pwc-archive/papers-with-abstracts huggingface dataset:
Kernel Memory Networks: A Unifying Framework for Memory Modeling
Authors: Georgios Iatropoulos, Johanni Brea, Wulfram Gerstner
Date: 2022-08-19 00:00:00

We consider the problem of training a neural network to store a set of patterns with maximal noise robustness. A solution, in terms of optimal weights and state update rules, is derived by training each individual neuron to perform either kernel classification or interpolation with a minimum weight norm. By applying this method to feed-forward and recurrent networks, we derive optimal models, termed kernel memory networks, that include, as special cases, many of the hetero- and auto-associative memory models that have been proposed over the past years, such as modern Hopfield networks and Kanerva's sparse distributed memory. We modify Kanerva's model and demonstrate a simple way to design a kernel memory network that can store an exponential number of continuous-valued patterns with a finite basin of attraction. The framework of kernel memory networks offers a simple and intuitive way to understand the storage capacity of previous memory models, and allows for new biological interpretations in terms of dendritic non-linearities and synaptic cross-talk.
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation
Authors: Sichun Luo, Yuanzhang Xiao, Linqi Song
Date: 2022-08-19 00:00:00
Tasks: Attribute, Clustering, Federated Learning, Graph Neural Network, Recommendation Systems, Representation Learning

Federated recommendation applies federated learning techniques in recommendation systems to help protect user privacy by exchanging models instead of raw user data between user devices and the central server. Due to the heterogeneity in user's attributes and local data, attaining personalized models is critical to help improve the federated recommendation performance. In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation. Specifically, we construct a collaborative graph and incorporate attribute information to jointly learn the representation through a federated GNN. Based on these learned representations, we cluster users into different user groups and learn personalized models for each cluster. Then each user learns a personalized model by combining the global federated model, the cluster-level federated model, and the user's fine-tuned local model. To alleviate the heavy communication burden, we intelligently select a few representative users (instead of randomly picked users) from each cluster to participate in training. Experiments on real-world datasets show that our proposed method achieves superior performance over existing methods.
A Ptolemaic Partitioning Mechanism
Authors: Richard Connor
Date: 2022-08-19 00:00:00

For many years, exact metric search relied upon the property of triangle inequality to give a lower bound on uncalculated distances. Two exclusion mechanisms derive from this property, generally known as pivot exclusion and hyperplane exclusion. These mechanisms work in any proper metric space and are the basis of many metric indexing mechanisms. More recently, the Ptolemaic and four-point lower bound properties have been shown to give tighter bounds in some subclasses of metric space. Both triangle inequality and the four-point lower bound directly imply straightforward partitioning mechanisms: that is, a method of dividing a finite space according to a fixed partition, in order that one or more classes of the partition can be eliminated from a search at query time. However, up to now, no partitioning principle has been identified for the Ptolemaic inequality, which has been used only as a filtering mechanism. Here, a novel partitioning mechanism for the Ptolemaic lower bound is presented. It is always better than either pivot or hyperplane partitioning. While the exclusion condition itself is weaker than Hilbert (four-point) exclusion, its calculation is cheaper. Furthermore, it can be combined with Hilbert exclusion to give a new maximum for exclusion power with respect to the number of distances measured per query.
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
Authors: Yujia Zhou, Jing Yao, Zhicheng Dou, Ledell Wu, Peitian Zhang, Ji-Rong Wen
Date: 2022-08-19 00:00:00
Tasks: Retrieval

Document retrieval has been extensively studied within the index-retrieve framework for decades, which has withstood the test of time. Unfortunately, such a pipelined framework limits the optimization of the final retrieval quality, because indexing and retrieving are separated stages that can not be jointly optimized in an end-to-end manner. In order to unify these two stages, we explore a model-based indexer for document retrieval. Concretely, we propose Ultron, which encodes the knowledge of all documents into the model and aims to directly retrieve relevant documents end-to-end. For the model-based indexer, how to represent docids and how to train the model are two main issues to be explored. Existing solutions suffer from semantically deficient docids and limited supervised data. To tackle these two problems, first, we devise two types of docids that are richer in semantics and easier for model inference. In addition, we propose a three-stage training workflow to capture more knowledge contained in the corpus and associations between queries and docids. Experiments on two public datasets demonstrate the superiority of Ultron over advanced baselines for document retrieval.
Audio-Guided Attention Network for Weakly Supervised Violence Detection
Authors: Yujiang Pu, Xiaoyu Wu
Date: 2022-02-21 00:00:00
Proceeding: Conference 2022 2
Tasks: Anomaly Detection In Surveillance Videos

