pwc-archive/papers-with-abstracts huggingface dataset:
Integrated Satellite-HAP-Terrestrial Networks for Dual-Band Connectivity
Authors: Wenwei Zhang, Ruoqi Deng, Boya Di, Lingyang Song
Date: 2021-07-06 00:00:00

The recent development of high-altitude platforms (HAPs) has attracted increasing attention since they can serve as a promising communication method to assist satellite-terrestrial networks. In this paper, we consider an integrated three-layer satellite-HAP-terrestrial network where the HAP support dual-band connectivity. Specifically, the HAP can not only communicate with terrestrial users over C-band directly, but also provide backhaul services to terrestrial user terminals over Ka-band. We formulate a sum-rate maximization problem and then propose a fractional programming based algorithm to solve the problem by optimizing the bandwidth and power allocation iteratively. The closed-form optimal solutions for bandwidth allocation and power allocation in each iteration are also derived. Simulation results show the capacity enhancement brought by the dual-band connectivity of the HAP. The influence of the power of the HAP and the power of the satellite is also discussed.
A Unified Theory of Adaptive Subspace Detection. Part I: Detector Designs
Authors: Danilo Orlando, Giuseppe Ricci, Louis L. Scharf
Date: 2021-07-05 00:00:00

This paper addresses the problem of detecting multidimensional subspace signals, which model range-spread targets, in noise of unknown covariance. It is assumed that a primary channel of measurements, possibly consisting of signal plus noise, is augmented with a secondary channel of measurements containing only noise. The noises in these two channels share a common covariance matrix, up to a scale, which may be known or unknown. The signal model is a subspace model with variations: the subspace may be known or known only by its dimension; consecutive visits to the subspace may be unconstrained or they may be constrained by a prior distribution. As a consequence, there are four general classes of detectors and, within each class, there is a detector for the case where the scale between the primary and secondary channels is known, and for the case where this scale is unknown. The generalized likelihood ratio (GLR) based detectors derived in this paper, when organized with previously published GLR detectors, comprise a unified theory of adaptive subspace detection from primary and secondary channels of measurements.
A dynamic version of the super-replication theorem under proportional transaction costs
Authors: Francesca Biagini, Thomas Reitsam
Date: 2021-07-06 00:00:00

We extend the super-replication theorems of [27] in a dynamic setting, both in the num\'eraire-based as well as in the num\'eraire-free setting. For this purpose, we generalize the notion of admissible strategies. In particular, we obtain a well-defined super-replication price process, which is right-continuous under some regularity assumptions.
Approximations to ultimate ruin probabilities with a Wienner process perturbation
Authors: Yacine Koucha, Alfredo D. Egidio dos Reis
Date: 2021-07-06 00:00:00

In this paper, we adapt the classic Cram\'er-Lundberg collective risk theory model to a perturbed model by adding a Wiener process to the compound Poisson process, which can be used to incorporate premium income uncertainty, interest rate fluctuations and changes in the number of policyholders. Our study is part of a Master dissertation, our aim is to make a short overview and present additionally some new approximation methods for the infinite time ruin probabilities for the perturbed risk model. We present four different approximation methods for the perturbed risk model. The first method is based on iterative upper and lower approximations to the maximal aggregate loss distribution. The second method relies on a four-moment exponential De Vylder approximation. The third method is based on the first-order Pad\'e approximation of the Renyi and De Vylder approximations. The last method is the second order Pad\'e-Ramsay approximation. These are generated by fitting one, two, three or four moments of the claim amount distribution, which greatly generalizes the approximations. We test the precision of approximations using a combination of light and heavy tailed distributions for the individual claim amount. We assess the ultimate ruin probability and present numerical results for the exponential, gamma, and mixed exponential claim distributions, demonstrating the high accuracy of these four methods. Analytical and numerical methods are used to highlight the practical implications of our findings.
STDP-based Associative Memory Formation and Retrieval
Authors: Hong-Gyu Yoon, Pilwon Kim
Date: 2021-07-06 00:00:00
Tasks: Retrieval

