scholarly journals Functional Tradeoffs Carry Phenotypes Across the Valley of the Shadow of Death

2020 ◽  
Vol 60 (5) ◽  
pp. 1268-1282 ◽  
Author(s):  
P David Polly
Keyword(s):  
Phylogenetic Tree ◽  
Optimal Solution ◽  
Poor Performance ◽  
Disruptive Selection ◽  
Phenotypic Evolution ◽  
Computational Simulations ◽  
Functional Roles ◽  
Specific Performance ◽  
Proportional Contribution ◽  
Intermediate Performance

Synopsis Functional tradeoffs are often viewed as constraints on phenotypic evolution, but they can also facilitate evolution across the suboptimal valleys separating performance peaks. I explore this process by reviewing a previously published model of how disruptive selection from competing functional demands defines an intermediate performance optimum for morphological systems that cannot simultaneously be optimized for all of the functional roles they must play. Because of the inherent tradeoffs in such a system, its optimal morphology in any particular environmental context will usually be intermediate between the performance peaks of the competing functions. The proportional contribution of each functional demand can be estimated by maximum likelihood from empirically observed morphologies, including complex ones measured with multivariate geometric morphometrics, using this model. The resulting tradeoff weight can be mapped onto a phylogenetic tree to study how the performance optimum has shifted across a functional landscape circumscribed by the function-specific performance peaks. This model of tradeoff evolution is sharply different from one in which a multipeak Ornstein–Uhlenbeck (OU) model is applied to a set of morphologies and a phylogenetic tree to estimate how many separate performance optima exist. The multi-peak OU approach assumes that each branch is pushed toward one of two or more performance peaks that exist simultaneously and are separated by valleys of poor performance, whereas the model discussed here assumes that each branch tracks a single optimal performance peak that wanders through morphospace as the balance of functional demands shifts. That the movements of this net performance peak emerge from changing frequencies of selection events from opposing functional demands are illustrated using a series of computational simulations. These simulations show how functional tradeoffs can carry evolution across putative performance valleys: even though intermediate morphologies may not perform optimally for any one function, they may represent the optimal solution in any environment in which an organism experiences competing functional demands.

The Importance of Role-Specific Performance and Sociopolitical Factors for Chief Financial Officer Employment Outcomes

2018 ◽  
Vol 33 (1) ◽  
pp. 61-81 ◽  
Author(s):  
Ellen Engel ◽  
Feng Gao ◽  
Xue Wang
Keyword(s):  
Performance Measures ◽  
Financial Reporting ◽  
Significant Role ◽  
Economic Performance ◽  
Career Paths ◽  
Poor Performance ◽  
Regulatory Compliance ◽  
Chief Financial Officer ◽  
Direct Influence ◽  
Specific Performance

SYNOPSIS This paper investigates the importance of role-specific performance measures and sociopolitical factors in the career paths of CFOs. We find that forced CFO turnover is associated with poor performance in functions over which they have more direct influence, including financial reporting, financing, and regulatory compliance. We also find that CFOs are less likely to be dismissed when they have greater connectedness with the CEO and have stronger influence within the firm. Interestingly, sociopolitical factors are linked with promotion outcomes, but economic performance does not appear to play a significant role. The collective evidence indicates that both economic and sociopolitical factors have an important role in influencing CFO career paths.

scholarly journals Competitive Caching with Machine Learned Advice

2021 ◽  
Vol 68 (4) ◽  
pp. 1-25
Author(s):  
Thodoris Lykouris ◽  
Sergei Vassilvitskii
Keyword(s):  
Online Algorithms ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Poor Performance ◽  
Machine Learning Algorithms ◽  
Average Error ◽  
Generalization Error ◽  
Worst Case ◽  
Future Events ◽  
Real World Datasets

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.

