Machine learning for fiber nonlinearity mitigation in long-haul coherent optical transmission systems

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation

Neural Circuit Dependence of Acute and Subacute Nociception in C. Elegans

Nociception, the detection and avoidance of harmful cues, is a crucial system in all organisms. Animals use nociceptive systems to escape from substances that decrease survival, and can also modulate the threshold for avoidance behaviors to weigh the attractive features of an environment against its harmful features. To allow regulation, the nociception system of mammals incorporates multiple feedback and feedforward loops in its central and peripheral pathways. The nociception system of the roundworm Caenorhabditis elegans shares many features of the mammalian circuit. Both neural circuits feature a direct path from sensory neurons to motor neurons that is connected by a single class of interneuron, bypassing the higher processing centers. Both neural circuits also feature higher processing pathways that receive information from sensory neurons and provide further input onto the direct pathway. While the anatomical wiring of the C. elegans nervous system has been known for decades, how sensory neurons access different downstream paths in the circuit is less clear. One possible route of differential access of sensory input to downstream neurons is through different dynamics of activation. The temporal dimension of neural circuits cannot be deduced by anatomical wiring, but must be measured directly. In my thesis, I have characterized and manipulated the dynamic properties of a classical nociceptor in C. elegans, the polymodal sensory neuron ASH, and asked how these properties instruct downstream circuits and behavior. I thus first elucidated ASH calcium activation dynamics using simple step responses and using a newly developed systems identification approach for C. elegans. Using both long pulses and rapidly fluctuating “white noise” sequences of different nociceptive stimuli, I deduced their ASH activation profiles and linear temporal filters describing how the neuron summates the history of stimulus encounter. This analysis demonstrated that ASH calcium responses to natural stimuli include both linear features and multiple nonlinear components. Mutations in G protein-coupled sensory signaling disrupt both fast linear filtering and sustained responses to nociceptive stimuli. Mutations in a voltage gated calcium channel alter the temporal qualities of the ASH response in a pattern suggesting a role of this channel in sensory adaptation. In the course of these studies, I discovered several additional classes of sensory neurons that respond to nociceptive stimuli with robust calcium responses, even though past studies did not demonstrate a role for these neurons in nociceptive behavior. To gain experimental control over the dynamic activity that initiates nociceptive signaling, I ectopically expressed the pheromone receptors SRG-34 and SRG-36 in ASH and activated this system with their endogenous ligand, the ascaroside C3. ASH does not normally detect C3, but when it expresses either of these receptors it generates robust calcium responses to C3. These calcium signals have distinct temporal dynamics: SRG-34 mediated calcium signals are fast rising and fast adapting, while SRG-36 mediated calcium signals increase slowly during stimulation with little adaptation. Expression of SRG-34 or SRG-36 in ASH caused animals to avoid C3. Remarkably, time-aligned histograms of C3-induced avoidance behavior during stimulus onset, presence, and removal closely followed the dynamics of ASH calcium activity at these same time points, with a fast onset and adaptation for SRG-34 and a slow, sustained avoidance of SRG-36. ASH can directly activated the backward command motor neuron AVA or indirectly activate AVA through other neuronal pathways, including the intermediate interneuron AIB. Selectively silencing the AIB interneuron with the a chemical genetics system using the histamine-gated chloride channel resulted in complete loss of nociceptive avoidance behaviors induced by slow-ramping SRG-36 receptor in ASH, but had less of an effect on SRG-34 avoidance. Selectively silencing the AVA backward command interneuron reduced reversals, but spared or increased other avoidance behaviors for both SRG-34 and SRG-36. These results indicate that downstream interneurons are engaged in different ways, and to different degrees, depending on the mechanism of ASH activation. I next monitored the activity of AIB and AVA neurons in freely-moving ASH:srg-34 or ASH:srg-36 animals responding to C3. In ASH:srg-34 animals, AIB and AVA begin increasing activity upon C3 onset. In ASH:srg-36 worms, AIB increased activity before AVA. Together with my AIB silencing results, these observations suggest that AIB accumulates signals from ASH over time to promote AVA activity. Using a coherent type-1 feed forward loop with a calcium slope-determined AND or OR logic, I modeled features of AIB contribution to nociceptive behaviors in response to different ASH temporal dynamics. These findings suggest that feedforward excitation loops, a motif seen in C. elegans and mammalian nervous systems, can result in behaviorally-salient consequences in response to different sensory neuron calcium dynamics

