pwc-archive/papers-with-abstracts huggingface dataset:
A Framework for Measures of Risk under Uncertainty
Authors: Tolulope Fadina, Yang Liu, Ruodu Wang
Date: 2021-10-20 00:00:00
Tasks: Management

A risk analyst assesses potential financial losses based on multiple sources of information. Often, the assessment does not only depend on the specification of the loss random variable but also various economic scenarios. Motivated by this observation, we design a unified axiomatic framework for risk evaluation principles which quantifies jointly a loss random variable and a set of plausible probabilities. We call such an evaluation principle a generalized risk measure. We present a series of relevant theoretical results. The worst-case, coherent, and robust generalized risk measures are characterized via different sets of intuitive axioms. We establish the equivalence between a few natural forms of law invariance in our framework, and the technical subtlety therein reveals a sharp contrast between our framework and the traditional one. Moreover, coherence and strong law invariance are derived from a combination of other conditions, which provides additional support for coherent risk measures such as Expected Shortfall over Value-at-Risk, a relevant issue for risk management practice.
On the reproducibility of enzyme reactions and kinetic modelling
Authors: Gudrun Gygli
Date: 2021-10-20 00:00:00

Enzyme reactions are highly dependent on reaction conditions. To ensure reproducibility of enzyme reaction parameters, experiments need to be carefully designed and kinetic modelling meticulously executed. Furthermore, to enable the judgement of the quality of enzyme reaction parameters, the experimental conditions, the modelling process as well as the raw data need to be reported comprehensively. By taking these steps, enzyme reaction parameters can be open and FAIR (findable, accessible, interoperable, re-usable) as well as repeatable, replicable and reproducible. This review discusses these issues and provides a practical guide to designing initial rate experiments for the determination of enzyme reaction parameters and gives an open, FAIR and re-editable example of the kinetic modelling of an enzyme reaction. Both the guide and example are scripted with Python in Jupyter Notebooks and are publicly available (https://fairdomhub.org/investigations/483). Finally, the prerequisites of automated data analysis and machine learning algorithms are briefly discussed to provide further motivation for the comprehensive, open and FAIR reporting of enzyme reaction parameters.
New advances in the automation of context-aware information selection and guided model assembly
Authors: Yasmine Ahmed, Adam A Butchy, Khaled Sayed, Cheryl Telmer, Natasa Miskov-Zivanov
Date: 2021-10-21 00:00:00

The automated assembly and extension of dynamic network models using information extracted from literature are challenging due to the amount and inconsistency in published literature. Recently, efforts have been made to automatically and efficiently assemble the information extracted from literature into models. In this review, we summarize the basic concept, performance, advantages, and limitations of five automated extension methods. Each method was tested for its ability to reconstruct a model of T-cell differentiation as compared against a number of predefined system properties.
Distribution of interspike intervals of a neuron with inhibitory autapse stimulated with a renewal process
Authors: Olha Shchur, Alexander Vidybida
Date: 2021-10-21 00:00:00

In this paper, we study analytically the impact of an inhibitory autapse on neuronal activity. In order to do this, we formulate conditions on a set of non-adaptive spiking neuron models with delayed feedback inhibition, instead of considering a particular neuronal model. The neuron is stimulated with a stochastic point renewal process of excitatory impulses. Probability density function (PDF) $p(t)$ of output interspike intervals (ISIs) of such a neuron is found exactly without any approximations made. It is expressed in terms of ISIs PDF for the input renewal stream and ISIs PDF for that same neuron without any feedback. Obtained results are applied to a subset of neuronal models with threshold 2 when the time intervals between input impulses are distributed according to the Erlang-2 distribution. In that case we have found explicitly the model-independent initial part of ISIs PDF $p(t)$ defined at some initial interval $[0;T_2]$ of ISI values.
Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory
Authors: Alpay Tekin, Ahmed Nebli, Islem Rekik
Date: 2021-10-06 00:00:00
Tasks: Graph Neural Network

