Though a small part of the body, the human hand is complex and remarkably versatile and multipurpose. Much work has gone into understanding the hand, such as understanding the physical capabilities of the human hand, how humans develop manipulation skills throughout the lifespan, how people translate task requirements into grasping strategy, and so on. Despite this effort, human manipulation is still not well understood. For example, how many grasps or manipulation actions do people use in daily life?

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Use of hands is the primary way we interact with the world around us. Recent trends in virtual reality (VR) also reflect the importance of interaction with hands. Mainstream virtual reality headsets such as Oculus Rift, HTC Vive, and the Playstation VR all support and encourage the use of their hand tracking controllers.

However, tracking hands is very challenging due to their small size and various occlusions. For this reason, makers of VR headsets let their users hold controllers that are more reliably tracked than tracking hands directly.

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Tuesday, August 14, 2018 - by

Students and faculty at Carnegie Mellon University's School of Computer Science are collaborating with the digital media intelligence firm Meltwater to advance the state of the art in artificial intelligence education and research using the company's AI platform.

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Linear tree constraints were introduced by Hofmann and Rodriguez in the context of amortized resource analysis for object oriented programs. More precisely, they gave a reduction from inference of resource types to constraint solving. Thus, once we have found an algorithm to solve the constraints generated from a program, we can read off the resource consumption from their solutions.

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This talk is a walk through the very large and labeled malware, IoT and attack datasets of the Stratosphere Lab. Among other security and ML research activities, one of the goals of the Stratosphere Lab is to create real, long, verified and labeled datasets for security research. This dataset is a huge collection of traffic that focuses on real (no simulations) and weeks-long malware executions, normal behaviors, infected normal employees, real hand-made attacks, traffic from real IoT devices, dozens of honeypots and background traffic from our university.

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The new non-volatile memory (NVM) technologies are projected to become the dominant type of main memory in the near future.  They promise byte-addressability, good read latencies, significantly lower energy and higher density compared to DRAM.  However, a key property of NVMs is the asymmetric read and write cost: write operations are much more expensive than reads regarding energy, bandwidth, and latency.  Such property contradicts the fifty years of classic algorithm research that has focused on settings in which reads and writes have similar cost, and poses the desire of “write-efficien

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We describe the implementation and evaluation of a Congestion Control Plane (CCP), a new way to structure congestion control functions at the sender by removing them from the datapath. With CCP, each datapath such as the Linux Kernel TCP, UDP-based QUIC, or kernel-bypass transports like mTCP/DPDK summarizes information about the round-trip time, packet receptions, losses, ECN, etc. via a well-defined interface, and algorithms running atop CCP can use this information to control the datapath’s congestion window or pacing rate.

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