Spike-timing-dependent plasticity(STDP) is a biological process in which the precise order and timing of neuronal spikes affect the degree of synaptic modification. While there have been numerous research focusing on the role of STDP in neural coding, the functional implications of STDP at the macroscopic level in the brain have not been fully explored yet. In this work, we propose a neurodynamical model based on STDP that renders storage and retrieval of a group of associative memories. We showed that the function of STDP at the macroscopic level is to form a "memory plane" in the neural state space which dynamically encodes high dimensional data. We derived the analytic relation between the input, the memory plane, and the induced macroscopic neural oscillations around the memory plane. Such plane produces a limit cycle in reaction to a similar memory cue, which can be used for retrieval of the original input.
Metapopulation dynamics of a respiratory disease with infection during travel
Authors: Indrajit Ghosh, Sk Shahid Nadim, Soumyendu Raha, Debnath Pal
Date: 2021-07-06 00:00:00

We formulate a compartmental model for the propagation of a respiratory disease in a patchy environment. The patches are connected through the mobility of individuals, and we assume that disease transmission and recovery are possible during travel. Moreover, the migration terms are assumed to depend on the distance between patches and the perceived severity of the disease. The positivity and boundedness of the model solutions are discussed. We analytically show the existence and global asymptotic stability of the disease-free equilibrium. We study three different network topologies numerically and find that underlying network structure is crucial for disease transmission. Further numerical simulations reveal that infection during travel has the potential to change the stability of disease-free equilibrium from stable to unstable. The coupling strength and transmission coefficients are also very crucial in disease propagation. Different exit screening scenarios indicate that the patch with the highest prevalence may have adverse effects but other patches will be benefited from exit screening. Furthermore, while studying the multi-strain dynamics, it is observed that two co-circulating strains will not persist simultaneously in the community but only one of the strains may persist in the long run. Transmission coefficients corresponding to the second strain are very crucial and show threshold like behavior with respect to the equilibrium density of the second strain.
Two Stochastic Control Problems In Capital Structure and Portfolio Choice
Authors: Shan Huang
Date: 2021-07-05 00:00:00
Tasks: Vocal Bursts Valence Prediction

This thesis mainly focuses on two problems in capital structure and individual's life-cycle portfolio choice. In the first problem, we derive a stochastic control model to optimize banks' dividend and recapitalization policies and calibrate that to a sample of U.S. banks in the situation where we model banks' true accounting asset values as partially observed variables due to the opaqueness in banks' assets. By the calibrated model, the noise in reported accounting asset values hides about one-third of the true asset return volatility and raises the banks' market equity value by 7.8\% because the noise hides the banks' solvency risk from banking regulators. Particularly, those banks with a high level of loan loss provisions, nonperforming assets, and real estate loans, and with a low volatility of reported total assets have noisy accounting asset values. Because of the substantial shock on the true asset values, the banks' assets were more opaque during the recent financial crisis. In the second problem, we present an optimal portfolio selection model with voluntary retirement option in an economic situation, where an investor is facing borrowing and short sale constraints, as well as the cointegration between the stock and labor markets. Our model reinterprets the non-participation puzzle in stock investment and early retirement in market booms. Investor's willingness to retire earlier becomes stronger as risk aversion increases or as wages decline in the long term. Consistent with the empirical evidence, we find that retirement flexibility makes the optimal portfolio invest less in the stock market. We also find that our model-generated portfolio share rises in wealth.
Linear Segmentation and Segment Significance
Authors: Min-Yen Kan, Judith L. Klavans, Kathleen R. McKeown
Date: 1998-09-15 00:00:00
Tasks: Retrieval, Segmentation

We present a new method for discovering a segmental discourse structure of a document while categorizing segment function. We demonstrate how retrieval of noun phrases and pronominal forms, along with a zero-sum weighting scheme, determines topicalized segmentation. Futhermore, we use term distribution to aid in identifying the role that the segment performs in the document. Finally, we present results of evaluation in terms of precision and recall which surpass earlier approaches.
Deep Extrapolation for Attribute-Enhanced Generation
Authors: Alvin Chan, Ali Madani, Ben Krause, Nikhil Naik
Date: 2021-07-07 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Attribute

Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution. We formulate a new task for extrapolation in sequence generation, focusing on natural language and proteins, and propose GENhance, a generative framework that enhances attributes through a learned latent space. Trained on movie reviews and a computed protein stability dataset, GENhance can generate strongly-positive text reviews and highly stable protein sequences without being exposed to similar data during training. We release our benchmark tasks and models to contribute to the study of generative modeling extrapolation and data-driven design in biology and chemistry.
Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation
Authors: Bingchen Zhao, Kai Han
Date: 2021-07-07 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Fine-Grained Visual Recognition, Knowledge Distillation

