There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Wave propagation and group velocity

To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

Optical burst switching (OBS) - a new paradigm for an optical Internet

https://engineering.purdue.edu/~fsoptics/articles/Ultrafast_optical_pulse_shaping_tutorial-Weiner.pdf

Ultrafast optical pulse shaping: A tutorial review

We transmit over all 30 spatial and polarization modes of a 22.8-km multimode fiber. 15-mode photonic lanterns enabled low-loss coupling into and out of the fiber and a time-multiplexed coherent receiver facilitates measurement of all 30 signals.

30×30 MIMO transmission over 15 spatial modes

This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.

DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

This paper presents a comparative study of contention-resolution schemes based on wavelength, time, and space domains in an unslotted optical packet-switched network with a large irregular mesh topology consisted of 15 nodes For the first time, to the best of our knowledge, we investigated the effect of selective deflection and limited wavelength conversion Features and performances of different combinational schemes are listed and compared While simulation results show the effectiveness of wavelength conversion for resolving contentions over optical buffering and space deflection, physical explanations of the different effectiveness in resolving contentions of these schemes are also discussed

All-optical packet-switched networks: a study of contention-resolution schemes in an irregular mesh network with variable-sized packets

This paper reports on large field-of-regard, high-efficiency, and large aperture active optical phased arrays (OPAs) for optical beam steering in LIDAR systems. The fabricated 5 mm-long silicon photonic OPA with a 1.3 μm waveguide pitch achieved adjacent waveguide crosstalk below −12dB. A relatively large and uniform emission aperture has been achieved with a low-contrast silicon nitride assisted grating (~20 dB/cm) whose emission profile can be further optimized using an apodized design. The fabricated silicon-photonic OPA demonstrated > 40° lateral beam steering with no sidelobes in a ± 33° field-of-regard and 3.3° longitudinal beam steering via wavelength tuning by 20 nm centered at 1550 nm. We have fully integrated the silicon photonic OPA device with electronic controls and successfully demonstrated 2-dimensional coherent optical beam steering of pre-planned far-field patterns. Future improvements include placement of a distributed Bragg reflector (DBR) underneath the grating emitter in order to achieve nearly a factor of two improvement in emission efficiency.

Sub-wavelength-pitch silicon-photonic optical phased array for large field-of-regard coherent optical beam steering

We demonstrate a 120 GHz 3-dB bandwidth on-chip silicon photonic interleaver with a flat passband over a broad spectral range of 70 nm. The structure of the interleaver is based on an asymmetric Mach-Zehnder interferometer (MZI) with 3 ring resonators coupled to the arms of the MZI. The transmission spectra of this device depict a rapid roll-off on the band edges, where the 20-dB bandwidth is measured to be 142 GHz. This device is optimized for operation in the C-band with a channel crosstalk as low as −20 dB. The device also has full reconfiguration capability to compensate for fabrication imperfections.

S. J. Ben Yoo

Papers

30×30 MIMO transmission over 15 spatial modes

DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

All-optical packet-switched networks: a study of contention-resolution schemes in an irregular mesh network with variable-sized packets

Sub-wavelength-pitch silicon-photonic optical phased array for large field-of-regard coherent optical beam steering

High bandwidth on-chip silicon photonic interleaver