pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Training of Liquid State Machines
Authors: Pavithra Koralalage, Ireoluwa Fakeye, Pedro Machado, Jason Smith, Isibor Kennedy Ihianle, Salisu Wada Yahaya, Andreas Oikonomou, Ahmad Lotfi
Date: 2023-02-06 00:00:00

Spiking Neural Networks (SNNs) emerged as a promising solution in the field of Artificial Neural Networks (ANNs), attracting the attention of researchers due to their ability to mimic the human brain and process complex information with remarkable speed and accuracy. This research aimed to optimise the training process of Liquid State Machines (LSMs), a recurrent architecture of SNNs, by identifying the most effective weight range to be assigned in SNN to achieve the least difference between desired and actual output. The experimental results showed that by using spike metrics and a range of weights, the desired output and the actual output of spiking neurons could be effectively optimised, leading to improved performance of SNNs. The results were tested and confirmed using three different weight initialisation approaches, with the best results obtained using the Barabasi-Albert random graph method.
Approximate message passing from random initialization with applications to $\mathbb{Z}_{2}$ synchronization
Authors: Gen Li, Wei Fan, Yuting Wei
Date: 2023-02-07 00:00:00

This paper is concerned with the problem of reconstructing an unknown rank-one matrix with prior structural information from noisy observations. While computing the Bayes-optimal estimator seems intractable in general due to its nonconvex nature, Approximate Message Passing (AMP) emerges as an efficient first-order method to approximate the Bayes-optimal estimator. However, the theoretical underpinnings of AMP remain largely unavailable when it starts from random initialization, a scheme of critical practical utility. Focusing on a prototypical model called $\mathbb{Z}_{2}$ synchronization, we characterize the finite-sample dynamics of AMP from random initialization, uncovering its rapid global convergence. Our theory provides the first non-asymptotic characterization of AMP in this model without requiring either an informative initialization (e.g., spectral initialization) or sample splitting.
Identification of Power System Oscillation Modes using Blind Source Separation based on Copula Statistic
Authors: Pooja Algikar, Lamine Mili, Mohsen Ben Hassine, Somayeh Yarahmadi, Almuatazbellah, Boker
Date: 2023-02-07 00:00:00
Tasks: blind source separation

The dynamics of a power system with large penetration of renewable energy resources are becoming more nonlinear due to the intermittence of these resources and the switching of their power electronic devices. Therefore, it is crucial to accurately identify the dynamical modes of oscillation of such a power system when it is subject to disturbances to initiate appropriate preventive or corrective control actions. In this paper, we propose a high-order blind source identification (HOBI) algorithm based on the copula statistic to address these non-linear dynamics in modal analysis. The method combined with Hilbert transform (HOBI-HT) and iteration procedure (HOBMI) can identify all the modes as well as the model order from the observation signals obtained from the number of channels as low as one. We access the performance of the proposed method on numerical simulation signals and recorded data from a simulation of time domain analysis on the classical 11-Bus 4-Machine test system. Our simulation results outperform the state-of-the-art method in accuracy and effectiveness.
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
Authors: Arnulf Jentzen, Adrian Riekert, Philippe von Wurstemberger
Date: 2023-02-07 00:00:00
Tasks: Operator learning

In this article we propose a new deep learning approach to approximate operators related to parametric partial differential equations (PDEs). In particular, we introduce a new strategy to design specific artificial neural network (ANN) architectures in conjunction with specific ANN initialization schemes which are tailor-made for the particular approximation problem under consideration. In the proposed approach we combine efficient classical numerical approximation techniques with deep operator learning methodologies. Specifically, we introduce customized adaptions of existing ANN architectures together with specialized initializations for these ANN architectures so that at initialization we have that the ANNs closely mimic a chosen efficient classical numerical algorithm for the considered approximation problem. The obtained ANN architectures and their initialization schemes are thus strongly inspired by numerical algorithms as well as by popular deep learning methodologies from the literature and in that sense we refer to the introduced ANNs in conjunction with their tailor-made initialization schemes as Algorithmically Designed Artificial Neural Networks (ADANNs). We numerically test the proposed ADANN methodology in the case of several parametric PDEs. In the tested numerical examples the ADANN methodology significantly outperforms existing traditional approximation algorithms as well as existing deep operator learning methodologies from the literature.
A distribution-free mixed-integer optimization approach to hierarchical modelling of clustered and longitudinal data
Authors: Madhav Sankaranarayanan, Intekhab Hossain, Tom Chen
Date: 2023-02-06 00:00:00
Tasks: regression