Several brain disorders can be detected by observing alterations in the brain's structural and functional connectivities. Neurological findings suggest that early diagnosis of brain disorders, such as mild cognitive impairment (MCI), can prevent and even reverse its development into Alzheimer's disease (AD). In this context, recent studies aimed to predict the evolution of brain connectivities over time by proposing machine learning models that work on brain images. However, such an approach is costly and time-consuming. Here, we propose to use brain connectivities as a more efficient alternative for time-dependent brain disorder diagnosis by regarding the brain as instead a large interconnected graph characterizing the interconnectivity scheme between several brain regions. We term our proposed method Recurrent Brain Graph Mapper (RBGM), a novel efficient edge-based recurrent graph neural network that predicts the time-dependent evaluation trajectory of a brain graph from a single baseline. Our RBGM contains a set of recurrent neural network-inspired mappers for each time point, where each mapper aims to project the ground-truth brain graph onto its next time point. We leverage the teacher forcing method to boost training and improve the evolved brain graph quality. To maintain the topological consistency between the predicted brain graphs and their corresponding ground-truth brain graphs at each time point, we further integrate a topological loss. We also use l1 loss to capture time-dependency and minimize the distance between the brain graph at consecutive time points for regularization. Benchmarks against several variants of RBGM and state-of-the-art methods prove that we can achieve the same accuracy in predicting brain graph evolution more efficiently, paving the way for novel graph neural network architecture and a highly efficient training scheme.
Lewontin (1972)
Authors: Rasmus Grønfeldt Winther
Date: 2021-10-21 00:00:00
Tasks: Anatomy, ARC, Diversity

Richard C. Lewontin is arguably the most influential evolutionary biologist of the second half of the 20th century. In this chapter, I provide two windows on his influential 1972 article "The Apportionment of Human Diversity": First, I show how the fourteen publications that he cites influenced him and framed his exploration; second, I present close readings of the five sections of the article: "Introduction," "The Genes," "The Samples," "The Measure of Diversity," and "The Results." I hope to illuminate the article's basic anatomy and argumentative arc, and why it became such a historically important document. In particular, I make explicit all of the mathematics (e.g., six Shannon information measures) and the general population genetic theory underlying this mathematics (e.g., the Wahlund effect). Lewontin did not make this explicit in his article. Furthermore, in redoing all of his calculations, I find that Lewontin made calculation errors (including rounding errors or omitting diversity component values) for all the genes he analyzed except one (P), and understated the among races diversity component, according to even just his own calculations. In reproducing the original computation, I find that the values of, respectively, within populations, among populations but within races, and among races diversity apportionments shift slightly (86%, 7%, 7%); here, in this "field guide" to Lewontin (1972), as well as in Winther (2022), I discuss this change in light of the values produced in subsequent replications of Lewontin's calculation with other statistics and data sets.
Ecological landscapes guide the assembly of optimal microbial communities
Authors: Ashish B. George, Kirill S. Korolev
Date: 2021-10-21 00:00:00
Tasks: Heuristic Search

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia.
Sharp thresholds limit the benefit of defector avoidance in cooperation on networks
Authors: Ashkaan K. Fahimipour, Fanqi Zeng, Martin Homer, Arne Traulsen, Simon A. Levin, Thilo Gross
Date: 2021-10-21 00:00:00

Consider a cooperation game on a spatial network of habitat patches, where players can relocate between patches if they judge the local conditions to be unfavorable. In time, the relocation events may lead to a homogeneous state where all patches harbor the same relative densities of cooperators and defectors or they may lead to self-organized patterns, where some patches become safe havens that maintain an elevated cooperator density. Here we analyze the transition between these states mathematically. We show that safe havens form once a certain threshold in connectivity is crossed. This threshold can be analytically linked to the structure of the patch network and specifically to certain network motifs. Surprisingly, a forgiving defector avoidance strategy may be most favorable for cooperators. Our results demonstrate that the analysis of cooperation games in ecological metacommunity models is mathematically tractable and has the potential to link topics such as macroecological patterns, behavioral evolution, and network topology.
Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-based Virtual Screening
Authors: Wei Ma, Qin Xie, Jianhang Zhang, Shiliang Li, Youjun Xu, Xiaobing Deng, Weilin Zhang
Date: 2021-10-21 00:00:00

Docking-based virtual screening (VS process) selects ligands with potential pharmacological activities from millions of molecules using computational docking methods, which greatly could reduce the number of compounds for experimental screening, shorten the research period and save the research cost. Howerver, a majority of compouds with low docking scores could waste most of the computational resources. Herein, we report a novel and practical docking-based machine learning method called MLDDM (Machince Learning Docking-by-Docking Models). It is composed of a regression model and a classification model that simulates a classical docking by docking protocol ususally applied in many virtual screening projects. MLDDM could quickly eliminate compounds with low docking scores and the retained compounds with potential high docking scores would be examined for further real docking program. We demonstrated that MLDDM has a good ability to identify active compounds in the case studies for 10 specific protein targets. Compared to pure docking by docking based VS protocol, the VS process with MLDDM can achieve an over 120 times speed increment on average and the consistency rate with corresponding docking by docking VS protocol is above 0.8. Therefore, it would be promising to be used for examing ultra-large compound libraries in the current big data era.
Media abnormal tone, earnings announcements, and the stock market
Authors: David Ardia, Keven Bluteau, Kris Boudt
Date: 2021-10-20 00:00:00
Tasks: Articles, Relation

