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Technical Perspective: Liquid Testing Using Built-in Phone Sensors
From Communications of the ACM

Technical Perspective: Liquid Testing Using Built-in Phone Sensors

"Liquid Testing with Your Smartphone," by Shichao Yue and Dina Katabi, proposes a novel technique for determining the surface tension of a liquid by leveraging...

Liquid Testing with Your Smartphone
From Communications of the ACM

Liquid Testing with Your Smartphone

We show a simple and accurate approach to measuring surface tension that's available to anyone with a smartphone.

Technical Perspective: The Importance of WINOGRANDE
From Communications of the ACM

Technical Perspective: The Importance of WINOGRANDE

"WINOGRANDE" explores new methods of dataset development and adversarial filtering, expressly designed to prevent AI systems from making claims of smashing through...

WinoGrande
From Communications of the ACM

WinoGrande: An Adversarial Winograd Schema Challenge at Scale

We introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original Winograd Schema Challenge, but adjusted to improve both the scale and the...

Technical Perspective: eBP Rides the Third Wave of Mobile Health
From Communications of the ACM

Technical Perspective: eBP Rides the Third Wave of Mobile Health

The automated blood pressure wearable system described in "eBP," by Nam Bui et al., is a sterling example of the third wave of mobile health tech to fill the preventative...

eBP
From Communications of the ACM

eBP: An Ear-Worn Device for Frequent and Comfortable Blood Pressure Monitoring

We developed eBP to measure blood pressure from inside a user's ear aiming to minimize the measurement's impact on normal activities while maximizing its comfort...

Technical Perspective: The Quest for Optimal Multi-Item Auctions
From Communications of the ACM

Technical Perspective: The Quest for Optimal Multi-Item Auctions

"Optimal Auctions Through Deep Learning," by Paul Dütting et al., contributes a very interesting and forward-looking new take on the optimal multi-item mechanism...

Optimal Auctions Through Deep Learning
From Communications of the ACM

Optimal Auctions Through Deep Learning

We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions...

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: 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: XNOR-Networks – Powerful but Tricky

How to produce a convolutional neural net that is small enough to run on a mobile device, and accurate enough to be worth using? The strategies in "Enabling AI...

Enabling AI at the Edge with XNOR-Networks
From Communications of the ACM

Enabling AI at the Edge with XNOR-Networks

We present a novel approach to running state-of-the-art AI algorithms in edge devices, and propose two efficient approximations to standard convolutional neural...

From Communications of the ACM

Technical Perspective: When the Adversary Is Your Friend

The key insight of the "Generative Adversarial Networks," by Ian Goodfellow et al., is to learn a generative model's loss function at the same time as learning...

Generative Adversarial Networks
From Communications of the ACM

Generative Adversarial Networks

In this overview paper, we describe one particular approach to unsupervised learning via generative modeling called generative adversarial networks. We briefly...

From Communications of the ACM

Technical Perspective: Progress in Spatial Computing for Flood Prediction

There are few algorithms for multi-flow graphs beyond flow accumulation. The authors of "Flood-Risk Analysis on Terrains" take a big step to fill this knowledge...

Flood-Risk Analysis on Terrains
From Communications of the ACM

Flood-Risk Analysis on Terrains

In this paper, we study a number of flood-risk related problems, give an overview of efficient algorithms for them, as well as explore the efficacy and efficiency...
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