Recent advancements in Mixed Integer Optimization (MIO) algorithms, paired with hardware enhancements, have led to significant speedups in resolving MIO problems. These strategies have been utilized for optimal subset selection, specifically for choosing $k$ features out of $p$ in linear regression given $n$ observations. In this paper, we broaden this method to facilitate cluster-aware regression, where selection aims to choose $\lambda$ out of $K$ clusters in a linear mixed effects (LMM) model with $n_k$ observations for each cluster. Through comprehensive testing on a multitude of synthetic and real datasets, we exhibit that our method efficiently solves problems within minutes. Through numerical experiments, we also show that the MIO approach outperforms both Gaussian- and Laplace-distributed LMMs in terms of generating sparse solutions with high predictive power. Traditional LMMs typically assume that clustering effects are independent of individual features. However, we introduce an innovative algorithm that evaluates cluster effects for new data points, thereby increasing the robustness and precision of this model. The inferential and predictive efficacy of this approach is further illustrated through its application in student scoring and protein expression.
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Authors: Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang, Yangyong Zhu, Philip S. Yu
Date: 2023-02-07 00:00:00
Tasks: MULTI-VIEW LEARNING

Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups' preferences via aggregating diverse members' interests. Actually, groups' ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions. To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. To comprehensively capture the group consensus, we innovatively design three distinct views which provide mutually complementary information to enable multi-view learning, including member-level aggregation, item-level tastes, and group-level inherent preferences. To integrate and balance the multi-view information, an adaptive fusion component is further proposed. As to member-level aggregation, different from existing linear or attentive strategies, we design a novel hypergraph neural network that allows for efficient hypergraph convolutional operations to generate expressive member-level aggregation. We evaluate our ConsRec on two real-world datasets and experimental results show that our model outperforms state-of-the-art methods. An extensive case study also verifies the effectiveness of consensus modeling.
Multi-Task Deep Recommender Systems: A Survey
Authors: Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang
Date: 2023-02-07 00:00:00
Tasks: Multi-Task Learning, Recommendation Systems, Relation, Survey

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic in recommendation due to the demand for multi-task prediction considering performance and efficiency. Although MTL has been well studied and developed, there is still a lack of systematic review in the recommendation community. To fill the gap, we provide a comprehensive review of existing multi-task deep recommender systems (MTDRS) in this survey. To be specific, the problem definition of MTDRS is first given, and it is compared with other related areas. Next, the development of MTDRS is depicted and the taxonomy is introduced from the task relation and methodology aspects. Specifically, the task relation is categorized into parallel, cascaded, and auxiliary with main, while the methodology is grouped into parameter sharing, optimization, and training mechanism. The survey concludes by summarizing the application and public datasets of MTDRS and highlighting the challenges and future directions of the field.
Exploration and Regularization of the Latent Action Space in Recommendation
Authors: Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Kun Gai, Peng Jiang, Xiangyu Zhao, Yongfeng Zhang
Date: 2023-02-07 00:00:00
Tasks: Recommendation Systems

In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction. However, the action space of the recommendation policy is a list of items, which could be extremely large with a dynamic candidate item pool. To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step. The first step maps the given state space into a vectorized hyper-action space, and the second step selects the item list based on the hyper-action. In order to regulate the discrepancy between the two action spaces, we design an alignment module along with a kernel mapping function for items to ensure inference accuracy and include a supervision module to stabilize the learning process. We build simulated environments on public datasets and empirically show that our framework is superior in recommendation compared to standard RL baselines.
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Authors: Dugang Liu, Yang Qiao, Xing Tang, Liang Chen, Xiuqiang He, Weike Pan, Zhong Ming
Date: 2023-02-07 00:00:00
Tasks: Management

Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most of the existing methods require some specific data, architectures and training methods. In this paper, we first argue through an online study that arbitrarily removing all the biases in industrial recommendation may not consistently yield a desired performance improvement. For the situation that a randomized dataset is not available, we propose a novel self-sampling training and evaluation (SSTE) framework to achieve the accuracy-bias tradeoff in recommendation, i.e., eliminate the harmful biases and preserve the beneficial ones. Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data. A self-training module infers the beneficial biases and learns better tradeoff based on these subsets, and a self-evaluation module aims to use these subsets to construct more plausible references to reflect the optimized model. Finally, we conduct extensive offline experiments on two datasets to verify the effectiveness of our SSTE. Moreover, we deploy our SSTE in homepage recommendation of a famous financial management product called Tencent Licaitong, and find very promising results in an online A/B test.
Disentangled Causal Embedding With Contrastive Learning For Recommender System
Authors: Weiqi Zhao, Dian Tang, Xin Chen, Dawei Lv, Daoli Ou, Biao Li, Peng Jiang, Kun Gai
Date: 2023-02-07 00:00:00
Tasks: Contrastive Learning, Recommendation Systems

Recommender systems usually rely on observed user interaction data to build personalized recommendation models, assuming that the observed data reflect user interest. However, user interacting with an item may also due to conformity, the need to follow popular items. Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results. Therefore, from the cause-effect view, disentangling these interaction causes is a crucial issue. It also contributes to OOD problems, where training and test data are out-of-distribution. Nevertheless, it is quite challenging as we lack the signal to differentiate interest and conformity. The data sparsity of pure cause and the items' long-tail problem hinder disentangled causal embedding. In this paper, we propose DCCL, a framework that adopts contrastive learning to disentangle these two causes by sample augmentation for interest and conformity respectively. Futhermore, DCCL is model-agnostic, which can be easily deployed in any industrial online system. Extensive experiments are conducted over two real-world datasets and DCCL outperforms state-of-the-art baselines on top of various backbone models in various OOD environments. We also demonstrate the performance improvements by online A/B testing on Kuaishou, a billion-user scale short-video recommender system.
MMRec: Simplifying Multimodal Recommendation
Authors: Xin Zhou
Date: 2023-02-02 00:00:00
Tasks: Multimodal Recommendation

This paper presents an open-source toolbox, MMRec for multimodal recommendation. MMRec simplifies and canonicalizes the process of implementing and comparing multimodal recommendation models. The objective of MMRec is to provide a unified and configurable arena that can minimize the effort in implementing and testing multimodal recommendation models. It enables multimodal models, ranging from traditional matrix factorization to modern graph-based algorithms, capable of fusing information from multiple modalities simultaneously. Our documentation, examples, and source code are available at \url{https://github.com/enoche/MMRec}.
The approach to modeling the value of statistical life using average per capita income
Authors: Stanislav Levytskyi, Oleksandr Gneushev, Vasyl Makhlinets
Date: 2023-02-07 00:00:00

The problem of determining the value of statistical life in Ukraine in order to find ways to improve it is an urgent one now. The current level of value is analyzed, which is a direct consequence of the poor quality of life of a citizen, hence his low level. The description of the basic theoretical and methodological approaches to the estimation of the cost of human life is given. Based on the analysis, a number of hypotheses have been advanced about the use of statistical calculations to achieve the modeling objectives. Model calculations are based on the example of Zaporozhye Oblast statistics for 2018-2019. The article elaborates the approach to the estimation of the economic equivalent of the cost of living on the basis of demographic indicators and average per capita income, and also analyzes the possibilities of their application in the realities of the national economy. Using Statistica, the regression equation parameters were determined for statistical data of population distribution of Zaporizhzhia region by age groups for 2018. The calculation parameters were also found using the Excel office application, using the Solution Finder option to justify the quantitative range of metric values. It is proved that the proposed approach to modeling and calculations are simpler and more efficient than the calculation methods proposed earlier. The study concluded that the value of statistical life in Ukraine is significantly undervalued.
Aspect-Category-Opinion-Sentiment Extraction Using Generative Transformer Model
Authors: Cao Duy Hoang, Quang Vinh Dinh, Ngoc Hong Tran
Date: 2023-01-18 00:00:00
Proceeding: RIFV 2023 1
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect-Category-Opinion-Sentiment Quadruple Extraction, Sentiment Analysis, Triplet

Sentiment analysis is one of Natural Language Processing's applications that aims to process and extract sentiment information quickly and effectively. To expand upon the previous triplet extraction, that being aspect-opinion-sentiment triplets, Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction was created. There are several methods to extract quadruples, albeit with several limitations, such as their effectiveness towards implicit information and their overall low-performance score. This paper proposes a method of using BART-Aspect-Based-Sentiment-Analysis (BARTABSA), a sentiment analysis model that aims to unify the previous Aspect Based Sentiment Analysis subtask - namely Aspect-Opinion pair extraction and Aspect-Opinion-Sentiment triplet extraction, and solve them without changing the core algorithm or adding other models to it - to solve the ACOS subtask. After some modification to the data and the model's outer layer, the result shows significant and promising improvements over previous results.
Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations
Authors: Piotr Kuwałek
Date: 2023-02-07 00:00:00

