Machine Learning Techniques To Mitigate Nonlinear Impairments In Optical Fiber System

The upcoming deployment of 5/6G networks, online services like 4k/8k HDTV (streamers and online games), the development of the Internet of Things concept, connecting billions of active devices, as well as the high-speed optical access networks, impose progressively higher and higher requirements on the underlying optical networks infrastructure. With current network infrastructures approaching almost unsustainable levels of bandwidth utilization/ data traffic rates, and the electrical power consumption of communications systems becoming a serious concern in view of our achieving the global carbon footprint targets, network operators and system suppliers are now looking for ways to respond to these demands while also maximizing the returns of their investments. The search for a solution to this predicted ªcapacity crunchº led to a renewed interest in alternative approaches to system design, including the usage of high-order modulation formats and high symbol rates, enabled by coherent detection, development of wideband transmission tools, new fiber types (such as multi-mode and ±core), and finally, the implementation of advanced digital signal processing (DSP) elements to mitigate optical channel nonlinearities and improve the received SNR. All aforementioned options are intended to boost the available optical systems’ capacity to fulfill the new traffic demands. This thesis focuses on the last of these possible solutions to the ªcapacity crunch," answering the question: ªHow can machine learning improve existing optical communications by minimizing quality penalties introduced by transceiver components and fiber media nonlinearity?". Ultimately, by identifying a proper machine learning solution (or a bevy of solutions) to act as a nonlinear channel equalizer for optical transmissions, we can improve the system’s throughput and even reduce the signal processing complexity, which means we can transmit more using the already built optical infrastructure. This problem was broken into four parts in this thesis: i) the development of new machine learning architectures to achieve appealing levels of performance; ii) the correct assessment of computational complexity and hardware realization; iii) the application of AI techniques to achieve fast reconfigurable solutions; iv) the creation of a theoretical foundation with studies demonstrating the caveats and pitfalls of machine learning methods used for optical channel equalization. Common measures such as bit error rate, quality factor, and mutual information are considered in scrutinizing the systems studied in this thesis. Based on simulation and experimental results, we conclude that neural network-based equalization can, in fact, improve the channel quality of transmission and at the same time have computational complexity close to other classic DSP algorithms

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

Benchmarking and survey of explanation methods for black box models

The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and discrepancies of returned explanations through their visual appearance. A companion website to the paper is provided as a continuous update to new explainers as they appear. Moreover, a subset of the most robust and widely adopted explainers, are benchmarked with respect to a repertoire of quantitative metrics

Proceedings - 30. Workshop Computational Intelligence : Berlin, 26. - 27. November 2020

The proceedings of the 30th workshop on computational intelligence focus on methods, applications, and tools for fuzzy systems, artificial neural networks, deep learning, system identification, and data mining techniques

The social life of placebos: proximate and evolutionary mechanisms of biocultural interactions in Asante medical encounters

The Social Life of Placebos is an interdisciplinary study of the evolution of placebogenic responses – beneficial ones activated by psychosocial triggers -- and their elicitation in Asante medical contexts. Based on an extensive literature review in social, cultural, and medical studies and over 26 months of intensive research in rural Ghana, West Africa, it examines the therapeutic efficacy of Asante medical encounters by analyzing rites of care-giving within an evolutionary framework. Section 1 investigates why evolutionary processes appear to have made human physiology susceptible to psychosocial manipulation, what the health consequences of that susceptibility are in modern environments, and how culturally specific expectations and healing rituals might dampen or amplify that susceptibility. Because of key transitions in human evolution, the fitness consequences of sociality have increased rapidly and created the conditions whereby endogenous mechanisms have become responsive to sociocultural conditions. This explanation helps us better understand why culturally specific rituals can elicit powerful beneficial (placebo) and adverse (nocebo) physiological responses. Using a mixed methodology of physiological data and ethnographic case studies collected from hundreds of Asante medical encounters, Section 2 illuminates evolutionary and proximate processes in Asante contexts of care-giving and healing rituals in detailed chapters on pain, emotion, and stress. It examines the social and cultural resources and techniques that Asante health practitioners rely on for pain management in contexts where no pain medication is available. It analyzes the biocultural interactions that can take place when healers modify patient perceptions, emotions, and expectations. The dissertation concludes with biometric evidence that Asante indigenous ritual healing ceremonies actually promote significant entrainment and relaxation effects