pwc-archive/papers-with-abstracts huggingface dataset:
Distributionally robust goal-reaching optimization in the presence of background risk
Authors: Yichun Chi, Zuo Quan Xu, Sheng Chao Zhuang
Date: 2021-08-10 00:00:00

In this paper, we examine the effect of background risk on portfolio selection and optimal reinsurance design under the criterion of maximizing the probability of reaching a goal. Following the literature, we adopt dependence uncertainty to model the dependence ambiguity between financial risk (or insurable risk) and background risk. Because the goal-reaching objective function is non-concave, these two problems bring highly unconventional and challenging issues for which classical optimization techniques often fail. Using quantile formulation method, we derive the optimal solutions explicitly. The results show that the presence of background risk does not alter the shape of the solution but instead changes the parameter value of the solution. Finally, numerical examples are given to illustrate the results and verify the robustness of our solutions.
BioCode: A Data-Driven Procedure to Learn the Growth of Biological Networks
Authors: Emre Sefer
Date: 2021-08-10 00:00:00

Probabilistic biological network growth models have been utilized for many tasks including but not limited to capturing mechanism and dynamics of biological growth activities, null model representation, capturing anomalies, etc. Well-known examples of these probabilistic models are Kronecker model, preferential attachment model, and duplication-based model. However, we should frequently keep developing new models to better fit and explain the observed network features while new networks are being observed. Additionally, it is difficult to develop a growth model each time we study a new network. In this paper, we propose BioCode, a framework to automatically discover novel biological growth models matching user-specified graph attributes in directed and undirected biological graphs. BioCode designs a basic set of instructions which are common enough to model a number of well-known biological graph growth models. We combine such instruction-wise representation with a genetic algorithm based optimization procedure to encode models for various biological networks. We mainly evaluate the performance of BioCode in discovering models for biological collaboration networks, gene regulatory networks, metabolic networks, and protein interaction networks which features such as assortativity, clustering coefficient, degree distribution closely match with the true ones in the corresponding real biological networks. As shown by the tests on the simulated graphs, the variance of the distributions of biological networks generated by BioCode is similar to the known models' variance for these biological network types.
The contribution of local variations in hue or contrast to symmetry of things in a thing
Authors: Birgitta Dresp-Langley
Date: 2021-08-10 00:00:00
Tasks: Decision Making, Symmetry Detection

Symmetry contributes to processes of perceptual organization in biological vision and influences the quality and time of goal directed decision making in animals and humans, as discussed in recent work on the examples of symmetry of things in a thing and bilateral shape symmetry. The present study was designed to show that selective chromatic variations in geometric shape configurations with mirror symmetry can be exploited to highlight functional properties of symmetry of things in a thing in human vision. The experimental procedure uses a psychophysical two alternative forced choice technique, where human observers have to decide as swiftly as possible whether two shapes presented simultaneously on a computer screen are symmetrical or not. The stimuli are computer generated 2D shape configurations consisting of multiple elements, with and without systematic variations in local color, color saturation, or achromatic contrast producing variations in symmetry of things in a thing. All stimulus pairs presented had perfect geometric mirror symmetry. The results show that varying the color of local shape elements selectively in multichromatic and monochromatic shapes significantly slows down perceptual response times, which are a direct measure of stimulus uncertainty. It is concluded that local variations in hue or contrast produce functionally meaningful variations in symmetry of things in thing, revealed here as a relevant perceptual variable in symmetry detection. Disturbance of the latter increases stimulus uncertainty and thereby affects the perceptual salience of mirror symmetry in the time course for goal relevant human decisions.
Influence of volatile solids and pH for the production of volatile fatty acids: batch fermentation tests using sewage sludge
Authors: Dario Presti, Alida Cosenza, Fanny Claire Capri, Giuseppe Gallo, Rosa Alduina, Giorgio Mannina
Date: 2021-08-09 00:00:00

The aim of this work was to study the effect of volatile suspended solid (VSS) and pH on volatile fatty acids (VFA) production from waste activated sludge (WAS) fermentation by means of batch tests. The final goal was to gain insights to enhance VFA stream quality, with the novelty of using WAS with high sludge retention time. Results revealed that the optimum conditions to maximize VFAs and minimize nutrients and non-VFA sCOD are a VSS concentration of 5.9 g/L and initial pH adjustment to pH 10. The WAS bacterial community structures were analysed according to Next Generation Sequencing (NGS) of 16S rDNA amplicons. The results revealed changes of bacterial phyla abundance in comparison with the batch test starting condition.
About inevitability of budgetary code receiving for fiscal politics
Authors: George Abuselidze
Date: 2021-08-10 00:00:00