Detecting violence in video is a challenging task due to its complex scenarios and great intra-class variability. Most previous works specialize in the analysis of appearance or motion information, ignoring the co-occurrence of some audio and visual events. Physical conflicts such as abuse and fighting are usually accompanied by screaming, while crowd violence such as riots and wars are generally related to gunshots and explosions. Therefore, we propose a novel audio-guided multimodal violence detection framework. First, deep neural networks are used to extract appearance and audio features, respectively. Then, a Cross-Modal Awareness Local-Arousal (CMA-LA) network is proposed for cross-modal interaction, which implements audio-to-visual feature enhancement over temporal dimension. The enhanced features are then fed into a multilayer perceptron (MLP) to capture high-level semantics, followed by a temporal convolution layer to obtain high-confidence violence scores. To validate the proposed method, we conduct experiments on a large violent video dataset, XD Violence. Comprehensive experiments demonstrate the robust performance of our approach, which also achieves a new state-of-the-art AP result.
Bridging the Domain Gap: Improve Informal Language Translation via Counterfactual Domain Adaptation
Authors: Ke Wang, Guandan Chen, Zhongqiang Huang, Xiaojun Wan, Fei Huang
Date: 2021-05-18 00:00:00
Proceeding: AAAI 2021 5
Tasks: Articles, counterfactual, Domain Adaptation, TAG, Translation

Despite the near-human performances already achieved on formal texts such as news articles, neural machine transla- tion still has difficulty in dealing with ”user-generated” texts that have diverse linguistic phenomena but lack large-scale high-quality parallel corpora. To address this problem, we propose a counterfactual domain adaptation method to better leverage both large-scale source-domain data (formal texts) and small-scale target-domain data (informal texts). Specifi- cally, by considering effective counterfactual conditions (the concatenations of source-domain texts and the target-domain tag), we construct the counterfactual representations to fill the sparse latent space of the target domain caused by a small amount of data, that is, bridging the gap between the source- domain data and the target-domain data. Experiments on English-to-Chinese and Chinese-to-English translation tasks show that our method outperforms the base model that is trained only on the informal corpus by a large margin, and consistently surpasses different baseline methods by +1.12 ∼ 4.34 BLEU points on different datasets. Furthermore, we also show that our method achieves competitive performances on cross-domain language translation on four language pairs.
SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis
Authors: Dunlu Peng ∗, Wuchen Yang, Cong Liu, Shuairui Lü
Date: 2021-02-10 00:00:00
Proceeding: Neural Networks 2021 2
Tasks: Image Generation, Semantic Similarity, Semantic Textual Similarity, Sentence

Synthesizing photo-realistic images based on text descriptions is a challenging task in the field of
computer vision. Although generative adversarial networks have made significant breakthroughs in this
task, they still face huge challenges in generating high-quality visually realistic images consistent with
the semantics of text. Generally, existing text-to-image methods accomplish this task with two steps,
that is, first generating an initial image with a rough outline and color, and then gradually yielding
the image within high-resolution from the initial image. However, one drawback of these methods is
that, if the quality of the initial image generation is not high, it is hard to generate a satisfactory
high-resolution image. In this paper, we propose SAM-GAN, Self-Attention supporting Multi-stage
Generative Adversarial Networks, for text-to-image synthesis. With the self-attention mechanism, the
model can establish the multi-level dependence of the image and fuse the sentence- and word-level
visual-semantic vectors, to improve the quality of the generated image. Furthermore, a multi-stage
perceptual loss is introduced to enhance the semantic similarity between the synthesized image and
the real image, thus enhancing the visual-semantic consistency between text and images. For the
diversity of the generated images, a mode seeking regularization term is integrated into the model. The
results of extensive experiments and ablation studies, which were conducted in the Caltech-UCSD Birds
and Microsoft Common Objects in Context datasets, show that our model is superior to competitive
models in text-to-image synthesis.
Approximate Dynamic Programming for Platoon Coordination under Hours-of-Service Regulations
Authors: Ting Bai, Alexander Johansson, Karl Henrik Johansson, Jonas Mårtensson
Date: 2022-08-19 00:00:00
Tasks: Decision Making