A novel image classification model based on adversarial training for pulsar candidate identification

2020 ◽  
Vol 39 (5) ◽  
pp. 7657-7669
Author(s):  
Linyong Zhou ◽  
Shanping You ◽  
Bimo Ren ◽  
Xuhong Yu ◽  
Xiaoyao Xie
Keyword(s):  
Image Recognition ◽  
Optimal Solution ◽  
Poor Performance ◽  
Classification Performance ◽  
Training Data ◽  
Classification Model ◽  
Model Based ◽  
Unseen Data ◽  
Adversarial Training ◽  
Candidate Identification

Pulsars are highly magnetized, rotating neutron stars with small volume and high density. The discovery of pulsars is of great significance in the fields of physics and astronomy. With the development of artificial intelligent, image recognition models based on deep learning are increasingly utilized for pulsar candidate identification. However, pulsar candidate datasets are characterized by unbalance and lack of positive samples, which has contributed the traditional methods to fall into poor performance and model bias. To this end, a general image recognition model based on adversarial training is proposed. A generator, a classifier, and two discriminators are included in the model. Theoretical analysis demonstrates that the model has a unique optimal solution, and the classifier happens to be the inference network of the generator. Therefore, the samples produced by the generator significantly augment the diversity of training data. When the model reaches equilibrium, it can not only predict labels for unseen data, but also generate controllable samples. In experiments, we split part of data from MNIST for training. The results reveal that the model not only behaves better classification performance than CNN, but also has better controllability than CGAN and ACGAN. Then, the model is applied to pulsar candidate dataset HTRU and FAST. The results exhibit that, compared with CNN model, the F-score has increased by 1.99% and 3.67%, and the Recall has also increased by 6.28% and 8.59% respectively.

scholarly journals Analyzing the Effect of Hyper-Parameters on a Neural Network for Emotion Detection

2021 ◽  
pp. 33-40
Author(s):  
Ambrish Jhamnani ◽  
Anshika Tiwari ◽  
Abhishek Soni ◽  
Arpit Deo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Optimal Solution ◽  
Poor Performance ◽  
Activation Function ◽  
Human Face ◽  
Emotion Detection ◽  
Good Balance ◽  
Human Emotion ◽  
The Poor

Human emotion prediction is a tough task. The human face is extremely complex to understand. To build an optimal solution for human emotion prediction model, setting hyper-parameter plays a major role. It is a difficult task to train a neural network. The poor performance of the model can result from poor judgment of sub-optimal hyper- parameters before training the model. This study aims to compare different hyper-parameters and their effect to train the convolutional neural network for emotion detection. We used different methods based on values of validation accuracy and validation loss. The study reveals that SELU activation function performs better in terms of validation accuracy. Swish activation function maintains a good balance between validation accuracy and validation loss. As different combinations of parameters behave differently likewise in optimizers, RMS prop gives less validation loss with Swish whereas Adam performs better with ReLU and ELU activation function.

scholarly journals Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 912
Author(s):  
Wenjuan Mei ◽  
Zhen Liu ◽  
Yuanzhang Su ◽  
Li Du ◽  
Jianguo Huang
Keyword(s):  
Extreme Learning Machine ◽  
Mean Squared Error ◽  
Probability Distributions ◽  
Optimal Solution ◽  
Poor Performance ◽  
Accurate Description ◽  
Local Similarity ◽  
Training Strategy ◽  
Cooperative Evolution ◽  
Learning Machine

In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods.

Machine Translation Evaluation: Unveiling the Role of Dense Sentence Vector Embedding for Morphologically Rich Language

2019 ◽  
Vol 34 (01) ◽  
pp. 2059001
Author(s):  
Samiksha Tripathi ◽  
Vineet Kansal
Keyword(s):  
Machine Translation ◽  
Optimal Solution ◽  
Poor Performance ◽  
Target Language ◽  
Linguistic Knowledge ◽  
Machine Translation Evaluation ◽  
Mt Evaluation ◽  
Morphologically Rich Languages ◽  
Evaluation Metric

Machine Translation (MT) evaluation metrics like BiLingual Evaluation Understudy (BLEU) and Metric for Evaluation of Translation with Explicit Ordering (METEOR) are known to have poor performance for word-order and morphologically rich languages. Application of linguistic knowledge to evaluate MTs for morphologically rich language like Hindi as a target language, is shown to be more effective and accurate [S. Tripathi and V. Kansal, Using linguistic knowledge for machine translation evaluation with Hindi as a target language, Comput. Sist.21(4) (2017) 717–724]. Leveraging the recent progress made in the domain of word vector and sentence vector embedding [T. Mikolov and J. Dean, Distributed representations of words and phrases and their compositionality, Adv. Neural Inf. Process. Syst. 2 (2013) 3111–3119], authors have trained a large corpus of pre-processed Hindi text ([Formula: see text] million tokens) for obtaining the word vectors and sentence vector embedding for Hindi. The training has been performed on high end system configuration utilizing Google Cloud platform resources. This sentence vector embedding is further used to corroborate the findings through linguistic knowledge in evaluation metric. For morphologically rich language as target, evaluation metric of MT systems is considered as an optimal solution. In this paper, authors have demonstrated that MT evaluation using sentence embedding-based approach closely mirrors linguistic evaluation technique. The relevant codes used to generate the vector embedding for Hindi have been uploaded on code sharing platform Github. a