Since the end of 90s till today when all the elements confirming Georgian State System have practically been established, budget system and policy remains as the most difficult Georgian macroeconomics challenge and even still half-and-half unsolved problem. One side of the fiscal policy is quite crucially formulated and administrative Tax Code, and the other side is the weak, unmanaged and incomplete law on Budget System. According to the above-mentioned the elaboration and adoption of the Budget Code having equal force as Tax Code is necessary by which the following are to be determined: excellence of government responsibility when it will not perform the budget obligations specified by the law permanently; the rights and responsibilities of the state, the optimal distribution of the funds mobilized by the tax towards each member of the society. For optimization of the budget system effective correlation between the state, regional and local budgets revenues and expenditures is particularly important as the social-economic development of the regions and territorial units of the country is impossible without the financial relations. For it the just differentiation of tax base in the section of state, regional and local budgets and transfers system for support of the budgets of the territorial units from the central budget are necessary. Solving the most part of these problems is possible by the adoption of the budget code which, in our opinion, is to be considered as the closest decisive task for the current legislative and executive authority.
What if we knew what the future brings? Optimal investment for a frontrunner with price impact
Authors: Peter Bank, Yan Dolinsky, Miklós Rásonyi
Date: 2021-08-09 00:00:00

In this paper we study optimal investment when the investor can peek some time units into the future, but cannot fully take advantage of this knowledge because of quadratic transaction costs. In the Bachelier setting with exponential utility, we give an explicit solution to this control problem with intrinsically infinite-dimensional memory. This is made possible by solving the dual problem where we make use of the theory of Gaussian Volterra integral equations.
Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization
Authors: Pilhyeon Lee, Hyeran Byun
Date: 2021-08-11 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Localization, Temporal Action Localization, Weakly Supervised Action Localization, Weakly-supervised Temporal Action Localization

We tackle the problem of localizing temporal intervals of actions with only a single frame label for each action instance for training. Owing to label sparsity, existing work fails to learn action completeness, resulting in fragmentary action predictions. In this paper, we propose a novel framework, where dense pseudo-labels are generated to provide completeness guidance for the model. Concretely, we first select pseudo background points to supplement point-level action labels. Then, by taking the points as seeds, we search for the optimal sequence that is likely to contain complete action instances while agreeing with the seeds. To learn completeness from the obtained sequence, we introduce two novel losses that contrast action instances with background ones in terms of action score and feature similarity, respectively. Experimental results demonstrate that our completeness guidance indeed helps the model to locate complete action instances, leading to large performance gains especially under high IoU thresholds. Moreover, we demonstrate the superiority of our method over existing state-of-the-art methods on four benchmarks: THUMOS'14, GTEA, BEOID, and ActivityNet. Notably, our method even performs comparably to recent fully-supervised methods, at the 6 times cheaper annotation cost. Our code is available at https://github.com/Pilhyeon.
Creutzfeldt-Jakob Disease Prediction Using Machine Learning Techniques
Authors: Arnav Bhakta, Carolyn Byrne
Date: 2021-08-11 00:00:00
Tasks: BIG-bench Machine Learning, Disease Prediction, Prediction

Creutzfeldt-Jakob disease (CJD) is a rapidly progressive and fatal neurodegenerative disease, that causes approximately 350 deaths in the United States every year. In specific, it is a prion disease that is caused by a misfolded prion protein, termed $PrP^{Sc}$, which is the infectious form of the prion protein $PrP^{C}$. Rather than being recycled by the body, the $PrP^{Sc}$ aggregates in the brain as plaques, leading to neurodegeneration of surrounding cells and the spongiform characteristics of the pathology. However, there has been very little research done into factors that can affect one's chances of acquiring $PrP^{Sc}$. In this paper, Elastic Net Regression, Long Short-Term Memory Recurrent Neural Network Architectures, and Random Forest have been used to predict Creutzfeldt-Jakob Disease Levels in the United States. New variables were created as data for the models to use on the basis of common factors that are known to affect CJD, such as soil, food, and water quality. Based on the root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) values, the study reveals the high impact of unhealthy lifestyle choices, CO$_{2}$ Levels, Pesticide Usage, and Potash K$_{2}$O Usage on CJD Levels. In doing so, the study highlights new avenues of research for CJD prevention and detection, as well as potential causes.
Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning
Authors: Xin Zhou, Yang Li
Date: 2021-08-11 00:00:00