Voltage fluctuations are common disturbances in power grids. Initially, it is necessary to selectively identify individual sources of voltage fluctuations to take actions to minimize the effects of voltage fluctuations. Selective identification of disturbing loads is possible by using a signal chain consisting of demodulation, decomposition, and assessment of the propagation of component signals. The accuracy of such an approach is closely related to the applied decomposition method. The paper presents a new method for decomposition by approximation with pulse waves. The proposed method allows for an correct identification of selected parameters, that is, the frequency of changes in the operating state of individual sources of voltage fluctuations and the amplitude of voltage changes caused by them. The article presents results from numerical simulation studies and laboratory experimental studies, based on which the estimation errors of the indicated parameters were determined by the proposed decomposition method and other empirical decomposition methods available in the literature. The real states that occur in power grids were recreated in the research. The metrological interpretation of the results obtained from the numerical simulation and experimental research is discussed.
Data-based optimal estimation of frequency bias: The case of Southwest Power Pool
Authors: Miroslav Kosanic, Marija Ilic, Daniel Baker, Harvey Scribner, Casey Cathey
Date: 2023-02-07 00:00:00
Tasks: parameter estimation

In this paper, we introduce a method to optimally estimate time-varying frequency bias. Current industry practice is to assume that frequency bias is changing only on annual basis. We suggest that this improved time-dependent bias estimate can be used to reduce the cost of frequency regulation needed to meet industry standards requested by the North American Electric Reliability Corporation (NERC). Optimization of time-varying frequency bias is posed as a parameter estimation (calibration) problem whose implementation utilizes online system measurements. It is further shown how this result can be used to estimate intra-dispatch load deviations. This knowledge is needed to estimate more accurately regulation reserve needed, and to therefore reduce overall regulation cost. Methods can be introduced to give incentives to demand response to participate in frequency regulation. Overall, we show the importance of incorporating knowledge of physics-based models for data-enabled parameter estimation of physical systems.
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
Authors: Vahidullah Tac, Manuel K. Rausch, Francisco Sahli-Costabal, Adrian B. Tepole
Date: 2023-01-11 00:00:00

We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy function and the dissipation potential with data-driven functions that a priori satisfy physics-based constraints such as objectivity and the second law of thermodynamics. Our approach enables modeling viscoelastic behavior of materials under arbitrary loads in three-dimensions even with large deformations and large deviations from the thermodynamic equilibrium. The data-driven nature of the governing potentials endows the model with much needed flexibility in modeling the viscoelastic behavior of a wide class of materials. We train the model using stress-strain data from biological and synthetic materials including humain brain tissue, blood clots, natural rubber and human myocardium and show that the data-driven method outperforms traditional, closed-form models of viscoelasticity.
Turing patterns in a Leslie-Gower predator prey model
Authors: Florinda Capone, Roberta De Luca, Isabella Torcicollo
Date: 2023-02-07 00:00:00

A reaction-diffusion Leslie-Gower predator-prey model, incorporating the fear effect and prey refuge, with Beddington-DeAngelis functional response, is introduced. A qualitative analysis of the solutions of the model and the stability analysis of the coexistence equilibrium, are performed. Sufficient conditions guaranteeing the occurrence of Turing instability have been determined either in the case of self-diffusion or in the case of cross-diffusion. Different types of Turing patterns, representing a spatial redistribution of population in the environment, emerge for different values of the model parameters.
Protein-protein docking using a tensor train black-box optimization method
Authors: Dmitry Morozov, Artem Melnikov, Vishal Shete, Michael Perelshtein
Date: 2023-02-07 00:00:00
Tasks: Drug Design

Black-box optimization methods play an important role in many fields of computational simulation. In particular, such methods are often used in the design and modelling of biological systems, including proteins and their complexes with various ligands. This work is mainly focused on the protein-protein docking that plays a key role in modern drug-design workflows. We develop a black-box approach for such docking problems using a novel technique based on the tensor-train decomposition of high-dimensional interaction functions. Our method shows an advantage in terms of the discovered global minima and has a high potential for further implementation on a wide range of devices, including graphical processing units and quantum processing units.