Foreword - Why Is Systematic Investing Important?

2021 ◽  
Vol 1 (1) ◽  
pp. i-iv
Keyword(s):  
Machine Learning ◽  
Structural Changes ◽  
Systematic Approach ◽  
Learning Tools ◽  
Behavioral Issues ◽  
Systematic Strategy ◽  
Product Claims

In this era of inexpensive computation and vast data, systematic, or algorithmically driven, investment is increasingly popular. Systematic strategies appear in stand-alone products as well in tail-hedging and defensive-overlay strategies. Indeed, given the enormous growth in data, it is becoming infeasible to process these data without the assistance of systematic tools. The key advantage of the systematic approach is the discipline it imposes—for example, machines are not plagued by behavioral issues such as disposition bias, and in a time of crisis, a systematic strategy keeps a “cool head.” Systematic approaches also pose many challenges. Systematic strategies may not quickly adapt to structural changes in the market. They also present the risk of “tech-washing” whereby an investment product claims to use “the latest machine-learning tools,” but the tools are misapplied or play a minimal role. Importantly, when systematic tools are applied by an inexperienced researcher, the backtests are often overfit, leading to disappointing performance in live trading.

Coming Together Through Falling Apart

2019 ◽  
Vol 40 (2) ◽  
pp. 151-168
Author(s):  
Heather Churchill ◽  
Jeremy M. Ridenour
Keyword(s):  
Data Analysis ◽  
Qualitative Data ◽  
Structural Changes ◽  
Systematic Approach ◽  
Qualitative Approach ◽  
Psychological Assessments ◽  
Transference And Countertransference ◽  
Testing Data ◽  
Psychoanalytically Oriented Psychotherapy

Abstract. Assessing change during long-term psychotherapy can be a challenging and uncertain task. Psychological assessments can be a valuable tool and can offer a perspective from outside the therapy dyad, independent of the powerful and distorting influences of transference and countertransference. Subtle structural changes that may not yet have manifested behaviorally can also be assessed. However, it can be difficult to find a balance between a rigorous, systematic approach to data, while also allowing for the richness of the patient’s internal world to emerge. In this article, the authors discuss a primarily qualitative approach to the data and demonstrate the ways in which this kind of approach can deepen the understanding of the more subtle or complex changes a particular patient is undergoing while in treatment, as well as provide more detail about the nature of an individual’s internal world. The authors also outline several developmental frameworks that focus on the ways a patient constructs their reality and can guide the interpretation of qualitative data. The authors then analyze testing data from a patient in long-term psychoanalytically oriented psychotherapy in order to demonstrate an approach to data analysis and to show an example of how change can unfold over long-term treatments.

A Comparative Study of Different Machine Learning Tools

2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Tools

Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency

2021 ◽  
Vol 192 ◽  
pp. 103181
Author(s):  
Jagadish Timsina ◽  
Sudarshan Dutta ◽  
Krishna Prasad Devkota ◽  
Somsubhra Chakraborty ◽  
Ram Krishna Neupane ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nutrient Management ◽  
Nutrient Use Efficiency ◽  
Learning Tools ◽  
Nutrient Use ◽  
Use Efficiency

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals Computational Methods for Structure-to-Function Analysis of Diet-Derived Catechins-Mediated Targeting of In Vitro Vasculogenic Mimicry

2021 ◽  
Vol 20 ◽  
pp. 117693512110092
Author(s):  
Abicumaran Uthamacumaran ◽  
Narjara Gonzalez Suarez ◽  
Abdoulaye Baniré Diallo ◽  
Borhane Annabi
Keyword(s):  
Machine Learning ◽  
Cancer Cells ◽  
Structural Changes ◽  
Function Analysis ◽  
Vasculogenic Mimicry ◽  
Machine Learning Algorithms ◽  
Emergent Behavior ◽  
Molecular Signature ◽  
Ovarian Cancer Cells

Background: Vasculogenic mimicry (VM) is an adaptive biological phenomenon wherein cancer cells spontaneously self-organize into 3-dimensional (3D) branching network structures. This emergent behavior is considered central in promoting an invasive, metastatic, and therapy resistance molecular signature to cancer cells. The quantitative analysis of such complex phenotypic systems could require the use of computational approaches including machine learning algorithms originating from complexity science. Procedures: In vitro 3D VM was performed with SKOV3 and ES2 ovarian cancer cells cultured on Matrigel. Diet-derived catechins disruption of VM was monitored at 24 hours with pictures taken with an inverted microscope. Three computational algorithms for complex feature extraction relevant for 3D VM, including 2D wavelet analysis, fractal dimension, and percolation clustering scores were assessed coupled with machine learning classifiers. Results: These algorithms demonstrated the structure-to-function galloyl moiety impact on VM for each of the gallated catechin tested, and shown applicable in quantifying the drug-mediated structural changes in VM processes. Conclusions: Our study provides evidence of how appropriate 3D VM compression and feature extractors coupled with classification/regression methods could be efficient to study in vitro drug-induced perturbation of complex processes. Such approaches could be exploited in the development and characterization of drugs targeting VM.