In this paper, we tackle the problem of novel visual category discovery, i.e., grouping unlabelled images from new classes into different semantic partitions by leveraging a labelled dataset that contains images from other different but relevant categories. This is a more realistic and challenging setting than conventional semi-supervised learning. We propose a two-branch learning framework for this problem, with one branch focusing on local part-level information and the other branch focusing on overall characteristics. To transfer knowledge from the labelled data to the unlabelled, we propose using dual ranking statistics on both branches to generate pseudo labels for training on the unlabelled data. We further introduce a mutual knowledge distillation method to allow information exchange and encourage agreement between the two branches for discovering new categories, allowing our model to enjoy the benefits of global and local features. We comprehensively evaluate our method on public benchmarks for generic object classification, as well as the more challenging datasets for fine-grained visual recognition, achieving state-of-the-art performance.
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions
Authors: Tianbo Li, Tianze Luo, Yiping Ke, Sinno Jialin Pan
Date: 2021-07-07 00:00:00
Tasks: Model Selection, Point Processes

Attributed event sequences are commonly encountered in practice. A recent research line focuses on incorporating neural networks with the statistical model -- marked point processes, which is the conventional tool for dealing with attributed event sequences. Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks. However, we find that performance of neural marked point processes is not always increasing as the network architecture becomes more complicated and larger, which is what we call the performance saturation phenomenon. This is due to the fact that the generalization error of neural marked point processes is determined by both the network representational ability and the model specification at the same time. Therefore we can draw two major conclusions: first, simple network structures can perform no worse than complicated ones for some cases; second, using a proper probabilistic assumption is as equally, if not more, important as improving the complexity of the network. Based on this observation, we propose a simple graph-based network structure called GCHP, which utilizes only graph convolutional layers, thus it can be easily accelerated by the parallel mechanism. We directly consider the distribution of interarrival times instead of imposing a specific assumption on the conditional intensity function, and propose to use a likelihood ratio loss with a moment matching mechanism for optimization and model selection. Experimental results show that GCHP can significantly reduce training time and the likelihood ratio loss with interarrival time probability assumptions can greatly improve the model performance.
SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
Authors: Xin Zhou, Aixin Sun, Yong liu, Jie Zhang, Chunyan Miao
Date: 2021-07-07 00:00:00
Tasks: Collaborative Filtering, Self-Supervised Learning

Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. Training with negative sampling on large datasets is computationally expensive. Further, negative items should be carefully sampled under the defined distribution, in order to avoid selecting an observed positive item in the training dataset. Unavoidably, some negative items sampled from the training dataset could be positive in the test set. In this paper, we propose a self-supervised collaborative filtering framework (SelfCF), that is specially designed for recommender scenario with implicit feedback. The proposed SelfCF framework simplifies the Siamese networks and can be easily applied to existing deep-learning based CF models, which we refer to as backbone networks. The main idea of SelfCF is to augment the output embeddings generated by backbone networks, because it is infeasible to augment raw input of user/item ids. We propose and study three output perturbation techniques that can be applied to different types of backbone networks including both traditional CF models and graph-based models. The framework enables learning informative representations of users and items without negative samples, and is agnostic to the encapsulated backbones. We conduct comprehensive experiments on four datasets to show that our framework may achieve even better recommendation accuracy than the encapsulated supervised counterpart with a 2$\times$--4$\times$ faster training speed. We also show that SelfCF can boost up the accuracy by up to 17.79% on average, compared with a self-supervised framework BUIR.
Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation
Authors: Jia Li, Linhua Xiang, Jiwei Chen, Zengfu Wang
Date: 2021-07-07 00:00:00
Tasks: 2D Human Pose Estimation, Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation

We propose a simple yet reliable bottom-up approach with a good trade-off between accuracy and efficiency for the problem of multi-person pose estimation. Given an image, we employ an Hourglass Network to infer all the keypoints from different persons indiscriminately as well as the guiding offsets connecting the adjacent keypoints belonging to the same persons. Then, we greedily group the candidate keypoints into multiple human poses (if any), utilizing the predicted guiding offsets. And we refer to this process as greedy offset-guided keypoint grouping (GOG). Moreover, we revisit the encoding-decoding method for the multi-person keypoint coordinates and reveal some important facts affecting accuracy. Experiments have demonstrated the obvious performance improvements brought by the introduced components. Our approach is comparable to the state of the art on the challenging COCO dataset under fair conditions. The source code and our pre-trained model are publicly available online.
Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer
Authors: Xiaodong Wang, Junbao Zhuo, Shuhao Cui, Shuhui Wang
Date: 2021-07-07 00:00:00
Tasks: Diversity, Domain Adaptation, Semi-supervised Domain Adaptation

Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain by utilizing transferable information learned from the available source domain and a few labeled target data. However, source data is not always accessible in practical scenarios, which restricts the application of SSDA in real world circumstances. In this paper, we propose a novel task named Semi-supervised Source Hypothesis Transfer (SSHT), which performs domain adaptation based on source trained model, to generalize well in target domain with a few supervisions. In SSHT, we are facing two challenges: (1) The insufficient labeled target data may result in target features near the decision boundary, with the increased risk of mis-classification; (2) The data are usually imbalanced in source domain, so the model trained with these data is biased. The biased model is prone to categorize samples of minority categories into majority ones, resulting in low prediction diversity. To tackle the above issues, we propose Consistency and Diversity Learning (CDL), a simple but effective framework for SSHT by facilitating prediction consistency between two randomly augmented unlabeled data and maintaining the prediction diversity when adapting model to target domain. Encouraging consistency regularization brings difficulty to memorize the few labeled target data and thus enhances the generalization ability of the learned model. We further integrate Batch Nuclear-norm Maximization into our method to enhance the discriminability and diversity. Experimental results show that our method outperforms existing SSDA methods and unsupervised model adaptation methods on DomainNet, Office-Home and Office-31 datasets. The code is available at https://github.com/Wang-xd1899/SSHT.
Regularization-based Continual Learning for Fault Prediction in Lithium-Ion Batteries
Authors: Benjamin Maschler, Sophia Tatiyosyan, Michael Weyrich
Date: 2021-07-07 00:00:00
Tasks: Continual Learning

In recent years, the use of lithium-ion batteries has greatly expanded into products from many industrial sectors, e.g. cars, power tools or medical devices. An early prediction and robust understanding of battery faults could therefore greatly increase product quality in those fields. While current approaches for data-driven fault prediction provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in operational or environmental parameters. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated and compared based on a real battery wear dataset. Online elastic weight consolidation delivers the best results, but, as with all examined approaches, its performance appears to be strongly dependent on task characteristics and task sequence.
Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models
Authors: Timo Müller, Benjamin Lindemann, Tobias Jung, Nasser Jazdi, Michael Weyrich
Date: 2021-07-07 00:00:00
Tasks: Management

Shorter product life cycles and increasing individualization of production leads to an increased reconfiguration demand in the domain of industrial automation systems, which will be dominated by cyber-physical production systems in the future. In constantly changing systems, however, not all configuration alternatives of the almost infinite state space are fully understood. Thus, certain configurations can lead to process instability, a reduction in quality or machine failures. Therefore, this paper presents an approach that enhances an intelligent Digital Twin with a self-organized reconfiguration management based on adaptive process models in order to find optimized configurations more comprehensively.
Introducing the structural bases of typicality effects in deep learning
Authors: Omar Vidal Pino, Erickson Rangel Nascimento, Mario Fernando Montenegro Campos
Date: 2021-07-07 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, Transfer Learning

In this paper, we hypothesize that the effects of the degree of typicality in natural semantic categories can be generated based on the structure of artificial categories learned with deep learning models. Motivated by the human approach to representing natural semantic categories and based on the Prototype Theory foundations, we propose a novel Computational Prototype Model (CPM) to represent the internal structure of semantic categories. Unlike other prototype learning approaches, our mathematical framework proposes a first approach to provide deep neural networks with the ability to model abstract semantic concepts such as category central semantic meaning, typicality degree of an object's image, and family resemblance relationship. We proposed several methodologies based on the typicality's concept to evaluate our CPM-model in image semantic processing tasks such as image classification, a global semantic description, and transfer learning. Our experiments on different image datasets, such as ImageNet and Coco, showed that our approach might be an admissible proposition in the effort to endow machines with greater power of abstraction for the semantic representation of objects' categories.
Contrastive Explanations for Argumentation-Based Conclusions
Authors: AnneMarie Borg, Floris Bex
Date: 2021-07-07 00:00:00

In this paper we discuss contrastive explanations for formal argumentation - the question why a certain argument (the fact) can be accepted, whilst another argument (the foil) cannot be accepted under various extension-based semantics. The recent work on explanations for argumentation-based conclusions has mostly focused on providing minimal explanations for the (non-)acceptance of arguments. What is still lacking, however, is a proper argumentation-based interpretation of contrastive explanations. We show under which conditions contrastive explanations in abstract and structured argumentation are meaningful, and how argumentation allows us to make implicit foils explicit.