Researchers have found that computations may require less memory than previously thought, with a new study suggesting that a calculation of X steps requires only the square root of X log X memory slots.
An interactive tool to visualize maze generation using Depth-First Search (DFS) and maze solving using Breadth-First Search (BFS).
This article by Zelda B. Zabinsky provides an overview of random search algorithms, which are particularly useful for tackling complex global optimization problems with either continuous or discrete variables. These algorithms, including simulated annealing, genetic algorithms, and particle swarm optimization, leverage randomness or probability in their iterative processes, often falling under the category of metaheuristics. Such methods are valuable for problems characterized by nonconvex, nondifferentiable, or discontinuous objective functions, as they offer a trade-off between optimality and computational speed. Random search algorithms can be categorized by their approach to exploration versus exploitation, and their application spans various fields, including engineering, scheduling, and biological systems. They address challenges where traditional deterministic methods struggle, particularly in the absence of clear structures distinguishing local from global optima.
The areas of research associated with Yinglian Xie, based on the dblp dataset, primarily focus on computer science domains such as cybersecurity, network analysis, and systems security. Key research topics include the detection and analysis of spamming botnets, anonymization techniques on the internet, and privacy protection in search systems. There is also significant work on network-level spam detection, botnet signatures, and web security. Yinglian Xie's publications span various conferences like IEEE Symposium on Security and Privacy, ACM SIGCOMM, and NDSS, highlighting a strong emphasis on both theoretical and practical aspects of security and privacy in distributed systems. Additionally, Xie has explored topics related to graph mining and anomaly detection in large networks.
In this paper, we revisit one of the simplest problems in data structures: the task of inserting elements into an open-addressed hash table so that elements can later be retrieved with as few probes as possible. We show that, even without reordering elements over time, it is possible to construct a hash table that achieves far better expected search complexities (both amortized and worst-case) than were previously thought possible. Along the way, we disprove the central conjecture left by Yao in his seminal paper 'Uniform Hashing is Optimal'. All of our results come with matching lower bounds.
A young computer scientist at Rutgers University, along with his former professor and a colleague from Carnegie Mellon University, has disproved a 40-year-old conjecture in data science related to hash tables, showing that a new type can achieve faster search times than previously thought possible.
Andrew Krapivin, an undergraduate student at Rutgers University, along with his colleagues Martín Farach-Colton and William Kuszmaul, has challenged a 40-year-old conjecture by demonstrating that a new type of hash table can perform searches and insertions faster than previously thought possible. Their invention, inspired by "tiny pointers," contradicts a long-standing assumption that the time complexity for these operations would be proportional to the table's fullness (measured as x). Instead, they found that the time complexity for their new hash table is proportional to (log x)^2, which is significantly faster. Additionally, their study also refutes another conjecture regarding the average time taken for queries in non-greedy hash tables, showing that a constant average query time is achievable regardless of the table's fullness.
Dr Valeria Correa Vaz de Paiva is a Brazilian mathematician, logician, computer scientist, and computational linguist known for introducing the concept of dialectica spaces, which model linear logic in advanced programming languages. She has contributed significantly to natural language processing (NLP) and works at the Topos Institute in Berkeley, California. De Paiva is committed to encouraging more women into STEM fields and is actively involved in Women In Logic (WIL).
Physicists and computer scientists are using stochastic thermodynamics to understand the energy costs of computation, with implications for designing more energy-efficient devices.
Professor Mima Noyuri's laboratory website, covering topics such as computer science, cognitive psychology, education, and AI, with news, publications, and seminars.
"We present a systematic review of some of the popular machine learning based email spam filtering approaches."
"Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering."