Truck drivers are required to stop and rest with a certain regularity according to the driving and rest time regulations, also called Hours-of-Service (HoS) regulations. This paper studies the problem of optimally forming platoons when considering realistic HoS regulations. In our problem, trucks have fixed routes in a transportation network and can wait at hubs along their routes to form platoons with others while fulfilling the driving and rest time constraints. We propose a distributed decision-making scheme where each truck controls its waiting times at hubs based on the predicted schedules of others. The decoupling of trucks' decision-makings contributes to an approximate dynamic programming approach for platoon coordination under HoS regulations. Finally, we perform a simulation over the Swedish road network with one thousand trucks to evaluate the achieved platooning benefits under the HoS regulations in the European Union (EU). The simulation results show that, on average, trucks drive in platoons for 37% of their routes if each truck is allowed to be delayed for 5% of its total travel time. If trucks are not allowed to be delayed, they drive in platoons for 12% of their routes.
Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs
Authors: Kunhee Ryu, Juhoon Back
Date: 2022-08-19 00:00:00
Tasks: Distributed Optimization

This paper investigates the distributed Kalman filtering (DKF) from distributed optimization viewpoint. Motivated by the fact that Kalman filtering is a maximum a posteriori estimation (MAP) problem, which is a quadratic optimization problem, we reformulate DKF problem as a consensus optimization problem, resulting in that it can be solved by many existing distributed optimization algorithms. A new DKF algorithm employing the dual ascent method is proposed, and its stability is proved under mild assumptions. The performance of the proposed algorithm is evaluated through numerical experiments.
Learning Local Volt/Var Controllers Towards Efficient Network Operation with Stability Guarantees
Authors: Guido Cavraro, Zhenyi Yuan, Manish K. Singh, Jorge Cortés
Date: 2022-08-19 00:00:00

This paper considers the problem of voltage regulation in distribution networks. The primary motivation is to keep voltages within preassigned operating limits by commanding the reactive power output of distributed energy resources (DERs) deployed in the grid. We develop a framework for developing local Volt/Var control that comprises two main steps. In the first, by exploiting historical data and for each DER, we learn a function representing the desirable equilibrium points for the power network. These points approximate solutions of an Optimal Power Flow (OPF) problem. In the second, we propose a control scheme for steering the network towards these favorable configurations. Theoretical conditions are derived to formally guarantee the stability of the developed control scheme, and numerical simulations illustrate the effectiveness of the proposed approach.
Resilience assessment and improvement for electric power transmission systems against typhoon disasters: A data-model hybrid driven approach
Authors: Rui Yang, Yang Li
Date: 2022-08-19 00:00:00

In response to the damage to electric power transmission systems caused by typhoon disasters in coastal areas, a planning-targeted resilience assessment framework that considers the impact of multiple factors is established to accurately find the weak links of the transmission system and improve the system resilience. Firstly, this paper constructs the attenuation model of the wind field and the comprehensive failure model of the system, in which the model drive to establish the cumulative failure model of the transmission system is adopted, and multiple data-driven schemes to correct the cumulative failure rate by relying on the feature factor information of the transmission system is adopted. At the same time, an analytic hierarchy process-weighted arithmetic averaging (AHP-WAA) method is introduced to select the optimal data-driven evaluation scheme. Secondly, this paper adopts the impact-increment-based state enumeration (IISE) method to establish resilience indicators for systems and corridors separately. On this basis, the optimal promotion strategy is selected according to the construction difficulty, resilience improvement ability, and cost analysis. Finally, simulations on the IEEE RTS-79 system have been carried out considering the influence of the real typhoon scenarios, micro-topographic and transmission corridor information factors. The results demonstrate that the hybrid-driven system resilience assessment and improvement method can assist planners in accurately judging the resilience level of the system against typhoon disasters and selecting the best resilience improvement strategy based on the cost-effectiveness ratio.
On the Accuracy of the One-step UKF and the Two-step UKF
Authors: Ankit Goel, Dennis S. Bernstein
Date: 2022-08-18 00:00:00

