pwc-archive/papers-with-abstracts huggingface dataset:
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification
Authors: Güray Hatipoğlu
Date: 2022-04-27 00:00:00
Tasks: Dimensionality Reduction, Management, Time Series, Time Series Analysis

Identification of the current and expected future pollution sources to rivers is crucial for sound environmental management. For this purpose numerous approaches were proposed that can be clustered under physical based models, stable isotope analysis and mixing methods, mass balance methods, time series analysis, land cover analysis, and spatial statistics. Another extremely common method is Principal Component Analysis, as well as its modifications, such as Absolute Principal Component Score. they have been applied to the source identification problems for nitrogen entry to rivers. This manuscript is checking whether PCA can really be a powerful method to uncover nitrogen pollution sources considering its theoretical background and assumptions. Moreover, slightly similar techniques, Independent Component Analysis and Factor Analysis will also be considered.
Signal Recovery with Non-Expansive Generative Network Priors
Authors: Jorio Cocola
Date: 2022-04-24 00:00:00
Tasks: Compressive Sensing, Denoising, Retrieval

We study compressive sensing with a deep generative network prior. Initial theoretical guarantees for efficient recovery from compressed linear measurements have been developed for signals in the range of a ReLU network with Gaussian weights and logarithmic expansivity: that is when each layer is larger than the previous one by a logarithmic factor. It was later shown that constant expansivity is sufficient for recovery. It has remained open whether the expansivity can be relaxed, allowing for networks with contractive layers (as often the case of real generators). In this work we answer this question, proving that a signal in the range of a Gaussian generative network can be recovered from few linear measurements provided that the width of the layers is proportional to the input layer size (up to log factors). This condition allows the generative network to have contractive layers. Our result is based on showing that Gaussian matrices satisfy a matrix concentration inequality which we term Range Restricted Weight Distribution Condition (R2WDC) and that weakens the Weight Distribution Condition (WDC) upon which previous theoretical guarantees were based. The WDC has also been used to analyze other signal recovery problems with generative network priors. By replacing the WDC with the R2WDC, we are able to extend previous results for signal recovery with expansive generative network priors to non-expansive ones. We discuss these extensions for phase retrieval, denoising, and spiked matrix recovery.
Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets
Authors: Rossano Gaeta, Michele Garetto, Giancarlo Ruffo, Alessandro Flammini
Date: 2022-04-28 00:00:00

We propose a simple model of an idealized online cultural market in which $N$ items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items' popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings. We do so under the assumption that users, having limited time/attention, are able to discriminate the best-quality only within a random subset of the items. We discover the existence of a harmful regime in which improper use of popularity can seriously compromise the emergence of quality, and a benign regime in which wise use of popularity, coupled with a small discrimination effort on behalf of users, guarantees the perfect alignment of quality and popularity ranking. Our findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.
KL-Mat : Fair Recommender System via Information Geometry
Authors: Hao Wang
Date: 2022-04-27 00:00:00
Tasks: Fairness, Recommendation Systems

Recommender system has intrinsic problems such as sparsity and fairness. Although it has been widely adopted for the past decades, research on fairness of recommendation algorithms has been largely neglected until recently. One important paradigm for resolving the issue is regularization. However, researchers have not been able to come up with a consensusly agreed regularization term like regularization framework in other fields such as Lasso or Ridge Regression. In this paper, we borrow concepts from information geometry and propose a new regularization-based fair algorithm called KL-Mat. The algorithmic technique leads to a more robust performance in accuracy performance such as MAE. More importantly, the algorithm produces much fairer results than vanilla matrix factorization approach. KL-Mat is fast, easy-to-implement and explainable.
Generative Multi-hop Retrieval
Authors: Hyunji Lee, Sohee Yang, Hanseok Oh, Minjoon Seo
Date: 2022-04-27 00:00:00
Tasks: Decoder, GPU, Retrieval, Text Retrieval