scholarly journals Graph Splitting: A Graph-Based Approach for Superfamily-Scale Phylogenetic Tree Reconstruction

2019 ◽  
Author(s):  
Motomu Matsui ◽  
Wataru Iwasaki
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Triosephosphate Isomerase ◽  
Rna World ◽  
Rapid Evolution ◽  
Poor Performance ◽  
Multiple Sequence ◽  
Data Set ◽  
Protein Superfamily ◽  
Tim Barrel

Abstract A protein superfamily contains distantly related proteins that have acquired diverse biological functions through a long evolutionary history. Phylogenetic analysis of the early evolution of protein superfamilies is a key challenge because existing phylogenetic methods show poor performance when protein sequences are too diverged to construct an informative multiple sequence alignment (MSA). Here, we propose the Graph Splitting (GS) method, which rapidly reconstructs a protein superfamily-scale phylogenetic tree using a graph-based approach. Evolutionary simulation showed that the GS method can accurately reconstruct phylogenetic trees and be robust to major problems in phylogenetic estimation, such as biased taxon sampling, heterogeneous evolutionary rates, and long-branch attraction when sequences are substantially diverge. Its application to an empirical data set of the triosephosphate isomerase (TIM)-barrel superfamily suggests rapid evolution of protein-mediated pyrimidine biosynthesis, likely taking place after the RNA world. Furthermore, the GS method can also substantially improve performance of widely used MSA methods by providing accurate guide trees.

Translations as Interpair Right and Wrong Items in Double-Function Verbal Discrimination Learning

1976 ◽  
Vol 38 (1) ◽  
pp. 247-250
Author(s):  
Robert W. Newby
Keyword(s):  
Discrimination Learning ◽  
Mixed List ◽  
Poor Performance ◽  
Verbal Discrimination ◽  
Superior Performance ◽  
Verbal Discrimination Learning ◽  
Single Function ◽  
Double Function ◽  
Intermediate Performance

60 subjects learned one of five verbal discrimination lists, a Spanish single-function, an English single-function, a mixed double-function, a Spanish double-function, or an English double-function. The results indicated superior performance on the single-function lists, intermediate performance with the mixed list, and poor performance on the two double-function lists. The results indicated that translations are of intermediate similarity when used as interpair right and wrong items in verbal discrimination learning.

Phylogenies and comparative data, a microevolutionary perspective

1995 ◽  
Vol 349 (1327) ◽  
pp. 85-91 ◽  
Keyword(s):  
Phylogenetic Tree ◽  
Related Species ◽  
General Framework ◽  
Comparative Data ◽  
Phenotypic Evolution ◽  
Closely Related Species ◽  
As Species ◽  
Evolutionary Changes ◽  
Relationship Structure ◽  
Degree Of Similarity

As species evolve along a phylogenetic tree, their phenotypes diverge. We expect closely related species to retain some phenotypic similarities owing to their shared evolutionary histories. The degree of similarity depends both on the phylogeny and on the detailed evolutionary changes that accumulate each generation. In this study, I review a general framework that can be used to translate between macroevolutionary patterns and the underlying microevolutionary process by comparing the observed relationships among measured species phenotypes and the expected relationship structure due to the phylogeny and underlying models of phenotypic evolution. I then show how the framework can be used to compare methods used (1) to reconstruct phylogenies, (2) to correct comparative data for phylogenetic non-independence, and (3) to infer details of the microevolutionary process from interspecific data and a phylogeny. Use of this framework and a microevolutionary perspective on the analysis of interspecific data opens up new fields of inquiry and many new uses for phylogenies and comparative data.

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