Modeling tap or click sequences of users on a mobile device can improve our understandings of interaction behavior and offers opportunities for UI optimization by recommending next element the user might want to click on. We analyzed a large-scale dataset of over 20 million clicks from more than 4,000 mobile users who opted in. We then designed a deep learning model that predicts the next element that the user clicks given the user's click history, the structural information of the UI screen, and the current context such as the time of the day. We thoroughly investigated the deep model by comparing it with a set of baseline methods based on the dataset. The experiments show that our model achieves 48% and 71% accuracy (top-1 and top-3) for predicting next clicks based on a held-out dataset of test users, which significantly outperformed all the baseline methods with a large margin. We discussed a few scenarios for integrating the model in mobile interaction and how users can potentially benefit from the model.
Person Re-identification via Attention Pyramid
Authors: Guangyi Chen, Tianpei Gu, Jiwen Lu, Jin-An Bao, Jie zhou
Date: 2021-08-11 00:00:00
Tasks: Person Re-Identification

In this paper, we propose an attention pyramid method for person re-identification. Unlike conventional attention-based methods which only learn a global attention map, our attention pyramid exploits the attention regions in a multi-scale manner because human attention varies with different scales. Our attention pyramid imitates the process of human visual perception which tends to notice the foreground person over the cluttered background, and further focus on the specific color of the shirt with close observation. Specifically, we describe our attention pyramid by a "split-attend-merge-stack" principle. We first split the features into multiple local parts and learn the corresponding attentions. Then, we merge local attentions and stack these merged attentions with the residual connection as an attention pyramid. The proposed attention pyramid is a lightweight plug-and-play module that can be applied to off-the-shelf models. We implement our attention pyramid method in two different attention mechanisms including channel-wise attention and spatial attention. We evaluate our method on four largescale person re-identification benchmarks including Market-1501, DukeMTMC, CUHK03, and MSMT17. Experimental results demonstrate the superiority of our method, which outperforms the state-of-the-art methods by a large margin with limited computational cost.
Putting RDF2vec in Order
Authors: Jan Portisch, Heiko Paulheim
Date: 2021-08-11 00:00:00
Tasks: Knowledge Graphs, Position

The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we argue that this might be a shortcoming when training RDF2vec, and show that using a word2vec variant which respects order yields considerable performance gains especially on tasks where entities of different classes are involved.
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness
Authors: Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan
Date: 2021-08-11 00:00:00
Tasks: Entity Alignment, Graph Sampling

Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs. However, most existing EA methods have poor scalability and are unable to cope with large-scale datasets. We summarize three issues leading to such high time-space complexity in existing EA methods: (1) Inefficient graph encoders, (2) Dilemma of negative sampling, and (3) "Catastrophic forgetting" in semi-supervised learning. To address these challenges, we propose a novel EA method with three new components to enable high Performance, high Scalability, and high Robustness (PSR): (1) Simplified graph encoder with relational graph sampling, (2) Symmetric negative-free alignment loss, and (3) Incremental semi-supervised learning. Furthermore, we conduct detailed experiments on several public datasets to examine the effectiveness and efficiency of our proposed method. The experimental results show that PSR not only surpasses the previous SOTA in performance but also has impressive scalability and robustness.
Trading Complexity for Sparsity in Random Forest Explanations
Authors: Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis
Date: 2021-08-11 00:00:00
Proceeding: NeurIPS 2021 12

Random forests have long been considered as powerful model ensembles in machine learning. By training multiple decision trees, whose diversity is fostered through data and feature subsampling, the resulting random forest can lead to more stable and reliable predictions than a single decision tree. This however comes at the cost of decreased interpretability: while decision trees are often easily interpretable, the predictions made by random forests are much more difficult to understand, as they involve a majority vote over hundreds of decision trees. In this paper, we examine different types of reasons that explain "why" an input instance is classified as positive or negative by a Boolean random forest. Notably, as an alternative to sufficient reasons taking the form of prime implicants of the random forest, we introduce majoritary reasons which are prime implicants of a strict majority of decision trees. For these different abductive explanations, the tractability of the generation problem (finding one reason) and the minimization problem (finding one shortest reason) are investigated. Experiments conducted on various datasets reveal the existence of a trade-off between runtime complexity and sparsity. Sufficient reasons - for which the identification problem is DP-complete - are slightly larger than majoritary reasons that can be generated using a simple linear- time greedy algorithm, and significantly larger than minimal majoritary reasons that can be approached using an anytime P ARTIAL M AX SAT algorithm.
DeliData: A dataset for deliberation in multi-party problem solving
Authors: Georgi Karadzhov, Tom Stafford, Andreas Vlachos
Date: 2021-08-11 00:00:00
Tasks: Problem-Solving Deliberation