A common practice for text retrieval is to use an encoder to map the documents and the query to a common vector space and perform a nearest neighbor search (NNS); multi-hop retrieval also often adopts the same paradigm, usually with a modification of iteratively reformulating the query vector so that it can retrieve different documents at each hop. However, such a bi-encoder approach has limitations in multi-hop settings; (1) the reformulated query gets longer as the number of hops increases, which further tightens the embedding bottleneck of the query vector, and (2) it is prone to error propagation. In this paper, we focus on alleviating these limitations in multi-hop settings by formulating the problem in a fully generative way. We propose an encoder-decoder model that performs multi-hop retrieval by simply generating the entire text sequences of the retrieval targets, which means the query and the documents interact in the language model's parametric space rather than L2 or inner product space as in the bi-encoder approach. Our approach, Generative Multi-hop Retrieval(GMR), consistently achieves comparable or higher performance than bi-encoder models in five datasets while demonstrating superior GPU memory and storage footprint.
MSG-SR-Net: A Weakly Supervised Network Integrating Multi-Scale Generation and Super-Pixel Refinement for Building Extraction from High-Resolution Remotely Sensed Imageries
Authors: Xin Yan, Li Shen, Jicheng Wang, Xu Deng, Zhilin Li
Date: 2021-12-29 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021 12
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) methods based on image-level labels can relieve the tedious pixel-level annotation burden, and these methods are mainly based on class activation maps (CAMs). However, it is challenging to generate high-quality CAMs for high-resolution remotely sensed imagery (HRSI). In this paper, we propose a WSSS method for building extraction from HRSI using image-level labels. The proposed method, termed as the MSG-SR-Net, integrates two novel modules, i.e., multi-scale generation (MSG) and super-pixel refinement (SR), to obtain high-quality CAMs so as to provide reliable pixel-level training samples for subsequent semantic segmentation steps. The MSG module is proposed to use global semantic information to guide the learning of multiple features across different levels, and then respectively to utilize multi-level features for generating multi-scale CAMs. This component can effectively suppress the interference of the class-irrelevant noise and strengthen the use of profitable information in multi-level features. The SR module is designed to take advantage of super-pixels to improve multi-scale CAMs in target integrity and details preserving. Extensive experiments on two public building datasets demonstrated that the proposed modules made the 
MSG-SR-Net obtain more integral and accurate CAMs for building extraction. Moreover, experimental results also showed 
the proposed method achieved excellent performance with over 67%  in F1-score, and outperformed other weakly supervised methods in effectiveness and generalization ability.
LECF: Recommendation via Learnable Edge Collaborative Filtering
Authors: Shitao Xiao, Yingxia Shao, Yawen Li, Hongzhi Yin, Yanyan Shen & Bin Cui
Date: 2021-12-23 00:00:00
Proceeding: Science China Information Sciences 2021 12
Tasks: Collaborative Filtering

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering (CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering (LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges (i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.
Two New Datasets for Italian-Language Abstractive Text Summarization
Authors: Nicola Landro, Ignazio Gallo, Riccardo La Grassa, Edoardo Federici
Date: 2022-04-29 00:00:00
Proceeding: Information 2022 4
Tasks: Abstractive Text Summarization, Articles, Machine Translation, Sentence, Text Summarization, Translation, Vocal Bursts Valence Prediction

Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.
Euler: Detecting Network Lateral Movement via Scalable Temporal Link Prediction
Authors: Isaiah J. King, H. Howie Huang
Date: 2022-04-24 00:00:00
Proceeding: NDSS 2022 4
Tasks: Dynamic Link Prediction, Edge Detection, Graph Learning, Graph Neural Network, Intrusion Detection, Link Prediction

Lateral movement is a key stage of system compromise used by advanced persistent threats. Detecting it is no
simple task. When network host logs are abstracted into discrete temporal graphs, the problem can be reframed as anomalous edge detection in an evolving network. Research in modern deep graph learning techniques has produced many creative and complicated
models for this task. However, as is the case in many machine learning fields, the generality of models is of paramount importance for accuracy and scalability during training and inference. In this paper, we propose a formalized approach to this problem with a framework we call EULER. It consists of a model-agnostic graph neural network stacked upon a model-agnostic sequence encoding layer such as a recurrent neural network. Models built according to the EULER framework can easily distribute their graph convolutional layers across multiple machines for large performance improvements. Additionally, we demonstrate that EULER-based models are competitive, or better than many state-of-the-art approaches to anomalous link detection and prediction. As anomaly-based intrusion detection systems, EULER models can efficiently identify anomalous connections between entities with high precision and outperform 
 other unsupervised techniques for anomalous lateral movement detection.
Adaptive Multi-Strategy Market-Making Agent For Volatile Markets
Authors: Ali Raheman, Anton Kolonin, Alexey Glushchenko, Arseniy Fokin, Ikram Ansari
Date: 2022-04-28 00:00:00