Research on the Impact of AI on China's Industrial Development and Policy Choices

Artificial intelligence (AI) is a strategic technology leading a new round of scientific and technological revolution and industrial reform, with a strong "head goose" effect of spillover. Accelerating the development of a new generation of AI is an important strategic starting point to win the initiative in global science and technology competition, and an important strategic resource to promote the leapfrog development of China's science and technology, industrial optimization and upgrading, and the overall leap in productivity. Based on the current situation of China's AI development, this paper analyzes the current development trend of China's AI, and puts forward policies and measures for the development of China's AI in the new era

Multiple Objective Function Optimization and Trade Space Analysis

Optimization can assist in obtaining the best possible solution to a design problem by varying related variables under given constraints. It can be applied in many practical applications, including engineering, during the design process. The design time can be further reduced by the application of automated optimization methods. Since the required resource and desired benefit can be translated to a function of variables, optimization can be viewed as the process of finding the variable values to reach the function maxima or minima. A Multiple Objective Optimization (MOO) problem is when there is more than one desired function that needs to be minimized concurrently. In MOO, Pareto Solutions are defined as the set of solutions that are not worse than any single solution of all objective functions simultaneously. In other words, MOO is a process of applying algorithms to find Pareto solutions to a certain problem. Using Tradespace analysis, we can further identify the optimal Pareto Solution with the highest utility at a fixed cost. The combination of MOO and tradespace analysis can evaluate hundreds of designs simultaneously to select the optimal one. Mechanical system design is the process of devising a procedure to accomplish the given task, for which a design engineer\u27s role is to optimize resource consumption. With recent advancements in multi-functional systems, the complexity of machines has been increasing. This presents a great challenge for design engineers, who must contend with optimizing systems with several functions in tandem. It is thus essential to develop methods that can simplify the design process. Tower clocks, as a classical type of machine, were extensively used for public time display during the period when watches and home clocks iii were rare. These mechanical movements once played essential roles in society and industry. They could be found at churches, courthouses, and universities/schools to visually and auditorily record the passage of time for residents and students. They were also used to regulate railroad schedules and workforce hours for the emerging industrial sector. Although mainly used for decorative purposes today, the components of such movement, including the assembled gears, escapement, and pendulum with weight drive, provide insight into optimization and tradespace analysis problems. In this research, computational methods plus experimental observations were used to investigate the optimal designs of the E. Howard Clock Model 00 - a movement was manufactured by E. Howard Clock Company. First, A computer-aided-design (CAD) model of this movement was created using the SolidWorks® software package to illustrate the working principle of the pendulum clock and facilitate engineering optimization studies. Next, the mathematical model of this clock was developed and simulated to explore the operation behaviors and conversion of potential-to-kinetic energy. The experimental process to validate this model was also described in detail. After that, A Single Objective Optimization (SOO) algorithm (i.e., simulated annealing) was applied to the model to optimize the pendulum subsystem for accuracy, quality factor, and mass. Numerical results show the desired quality factor can be achieved by varying the pendulum length and bob radius/thickness. Compared to the original, the optimized design added 15% to the mass of the pendulum while maintaining the clock\u27s accuracy. Tradeoffs between quality factor, pendulum properties, and period were investigated and discussed with representative experimental and computational results. Lastly, two Multiple Objective Optimization iv (MOO) approaches (i.e., Multi-objective Genetic Algorithm (MOGA-II) and Multiobjective Simulated Annealing (MOSA) were applied to the developed mathematic model. The optimal movement designs in terms of pendulum mass and time accuracy were further explored for a range of clock periods. Numerical results demonstrated a 0.7% increase in the quality factor and a 0.56% reduction in the mass while maintaining the designed period by modifying the above-mentioned pendulum\u27s parameters. More importantly, these changes can provide material cost savings in a mass production scenario. Overall, this study highlighted the optimization design engineers have considered for decades which can now be visualized using computer tools for greater insight. This methodology has the potential to be applied in the designing of other complex systems as well