Group deliberation enables people to collaborate and solve problems, however, it is understudied due to a lack of resources. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a well-established cognitive task, consisting of 500 group dialogues and 14k utterances. In 64% of these conversations, the group members are able to find a better solution than they had identified individually, and in 43.8% of the groups who had a correct answer as their final solution, none of the participants had solved the task correctly by themselves. Furthermore, we propose a novel annotation schema that captures deliberation cues and release all 14k utterances annotated with it. Finally, we use the proposed dataset to develop and evaluate two methods for generating deliberation utterances. The data collection platform, dataset and annotated corpus are publicly available at https://delibot.xyz.
Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
Authors: Jianning Li, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jan Egger
Date: 2021-08-11 00:00:00
Tasks: GPU, Image Generation, Medical Image Analysis

Medical images, especially volumetric images, are of high resolution and often exceed the capacity of standard desktop GPUs. As a result, most deep learning-based medical image analysis tasks require the input images to be downsampled, often substantially, before these can be fed to a neural network. However, downsampling can lead to a loss of image quality, which is undesirable especially in reconstruction tasks, where the fine geometric details need to be preserved. In this paper, we propose that high-resolution images can be reconstructed in a coarse-to-fine fashion, where a deep learning algorithm is only responsible for generating a coarse representation of the image, which consumes moderate GPU memory. For producing the high-resolution outcome, we propose two novel methods: learned voxel rearrangement of the coarse output and hierarchical image synthesis. Compared to the coarse output, the high-resolution counterpart allows for smooth surface triangulation, which can be 3D-printed in the highest possible quality. Experiments of this paper are carried out on the dataset of AutoImplant 2021 (https://autoimplant2021.grand-challenge.org/), a MICCAI challenge on cranial implant design. The dataset contains high-resolution skulls that can be viewed as 2D manifolds embedded in a 3D space. Codes associated with this study can be accessed at https://github.com/Jianningli/voxel_rearrangement.
On the Explanatory Power of Decision Trees
Authors: Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis
Date: 2021-08-11 00:00:00
Proceeding: NeurIPS 2021 12

Decision trees have long been recognized as models of choice in sensitive applications where interpretability is of paramount importance. In this paper, we examine the computational ability of Boolean decision trees in deriving, minimizing, and counting sufficient reasons and contrastive explanations. We prove that the set of all sufficient reasons of minimal size for an instance given a decision tree can be exponentially larger than the size of the input (the instance and the decision tree). Therefore, generating the full set of sufficient reasons can be out of reach. In addition, computing a single sufficient reason does not prove enough in general; indeed, two sufficient reasons for the same instance may differ on many features. To deal with this issue and generate synthetic views of the set of all sufficient reasons, we introduce the notions of relevant features and of necessary features that characterize the (possibly negated) features appearing in at least one or in every sufficient reason, and we show that they can be computed in polynomial time. We also introduce the notion of explanatory importance, that indicates how frequent each (possibly negated) feature is in the set of all sufficient reasons. We show how the explanatory importance of a feature and the number of sufficient reasons can be obtained via a model counting operation, which turns out to be practical in many cases. We also explain how to enumerate sufficient reasons of minimal size. We finally show that, unlike sufficient reasons, the set of all contrastive explanations for an instance given a decision tree can be derived, minimized and counted in polynomial time.
Approximating Defeasible Logics to Improve Scalability
Authors: Michael J. Maher
Date: 2021-08-11 00:00:00
Tasks: valid

Defeasible rules are used in providing computable representations of legal documents and, more recently, have been suggested as a basis for explainable AI. Such applications draw attention to the scalability of implementations. The defeasible logic $DL(\partial_{||})$ was introduced as a more scalable alternative to $DL(\partial)$, which is better known. In this paper we consider the use of (implementations of) $DL(\partial_{||})$ as a computational aid to computing conclusions in $DL(\partial)$ and other defeasible logics, rather than as an alternative to $DL(\partial)$. We identify conditions under which $DL(\partial_{||})$ can be substituted for $DL(\partial)$ with no change to the conclusions drawn, and conditions under which $DL(\partial_{||})$ can be used to draw some valid conclusions, leaving the remainder to be drawn by $DL(\partial)$.