Crypto-currency market uncertainty drives the need to find adaptive solutions to maximise gain or at least to avoid loss throughout the periods of trading activity. Given the high dimensionality and complexity of the state-action space in this domain, it can be treated as a "Narrow AGI" problem with the scope of goals and environments bound to financial markets. Adaptive Multi-Strategy Agent approach for market-making introduces a new solution to maximise positive "alpha" in long-term handling limit order book (LOB) positions by using multiple sub-agents implementing different strategies with a dynamic selection of these agents based on changing market conditions. AMSA provides no specific strategy of its own while being responsible for segmenting the periods of market-making activity into smaller execution sub-periods, performing internal backtesting on historical data on each of the sub-periods, doing sub- agent performance evaluation and re-selection of them at the end of each sub- period, and collecting returns and losses incrementally. With this approach, the return becomes a function of hyper-parameters such as market data granularity (refresh rate), the execution sub-period duration, number of active sub-agents, and their individual strategies. Sub-agent selection for the next trading sub-period is made based on return/loss and alpha values obtained during internal backtesting as well as real trading. Experiments with the AMSA have been performed under different market conditions relying on historical data and proved a high probability of positive alpha throughout the periods of trading activity in the case of properly selected hyper-parameters.
Clustering Systems of Phylogenetic Networks
Authors: Marc Hellmuth, David Schaller, Peter F. Stadler
Date: 2022-04-28 00:00:00
Tasks: Clustering

Rooted acyclic graphs appear naturally when the phylogenetic relationship of a set $X$ of taxa involves not only speciations but also recombination, horizontal transfer, or hybridization, that cannot be captured by trees. A variety of classes of such networks have been discussed in the literature, including phylogenetic, level-1, tree-child, tree-based, galled tree, regular, or normal networks as models of different types of evolutionary processes. Clusters arise in models of phylogeny as the sets $\mathtt{C}(v)$ of descendant taxa of a vertex $v$. The clustering system $\mathscr{C}_N$ comprising the clusters of a network $N$ conveys key information on $N$ itself. In the special case of rooted phylogenetic trees, $T$ is uniquely determined by its clustering system $\mathscr{C}_T$. Although this is no longer true for networks in general, it is of interest to relate properties of $N$ and $\mathscr{C}_N$. Here, we systematically investigate the relationships of several well-studied classes of networks and their clustering systems. The main results are correspondences of classes of networks and clustering system of the following form: If $N$ is a network of type $\mathbb{X}$, then $\mathcal{C}_N$ satisfies $\mathbb{Y}$, and conversely if $\mathscr{C}$ is a clustering system satisfying $\mathbb{Y}$ then there is network $N$ of type $\mathbb{X}$ such that $\mathscr{C}\subseteq\mathscr{C}_N$.This, in turn, allows us to investigate the mutual dependencies between the distinct types of networks in much detail.
A unified theory of information transfer and causal relation
Authors: Yang Tian, Hedong Hou, Yaoyuan Wang, Ziyang Zhang, Pei Sun
Date: 2022-04-21 00:00:00
Tasks: Causal Inference, Relation

Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems. While information transfer analysis has booming applications in both science and engineering fields, critical mysteries about its foundations remain unsolved. Fundamental yet difficult questions concern how information transfer and causal relation originate, what they depend on, how they differ from each other, and if they are created by a unified and general quantity. These questions essentially determine the validity of causal relation measurement via information transfer. Here we pursue to lay a complete theoretical basis of information transfer and causal relation. Beyond the well-known relations between these concepts that conditionally hold, we demonstrate that information transfer and causal relation universally originate from specific information synergy and redundancy phenomena characterized by high-order mutual information. More importantly, our theory analytically explains the mechanisms for information transfer and causal relation to originate, vanish, and differ from each other. Moreover, our theory naturally defines the effect sizes of information transfer and causal relation based on high-dimensional coupling events. These results may provide a unified view of information, synergy, and causal relation to bridge Pearl's causal inference theory in computer science and information transfer analysis in physics.
Fast Cross-Correlation for TDoA Estimation on Small Aperture Microphone Arrays
Authors: François Grondin, Marc-Antoine Maheux, Jean-Samuel Lauzon, Jonathan Vincent, François Michaud
Date: 2022-04-28 00:00:00

