Quantum Grid Search Algorithm
In this paper we present a novel quantum algorithm, namely the quantum grid search algorithm, to solve a special search problem. Suppose $ k $ non-empty buckets are given, such that each bucket contains some marked and some unmarked items. In one trial an item is selected from each of the $ k $ buckets. If every selected item is a marked item, then the search is considered successful. This search problem can also be formulated as the problem of finding a ‘marked path’ associated with specified bounds on a discrete grid. Our algorithm essentially uses several Grover search operators in parallel to efficiently solve such problems. We also present an extension of our algorithm combined with a binary search algorithm in order to efficiently solve global trajectory optimization problems. Estimates of the expected run times of the algorithms are also presented, and it is proved that our proposed algorithms offer exponential improvement over pure classical search algorithms, while a traditional Grover’s search algorithm offers only a quadratic speedup. We note that this gain comes at the cost of increased complexity of the quantum circuitry. The implication of such exponential gains in performance is that many high dimensional optimization problems, which are intractable for classical computers, can be efficiently solved by our proposed quantum grid search algorithm. …

Deep Factor Model
We propose to represent a return model and risk model in a unified manner with deep learning, which is a representative model that can express a nonlinear relationship. Although deep learning performs quite well, it has significant disadvantages such as a lack of transparency and limitations to the interpretability of the prediction. This is prone to practical problems in terms of accountability. Thus, we construct a multifactor model by using interpretable deep learning. We implement deep learning as a return model to predict stock returns with various factors. Then, we present the application of layer-wise relevance propagation (LRP) to decompose attributes of the predicted return as a risk model. By applying LRP to an individual stock or a portfolio basis, we can determine which factor contributes to prediction. We call this model a deep factor model. We then perform an empirical analysis on the Japanese stock market and show that our deep factor model has better predictive capability than the traditional linear model or other machine learning methods. In addition , we illustrate which factor contributes to prediction. …

Pypeline
Pypeline is a simple yet powerful python library for creating concurrent data pipelines.
• Pypeline was designed to solve simple medium data tasks that require concurrency and parallelism but where using frameworks like Spark or Dask feel exaggerated or unnatural.
• Pypeline exposes an easy to use, familiar, functional API.
• Pypeline enables you to build pipelines using Processes, Threads and asyncio.Tasks via the exact same API.
• Pypeline allows you to have control over the memory and cpu resources used at each stage of your pipeline. …

Industrial Symbiosis System (ISS)
Multiagent Systems (MAS) research reached a maturity to be confidently applied to real-life complex problems. Successful application of MAS methods for behavior modeling, strategic reasoning, and decentralized governance, encouraged us to focus on applicability of MAS techniques in a class of industrial systems and to elaborate on potentials and challenges for method integration/contextualization. We direct attention towards a form of industrial practices called Industrial Symbiosis Systems (ISS) as a highly dynamic domain of application for MAS techniques. In ISS, firms aim to reduce their material and energy footprint by circulating reusable resources among the members. To enable systematic reasoning about ISS behavior and support firms’ (as well as ISS designers’) decisions, we see the opportunity for marrying industrial engineering with engineering multiagent systems. This enables introducing (1) representation frameworks to reason about dynamics of ISS, (2) operational semantics to develop computational models for ISS, and (3) coordination mechanisms to enforce desirable ISS behaviors. We argue for applicability and expressiveness of resource-bounded formalisms and norm-aware mechanisms for the design and deployment of ISS practices. In this proposal, we elaborate on different dimensions of ISS, present a methodological foundation for ISS development, and finally discuss open problems. …