We conduct a tone-based event study to examine the aggregate abnormal tone dynamics in media articles around earnings announcements. We test whether they convey incremental information that is useful for price discovery for nonfinancial S&P 500 firms. The relation we find between the abnormal tone and abnormal returns suggests that media articles provide incremental information relative to the information contained in earnings press releases and earnings calls.
A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems
Authors: Arpan Biswas, Claudio Fuentes, Christopher Hoyle
Date: 2021-10-16 00:00:00
Tasks: Bayesian Optimization, Model Selection, parameter estimation, regression

We propose a nested weighted Tchebycheff Multi-objective Bayesian optimization framework where we build a regression model selection procedure from an ensemble of models, towards better estimation of the uncertain parameters of the weighted-Tchebycheff expensive black-box multi-objective function. In existing work, a weighted Tchebycheff MOBO approach has been demonstrated which attempts to estimate the unknown utopia in formulating acquisition function, through calibration using a priori selected regression model. However, the existing MOBO model lacks flexibility in selecting the appropriate regression models given the guided sampled data and therefore, can under-fit or over-fit as the iterations of the MOBO progress, reducing the overall MOBO performance. As it is too complex to a priori guarantee a best model in general, this motivates us to consider a portfolio of different families of predictive models fitted with current training data, guided by the WTB MOBO; the best model is selected following a user-defined prediction root mean-square-error-based approach. The proposed approach is implemented in optimizing a multi-modal benchmark problem and a thin tube design under constant loading of temperature-pressure, with minimizing the risk of creep-fatigue failure and design cost. Finally, the nested weighted Tchebycheff MOBO model performance is compared with different MOBO frameworks with respect to accuracy in parameter estimation, Pareto-optimal solutions and function evaluation cost. This method is generalized enough to consider different families of predictive models in the portfolio for best model selection, where the overall design architecture allows for solving any high-dimensional (multiple functions) complex black-box problems and can be extended to any other global criterion multi-objective optimization methods where prior knowledge of utopia is required.
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation
Authors: Saurav Jha
Date: 2021-09-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Machine Translation, NMT, speech-recognition, Speech Recognition, Translation

Automatic Speech Recognition (ASR) systems have been gaining popularity in the recent years for their widespread usage in smart phones and speakers. Building ASR systems for task-specific scenarios is subject to the availability of utterances that adhere to the style of the task as well as the language in question. In our work, we target such a scenario wherein task-specific text data is available in a language that is different from the target language in which an ASR Language Model (LM) is expected. We use Neural Machine Translation (NMT) as an intermediate step to first obtain translations of the task-specific text data. We then train LMs on the 1-best and N-best translations and study ways to improve on such a baseline LM. We develop a procedure to derive word confusion networks from NMT beam search graphs and evaluate LMs trained on these confusion networks. With experiments on the WMT20 chat translation task dataset, we demonstrate that NMT confusion networks can help to reduce the perplexity of both n-gram and recurrent neural network LMs compared to those trained only on N-best translations.
A guided journey through non-interactive automatic story generation
Authors: Luis Miguel Botelho
Date: 2021-10-08 00:00:00
Tasks: Evolutionary Algorithms, Story Generation

We present a literature survey on non-interactive computational story generation. The article starts with the presentation of requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing. Then it reviews each class of story generation approach depending on the used technology: story-schemas, analogy, rules, planning, evolutionary algorithms, implicit knowledge learning, and explicit knowledge learning. Before the concluding section, the article analyses the contributions of the reviewed work to improve the quality of the generated stories. This analysis addresses the description of the story characters, the use of narrative knowledge including about character believability, and the possible lack of more comprehensive or more detailed knowledge or creativity models. Finally, the article presents concluding remarks in the form of suggestions of research topics that might have a significant impact on the advancement of the state of the art on autonomous non-interactive story generation systems. The article concludes that the autonomous generation and adoption of the main idea to be conveyed and the autonomous design of the creativity ensuring criteria are possibly two of most important topics for future research.
S2D2Net: An Improved Approach For Robust Steel Surface Defects Diagnosis With Small Sample Learning
Authors: Vikanksh Nath, Chiranjoy Chattopadhyay
Date: 2021-08-23 00:00:00
Proceeding: IEEE International Conference on Image Processing (ICIP) 2021 8
Tasks: Defect Detection, Diversity, Robust classification, Small Data Image Classification, Weakly Supervised Defect Detection

