

Vision-based detection of aircrafts and UAVs

A Computer Vision System to Localize and Classify Wastes on the Streets

Formalizing and verifying transactional memories

Program Analysis and Compilation Techniques for Speeding up Transactional Database Workloads

Model checking transactional memories

Sequential Proximity: Towards Provably Scalable Concurrent Search Algorithms