Event Archive

Recently, Merkle trees have been proposed as a way to scale block validation in cryptocurrencies such as Ethereum (or Bitcoin). The key insight is that block validators can verify Merkle proofs of account balances (or of unspent coins) against a Merkle root of the cryptocurrency’s state (i.e., the database of every user’s balance). This so-called stateless validation approach eliminates the need for block validators (e.g., miners, P2P nodes) to store large amounts of data and access it from disk during validation, which can be slow.

How can we efficiently represent and enumerate the results of theta-join queries? Factorization techniques have found notable success as a compact representation scheme that allows for enumeration algorithms where the results are produced incrementally after a preprocessing phase. However, these factorized representations have mainly been limited so far to equi-joins. This talk will present factorization techniques for general theta-join queries where the join conditions between relations go beyond equality.

The distributed futures interface is an increasingly popular choice for building distributed applications that manipulate large amounts of data. Distributed futures are an extension of RPC that combines futures and distributed memory: a distributed future is a reference whose eventual value may be stored on a remote node. An application can then express distributed computation without having to specify when or where execution should occur and data should be moved.

Smart Contracts and transactions allow users to implement elaborate constructions on cryptocurrency blockchains like Bitcoin and Ethereum. Many of these constructions, including operational payment channels and atomic swaps, use a building block called Hashed Time-Locked Contract (HTLC).

DUKE-FUQUA DECISION SCIENCES SEMINAR

When can maximum flow be solved for all pairs of nodes faster than naively solving it separately for each pair? We will overview new algorithms, including a very recent result, and also lower bounds (under some popular assumptions) for this question. 

Duke TechConnect brings students and employers together for networking, education and connections. At TechConnect on February 9, 2021, employers will connect with Engineering and Computer Science students in a virtual networking environment powered by CareerEco.

Graph mining, which finds all embeddings matching specific patterns, is a fundamental task in many applications. In this talk, I will present the first graph mining system that decomposes the target pattern into several subpatterns, and then computes the count of each.

Deep reinforcement learning (RL) has received great success in playing video and board games, where a simulator is well-defined and massive samples are available. However, in many real-world applications, the samples may be expensive and risky to collect. We consider designing sample efficient RL algorithms for online exploration and learning from offline interactions.

Unsolicited calls are one of the most prominent security issues facing individuals today. Despite wide-spread anecdotal discussion of the problem, many important questions remain unanswered. In this paper, we present the first large-scale, longitudinal analysis of unsolicited calls to a honeypot of up to 66,606 lines over 11 months. From call metadata we characterize the long-term trends of unsolicited calls, develop the first techniques to measure voicemail spam, wangiri attacks, and identify unexplained high-volume call incidences.

Based on our two recent publications, HPCC (SIGCOMM'19) and NetSeet (SIGCOMM'20), this talk will introduce the performance and reliability challenges faced by state-of-the-art data center networks in practice.

We will look at a vision of how the programmability in the data plane can potentially address these challenges, and the efforts Alibaba Cloud is making towards this direction.

For 15+ years, Google has had an engineering office in Chapel Hill. Come hear how that came to be, and what the teams are working on. Learn about the graphic engine in Android and Chrome, and how that technology has changed/evolved over time. Get a sampling of current efforts and challenges, and how some of that work inspired a class at UNC.

The evolution of Big Data and Internet of Things (IoT) paves the way to digitally connected devices and objects to the internet that influences every aspect of our lives. The devices in the IoT are heterogeneous based on the different hardware and network platforms. The state of the Big Data/IoT devices and the number of devices can change dynamically as well as the context of devices connecting location and speed.

Deep learning has shown to be very well suited to solve many outstanding problems in medical imaging. Unique challenges of medical imaging also present an opportunity for development of new deep learning algorithms. Dr. Mazurowski’s laboratory focuses on development and application of machine learning methods in medical imaging with a strong focus on ensuring that the developed algorithms will have an impact on clinical care. In this presentation, Dr. Mazurowski will discuss some ongoing projects, both focused on  development of new algorithms and on their clinical applications.