Surface defect recognition of products is a necessary process to guarantee the quality of industrial production. This paper proposes a hybrid model, S2D2Net (Steel Surface Defect Diagnosis Network), for an efficient and robust inspection of the steel surface during the manufacturing process. The S2D2Net uses a pretrained ImageNet model as a feature extractor and learns a Capsule Network over the extracted features. The experimental results on a publicly available steel surface defect dataset (NEU) show that S2D2Net achieved 99.17% accuracy with minimal training data and improved by 9.59% over its closest competitor based on GAN. S2D2Net proved its robustness by achieving 94.7% accuracy on a diversity enhanced dataset, ENEU, and improved by 3.6% over its closest competitor. It has better, robust recognition performance compared to other state-of-the-art DNN-based detectors.
Bayesian Logic Programs
Authors: Kristian Kersting, Luc De Raedt
Date: 2001-11-23 00:00:00

Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developed
WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy
Authors: Zhuotao Lian, Qinglin Yang, Qingkui Zeng, Chunhua Su
Date: 2021-10-22 00:00:00
Tasks: Federated Learning

For data isolated islands and privacy issues, federated learning has been extensively invoking much interest since it allows clients to collaborate on training a global model using their local data without sharing any with a third party. However, the existing federated learning frameworks always need sophisticated condition configurations (e.g., sophisticated driver configuration of standalone graphics card like NVIDIA, compile environment) that bring much inconvenience for large-scale development and deployment. To facilitate the deployment of federated learning and the implementation of related applications, we innovatively propose WebFed, a novel browser-based federated learning framework that takes advantage of the browser's features (e.g., Cross-platform, JavaScript Programming Features) and enhances the privacy protection via local differential privacy mechanism. Finally, We conduct experiments on heterogeneous devices to evaluate the performance of the proposed WebFed framework.
Voltage Stabilization of A DC-Microgrid Using ANFIS Controller Considering EVs, DER, and Transient Storage
Authors: Hussein Zolfaghari, Hossein Karimi, Dr. Hamidreza Momeni
Date: 2021-10-22 00:00:00

In this paper, a DC microgrid will be considered to optimize the operation of this microgrid under a combination of Fuzzy and metaheuristic algorithms. Keywords: DC Microgrid, Fuzzy Inference System, Stochastic Power Resource, Particle Swarm Optimization, Battery, Electrical Vehicles, DERs
SOFT: Softmax-free Transformer with Linear Complexity
Authors: Jiachen Lu, Jinghan Yao, Junge Zhang, Xiatian Zhu, Hang Xu, Weiguo Gao, Chunjing Xu, Tao Xiang, Li Zhang
Date: 2021-10-22 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Computational Efficiency

Vision transformers (ViTs) have pushed the state-of-the-art for various visual recognition tasks by patch-wise image tokenization followed by self-attention. However, the employment of self-attention modules results in a quadratic complexity in both computation and memory usage. Various attempts on approximating the self-attention computation with linear complexity have been made in Natural Language Processing. However, an in-depth analysis in this work shows that they are either theoretically flawed or empirically ineffective for visual recognition. We further identify that their limitations are rooted in keeping the softmax self-attention during approximations. Specifically, conventional self-attention is computed by normalizing the scaled dot-product between token feature vectors. Keeping this softmax operation challenges any subsequent linearization efforts. Based on this insight, for the first time, a softmax-free transformer or SOFT is proposed. To remove softmax in self-attention, Gaussian kernel function is used to replace the dot-product similarity without further normalization. This enables a full self-attention matrix to be approximated via a low-rank matrix decomposition. The robustness of the approximation is achieved by calculating its Moore-Penrose inverse using a Newton-Raphson method. Extensive experiments on ImageNet show that our SOFT significantly improves the computational efficiency of existing ViT variants. Crucially, with a linear complexity, much longer token sequences are permitted in SOFT, resulting in superior trade-off between accuracy and complexity.