Mode stabilized terrace InGaAsP lasers on semi-insulating InP

Mode stabilized terrace InGaAsP lasers have been fabricated on semi-insulating InP substrates. The fabrication involves a selective, single-step liquid phase epitaxial growth process, and a lateral Zn diffusion. Two versions of the terrace lasers are fabricated, and threshold currents as low as 35 mA and 50 mA respectively are obtained. The lasers operate with a stable single lateral mode. High power performance is observed. These lasers are suitable for monolithic integration with other optoelectronic devices

Connectivity and Performance Tradeoffs in the Cascade Correlation Learning Architecture

The Cascade Correlation [1] is a very flexible, efficient and fast algorithm for supervised learning. It incrementally builds the network by adding hidden units one at a time, until the desired input/output mapping is achieved. It connects all the previously installed units to the new unit being added. Consequently, each new unit in effect adds a new layer and the fan–in of the hidden and output units keeps on increasing as more units get added. The resulting structure could be hard to implement in VLSI, because the connections are irregular and the fan-in is unbounded. Moreover, the depth or the propagation delay through the resulting network is directly proportional to the number of units and can be excessive. We have modified the algorithm to generate networks with restricted fan-in and small depth (propagation delay) by controlling the connectivity. Our results reveal that there is a tradeoff between connectivity and other performance attributes like depth, total number of independent parameters, learning time, etc. When the number of inputs or outputs is small relative to the size of the training set, a higher connectivity usually leads to faster learning, and fewer independent parameters, but it also results in unbounded fan-in and depth. Strictly layered architectures with restricted connectivity, on the other hand, need more epochs to learn and use more parameters, but generate more regular structures, with smaller, limited fan-in and significantly smaller depth (propagation delay), and may be better suited for VLSI implementations. When the number of inputs or outputs is not very small compared to the size of the training set, however, a strictly layered topology is seen to yield an overall better performance

Monolithic integration of a very low threshold GaInAsP laser and metal-insulator-semiconductor field-effect transistor on semi-insulating InP

Monolithic integration of 1.3-µm groove lasers and metal-insulator-semiconductor field-effect transistors (MISFET) is achieved by a simple single liquid phase epitaxy (LPE) growth process. Laser thresholds as low as 14 mA for 300-µm cavity length are obtained. MIS depletion mode FET's with n channels on LPE grown InP layer show typical transconductance of 5–10 mmho. Laser modulation by the FET current is demonstrated at up to twice the threshold current

The Chilling Effect of Governance-by-Data on Data Markets

Big data has become an important resource not only for commerce but also for governance. Governance-by-data seeks to take advantage of the bulk of data collected by private firms to make law enforcement more efficient. It can take many forms, including setting enforcement priorities, affecting methods of proof, and even changing the content of legal norms. For instance, car manufacturers can use real-time data on the driving habits of drivers to learn how their cars respond to different driving patterns. If shared with the government, the same data can be used to enforce speed limits or even to craft personalized speed limits for each driver. The sharing of data for the purpose of law enforcement raises obvious concerns for civil liberties. Indeed, over the past two decades, scholars have focused on the risks arising from such data sharing for privacy and freedom. So far, however, the literature has generally overlooked the implications of such dual use of data for data markets and data-driven innovation. In this Essay, we argue that governance-by-data may create chilling effects that could distort data collection and data-driven innovation. We challenge the assumptions that incentives to collect data are a given and that firms will continue to collect data notwithstanding governmental access to such data. We show that, in some instances, an inverse relationship exists between incentives for collecting data and sharing it for the purpose of governance. Moreover, the incentives of data subjects to allow the collection of data by private entities might also change, thereby potentially affecting the efficiency of data-driven markets and, subsequently, data-driven innovation. As a result, data markets might not provide sufficient and adequate data to support digital governance. This, in turn, might significantly affect welfare