scholarly journals Automated computational analysis reveals structural changes in the enteric nervous system of nNOS deficient mice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ben R. Cairns ◽  
Benjamin Jevans ◽  
Atchariya Chanpong ◽  
Dale Moulding ◽  
Conor J. McCann
Keyword(s):  
Machine Learning ◽  
Nervous System ◽  
Enteric Nervous System ◽  
Structural Changes ◽  
Computational Analysis ◽  
Confocal Imaging ◽  
Learning Approaches ◽  
Composition And Structure ◽  
Oxide Synthase ◽  
Computational Image Analysis

AbstractNeuronal nitric oxide synthase (nNOS) neurons play a fundamental role in inhibitory neurotransmission, within the enteric nervous system (ENS), and in the establishment of gut motility patterns. Clinically, loss or disruption of nNOS neurons has been shown in a range of enteric neuropathies. However, the effects of nNOS loss on the composition and structure of the ENS remain poorly understood. The aim of this study was to assess the structural and transcriptional consequences of loss of nNOS neurons within the murine ENS. Expression analysis demonstrated compensatory transcriptional upregulation of pan neuronal and inhibitory neuronal subtype targets within the Nos1−/− colon, compared to control C57BL/6J mice. Conventional confocal imaging; combined with novel machine learning approaches, and automated computational analysis, revealed increased interconnectivity within the Nos1−/− ENS, compared to age-matched control mice, with increases in network density, neural projections and neuronal branching. These findings provide the first direct evidence of structural and molecular remodelling of the ENS, upon loss of nNOS signalling. Further, we demonstrate the utility of machine learning approaches, and automated computational image analysis, in revealing previously undetected; yet potentially clinically relevant, changes in ENS structure which could provide improved understanding of pathological mechanisms across a host of enteric neuropathies.

scholarly journals Organizing workers and machine learning tools for a less oppressive workplace

2021 ◽  
Vol 59 ◽  
pp. 102353
Author(s):  
Amber Grace Young ◽  
Ann Majchrzak ◽  
Gerald C. Kane
Keyword(s):  
Machine Learning ◽  
Learning Tools

scholarly journals A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method

Fluids ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 76
Author(s):  
Irfan Bahiuddin ◽  
Setyawan Bekti Wibowo ◽  
M. Syairaji ◽  
Jimmy Trio Putra ◽  
Cahyo Adi Pandito ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Systematic Approach ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Horizontal Distance ◽  
Droplet Generator ◽  
Droplet Model ◽  
Droplet Behavior ◽  
Diameter Reduction

Coronavirus disease 2019 (Covid-19) has been identified as being transmitted among humans with droplets from breath, cough, and sneezes. Understanding the droplets’ behavior can be critical information to avoid disease transmission, especially while designing a device deals with human air respiratory. Although various studies have provided enormous computational fluid simulations, most cases are too specific and quite challenging to combine with other similar studies directly. Therefore, this paper proposes a systematic approach to predict the droplet behavior for coughing cases using machine learning. The approach consists of three models, which are droplet generator, mask model, and free droplet model modeled using feedforward neural network (FFNN). The evaluation has shown that the three FFNNs models’ accuracies are relatively high, with R-values of more than 0.990. The model has successfully predicted the evaporation effect on the diameter reduction and the completely evaporated state, which can be considered unlearned cases for machine learning models. The predicted horizontal distance pattern also agrees with the data in the literature. In summary, the proposed approach has demonstrated the capability to predict the diameter pattern according to the experimental or previous work data at various mask face types.

Alignment and Identification of the Relationships between Key Performance Indicators in a Multilevel Tree Structure to Support Transportation Agency Decisions

2021 ◽  
pp. 036119812110190
Author(s):  
Hector Donaldo Mata ◽  
Mohammed Hadi ◽  
David Hale
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Ordinary Least Squares ◽  
Learning Tools ◽  
Least Squares Regression ◽  
Upper Level ◽  
Selection Operator ◽  
Transportation Agencies

Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

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