This paper introduces the Fast Cross-Correlation (FCC) method for Time Difference of Arrival (TDoA) Estimation for pairs of microphones on a small aperture microphone array. FCC relies on low-rank decomposition and exploits symmetry in even and odd bases to speed up computation while preserving TDoA accuracy. FCC reduces the number of flops by a factor of 4.5 and the execution speed by factors between 3.5 and 8.3 on embedded hardware, compared to the state-of-the-art Generalized Cross-Correlation (GCC) method that relies on the Fast Fourier Transform (FFT). This improvement can provide portable microphone arrays with extended battery life and allow real-time processing on low-cost hardware.
Mobile EEG artifact correction on limited hardware using artifact subspace recon- struction
Authors: Paul Maanen, Sarah Blum, Stefan Debener
Date: 2022-04-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Biological data like electroencephalography (EEG) are typically contaminated by unwanted signals, called artifacts. Therefore, many applications dealing with biological data with low signal-to-noise ratio require robust artifact correction. For some applications like brain-computer-interfaces (BCI), the artifact correction needs to be real-time capable. Artifact subspace reconstruction (ASR) is a statistical method for artifact reduction in EEG. However, in its current implementation, ASR cannot be used in mobile data recordings using limited hardware easily. In this report, we add to the growing field of portable, online signal processing methods by describing an implementation of ASR for limited hardware like single-board computers. We describe the architecture, the process of translating and compiling a Matlab codebase for a research platform, and a set of validation tests using publicly available data sets. The implementation of ASR on limited, portable hardware facilitates the online interpretation of EEG signals acquired outside of the laboratory environment.
Generalizing Hybrid Integrator-Gain Systems Using Fractional Calculus
Authors: S. Ali Hosseini, Mohammad Saleh Tavazoei, Luke F. van Eijk, S. Hassan HosseinNia
Date: 2022-04-28 00:00:00

The Hybrid Integral Gain Systems (HIGS) has recently gained a lot of attention in the control of precision motion systems. HIGS is a nonlinear low pass filter with a 52 degree phase advantage over its linear counterpart. This property allows us to avoid the limitations typically associated with linear controllers like the waterbed effect and bode's phase gain relation. In this paper, we generalize HIGS via replacing the involved integer order integrator with a fractional order one to adapt the phase lead from 0 degree (linear low pass filter) to 52 degree (HIGS). To analyze this filter in the frequency domain, the describing function of the proposed filter, i.e., the fractional-order HIGS, is obtained using the Fourier expansion of the output signal. In addition, this generalized HIGS is implemented in a PID structure controlling a double integrator system to validate the performance of the proposed filter in the time domain, in which changing the fractional variable from zero to one, the output varies from the response of a nonlinear control system to a linear one.
Safety-Aware Optimal Control for Motion Planning with Low Computing Complexity
Authors: Xuda Ding, Han Wang, Jianping He, Cailian Chen, Kostas Margellos, Antonis Papachristodoulou
Date: 2022-04-28 00:00:00
Tasks: Motion Planning

The existence of multiple irregular obstacles in the environment introduces nonconvex constraints into the optimization for motion planning, which makes the optimal control problem hard to handle. One efficient approach to address this issue is Successive Convex Approximation (SCA), where the nonconvex problem is convexified and solved successively. However, this approach still faces two main challenges: I) infeasibility, caused by linearisation about infeasible reference points; ii) high computational complexity incurred by multiple constraints, when solving the optimal control problem with a long planning horizon and multiple obstacles. To overcome these challanges, this paper proposes an energy efficient safetyaware control method for motion planning with low computing complexity and address these challenges. Specifically, a control barrier function-based linear quadratic regulator is formulated for the motion planning to guarantee safety and energy efficiency. Then, to avoid infeasibility, Backward Receding SCA (BRSCA) approach with a dynamic constraints-selection rule is proposed. Dynamic programming with primal-dual iteration is designed to decrease computational complexity. It is found that BRSCA is applicable to time-varying control limits. Numerical simulations and hardware experiments vevify the efficiency of BRSCA. Simulations demonstrates that BRSCA has a higher probability of finding feasible solutions, reduces the computation time by about 17.4% and the energy cost by about four times compared to other methods in the literature.