Bell Labs

About: Bell Labs is a based out in . It is known for research contribution in the topics: Laser & Optical fiber. The organization has 36499 authors who have published 59862 publications receiving 3190823 citations. The organization is also known as: Bell Laboratories & AT&T Bell Laboratories.

Expertise Browser: a quantitative approach to identifying expertise

Abstract: Finding relevant expertise is a critical need in collaborative software engineering, particularly in geographically distributed developments. We introduce a tool, called Expertise Browser (ExB), that uses data from change management systems to locate people with desired expertise. It uses a quantification of experience, and presents evidence to validate this quantification as a measure of expertise. The tool enables developers, for example, to easily distinguish someone who has worked only briefly in a particular area of the code from someone who has more extensive experience, and to locate people with broad expertise throughout large parts of the product, such as modules or even subsystems. In addition, it allows a user to discover expertise profiles for individuals or organizations. Data from a deployment of the tool in a large software development organization shows that newer, remote sites tend to use the tool for expertise location more frequently. Larger, more established sites used the tool to find expertise profiles for people or organizations. We conclude by describing extensions that provide continuous awareness of ongoing work and an interactive, quantitative resume/spl acute/.

DeepX: a software accelerator for low-power deep learning inference on mobile devices

Abstract: Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted to extract the high-level information needed by mobile apps. It is critical that the gains in inference accuracy that deep models afford become embedded in future generations of mobile apps. In this work, we present the design and implementation of DeepX, a software accelerator for deep learning execution. DeepX signif- icantly lowers the device resources (viz. memory, computation, energy) required by deep learning that currently act as a severe bottleneck to mobile adoption. The foundation of DeepX is a pair of resource control algorithms, designed for the inference stage of deep learning, that: (1) decompose monolithic deep model network architectures into unit- blocks of various types, that are then more efficiently executed by heterogeneous local device processors (e.g., GPUs, CPUs); and (2), perform principled resource scaling that adjusts the architecture of deep models to shape the overhead each unit-blocks introduces. Experiments show, DeepX can allow even large-scale deep learning models to execute efficently on modern mobile processors and significantly outperform existing solutions, such as cloud-based offloading.

Automatic symbolic verification of embedded systems

Abstract: We present a model checking procedure and its implementation for the automatic verification of embedded systems. Systems are represented by hybrid automata - machines with finite control and real-valued variables modeling continuous environment parameters such as time, pressure, and temperature. System properties are specified in a real-time temporal logic and verified by symbolic computation. The verification procedure, implemented in Mathematica, is used to prove digital controllers and distributed algorithms correct. The verifier checks safety, liveness, time-bounded, and duration properties of hybrid automata. >

Scattering of Light by One- and Two-Magnon Excitations

Abstract: We present details of the theory of light scattering by one- and two-magnon excitations, and compare predictions of the theory with our experimental results in the tetragonal antiferromagnets Mn${\mathrm{F}}_{2}$ and Fe${\mathrm{F}}_{2}$. Two mechanisms are considered for first-order (one-magnon) light scattering: one involving a direct magnetic-dipole coupling and the other involving an indirect electric-dipole coupling which proceeds through a spin-orbit interaction. Experimental results on the intensity and polarization selection rules of the first-order scattering show that the spin-orbit mechanism is the important one. On the other hand, second-order (two-magnon) scattering is observed to be even stronger than first-order scattering in these antiferromagnets, implying that the process is not due to the spin-orbit mechanism taken to a higher order in perturbation theory. A theory of second-order scattering based on an excited-state exchange interaction between opposite sublattices is given. When coupled with group-theoretical requirements for the ${{D}_{2h}}^{12}$ crystals, the mechanism predicts the intensity, the polarization selection rules, and the magnetic field dependence of the second-order spectrum. Features of the second-order spectra are related quantitatively to magnons at specific points in the Brillouin zone. Analysis of both first- and second-order magnon scattering has thus enabled determination of the complete magnon dispersion relation for Fe${\mathrm{F}}_{2}$.