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Technical Perspective: Tracking Pandemic-Driven Internet Traffic
From Communications of the ACM

Technical Perspective: Tracking Pandemic-Driven Internet Traffic

"A Year in Lockdown," by Anja Feldmann, et al., offers a detailed look at how Internet traffic changed during the COVID-19 pandemic.

A Year in Lockdown
From Communications of the ACM

A Year in Lockdown: How the Waves of COVID-19 Impact Internet Traffic

We review the impact of the first year of the COVID-19 pandemic on Internet traffic in order to analyze its performance.

Technical Perspective: An Elegant Model for Deriving Equations
From Communications of the ACM

Technical Perspective: An Elegant Model for Deriving Equations

"Deriving Equations from Sensor Data Using Dimensional Function Synthesis," by Vasileios Tsoutsouras, et al., addresses the key problem of discovering relationships...

Deriving Equations from Sensor Data Using Dimensional Function Synthesis
From Communications of the ACM

Deriving Equations from Sensor Data Using Dimensional Function Synthesis

We present a new method, which we call dimensional function synthesis, for deriving functions that model the relationship between multiple signals in a physical...

From Communications of the ACM

Technical Perspective: Race Logic Presents a Novel Form of Encoding

"In-Sensor Classification With Boosted Race Trees," by Georgios Tzimpragos, et al., proposes a surprising, novel, and creative approach to post-Moore's Law computing...

In-Sensor Classification With Boosted Race Trees
From Communications of the ACM

In-Sensor Classification With Boosted Race Trees

We demonstrate the potential of a novel form of encoding, race logic, in which information is represented as the delay in the arrival of a signal.

From Communications of the ACM

Technical Perspective: A Chiplet Prototype System for Deep Learning Inference

"Simba," by Yakun Sophia Shao, et al., presents a scalable deep learning accelerator architecture that tackles issues ranging from chip integration technology to...

Simba
From Communications of the ACM

Simba: Scaling Deep-Learning Inference with Chiplet-Based Architecture

This work investigates and quantifies the costs and benefits of using multi-chip-modules with fine-grained chiplets for deep learning inference, an application...

From Communications of the ACM

Technical Perspective: Robust Statistics Tackle New Problems

"Robustness Meets Algorithms," by Ilias Diakonikolas, et al., represents the beginning of a long and productive line of work on robust statistics in high dimensions...

Robustness Meets Algorithms
From Communications of the ACM

Robustness Meets Algorithms

We give the first efficient algorithm for estimating the parameters of a high-dimensional Gaussian that is able to tolerate a constant fraction of corruptions that...

From Communications of the ACM

Technical Perspective: A Logical Step Toward the Graph Isomorphism Problem

In "Isomorphism, Canonization, and Definability for Graphs of Bounded Rank Width," Grohe and Neuen show that the Weisfeiler-Leman algorithm in its plain form solves...

Isomorphism, Canonization, and Definability for Graphs of Bounded Rank Width
From Communications of the ACM

Isomorphism, Canonization, and Definability for Graphs of Bounded Rank Width

In this paper we study the graph isomorphism problem and the closely related graph canonization problem as well as logical definability and descriptive complexity...

From Communications of the ACM

Technical Perspective: The Strength of SuRF

The authors of "Succinct Range Filters" make a critical and insightful observation: For a given set of queries, the upper levels of the trie incur many more accesses...

Succinct Range Filters
From Communications of the ACM

Succinct Range Filters

We present the Succinct Range Filter (SuRF), a fast and compact data structure for approximate membership tests.

From Communications of the ACM

Technical Perspective: Why Don't Today's Deep Nets Overfit to Their Training Data?

"Understanding Deep Learning (Still) Requires Rethinking Generalization," Chiyuan Zhang, et al., brings a fundamental new theoretical challenge: Why don't today's...

Understanding Deep Learning (Still) Requires Rethinking Generalization
From Communications of the ACM

Understanding Deep Learning (Still) Requires Rethinking Generalization

In this work, we presented a simple experimental framework for interrogating purported measures of generalization.

From Communications of the ACM

Technical Perspective: Localizing Insects Outdoors

"3D Localization for Subcentimeter-Sized Devices," by Iyer, et al., neatly separates and solves the problems of robotic locomotion, sensing, localization, and communications...

3D Localization for Subcentimeter-Sized Devices
From Communications of the ACM

3D Localization for Subcentimeter-Sized Devices

We present the first localization system that consumes microwatts of power at a mobile device and can be localized across multiple rooms in settings such as homes...

From Communications of the ACM

Technical Perspective: Solving the Signal Reconstruction Problem at Scale

"Scalable Signal Reconstruction for a Broad Range of Applications," by Abolfazl Asudeh, et al. shows that algorithmic insights about SRP, combined with database...

Scalable Signal Reconstruction for a Broad Range of Applications
From Communications of the ACM

Scalable Signal Reconstruction for a Broad Range of Applications

Most of the common approaches for solving signal reconstruction problem do not scale to large problem sizes. We propose a novel and scalable algorithm for solving...
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