The most accurate version of the unscented Kalman filter (UKF) involves the construction of two ensembles. To reduce computational cost, however, UKF is often implemented without the second ensemble. This simplification comes at a price, however, since, for linear systems, the one-step variation of the two-step UKF does not specialize to the classical Kalman filter, with an associated loss of accuracy. This paper remedies this drawback by developing a modified one-step UKF that recovers the classical Kalman filter for linear systems. Numerical examples show that the modified one-step UKF also recovers the accuracy of the two-step UKF in nonlinear systems with linear outputs.
Towards Daily High-resolution Inundation Observations using Deep Learning and EO
Authors: Antara Dasgupta, Lasse Hybbeneth, Björn Waske
Date: 2022-08-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Satellite remote sensing presents a cost-effective solution for synoptic flood monitoring, and satellite-derived flood maps provide a computationally efficient alternative to numerical flood inundation models traditionally used. While satellites do offer timely inundation information when they happen to cover an ongoing flood event, they are limited by their spatiotemporal resolution in terms of their ability to dynamically monitor flood evolution at various scales. Constantly improving access to new satellite data sources as well as big data processing capabilities has unlocked an unprecedented number of possibilities in terms of data-driven solutions to this problem. Specifically, the fusion of data from satellites, such as the Copernicus Sentinels, which have high spatial and low temporal resolution, with data from NASA SMAP and GPM missions, which have low spatial but high temporal resolutions could yield high-resolution flood inundation at a daily scale. Here a Convolutional-Neural-Network is trained using flood inundation maps derived from Sentinel-1 Synthetic Aperture Radar and various hydrological, topographical, and land-use based predictors for the first time, to predict high-resolution probabilistic maps of flood inundation. The performance of UNet and SegNet model architectures for this task is evaluated, using flood masks derived from Sentinel-1 and Sentinel-2, separately with 95 percent-confidence intervals. The Area under the Curve (AUC) of the Precision Recall Curve (PR-AUC) is used as the main evaluation metric, due to the inherently imbalanced nature of classes in a binary flood mapping problem, with the best model delivering a PR-AUC of 0.85.
Understanding Volatility Spillover Relationship Among G7 Nations And India During Covid-19
Authors: Avik Das, Dr. Devanjali Nandi Das
Date: 2022-08-19 00:00:00

Purpose: In the context of a COVID pandemic in 2020-21, this paper attempts to capture the interconnectedness and volatility transmission dynamics. The nature of change in volatility spillover effects and time-varying conditional correlation among the G7 countries and India is investigated. Methodology: To assess the volatility spillover effects, the bivariate BEKK and t- DCC (1,1) GARCH (1,1) models have been used. Our research shows how the dynamics of volatility spillover between India and the G7 countries shift before and during COVID-19. Findings: The findings reveal that the extent of volatility spillover has altered during COVID compared to the pre-COVID environment. During this pandemic, a sharp increase in conditional correlation indicates an increase in systematic risk between countries. Originality: The study contributes to a better understanding of the dynamics of volatility spillover between G7 countries and India. Asset managers and foreign corporations can use the changing spillover dynamics to improve investment decisions and implement effective hedging measures to protect their interests. Furthermore, this research will assist financial regulators in assessing market risk in the future owing to crises like as COVID-19.
On the Estimation of Peer Effects for Sampled Networks
Authors: Mamadou Yauck
Date: 2022-08-19 00:00:00

This paper deals with the estimation of exogeneous peer effects for partially observed networks under the new inferential paradigm of design identification, which characterizes the missing data challenge arising with sampled networks with the central idea that two full data versions which are topologically compatible with the observed data may give rise to two different probability distributions. We show that peer effects cannot be identified by design when network links between sampled and unsampled units are not observed. Under realistic modeling conditions, and under the assumption that sampled units report on the size of their network of contacts, the asymptotic bias arising from estimating peer effects with incomplete network data is characterized, and a bias-corrected estimator is proposed. The finite sample performance of our methodology is investigated via simulations.
The Interactions of Social Norms about Climate Change: Science, Institutions and Economics
Authors: Antonio Cabrales, Manu García, David Ramos Muñoz, Angel Sánchez
Date: 2022-08-19 00:00:00

We study the evolution of interest about climate change between different actors of the population, and how the interest of those actors affect one another. We first document the evolution individually, and then provide a model of cross influences between them, that we then estimate with a VAR. We find large swings over time of said interest for the general public by creating a Climate Change Index for Europe and the US (CCI) using news media mentions, and little interest among economists (measured by publications in top journals of the discipline). The general interest science journals and policymakers have a more steady interest, although policymakers get interested much later.
Integrated modelling approaches for sustainable agri-economic growth and environmental improvement: Examples from Canada, Greece, and Ireland
Authors: Jorge A. Garcia, Angelos Alamanos
Date: 2022-08-18 00:00:00

Complex agricultural problems concern many countries, as the economic motives are increasingly higher, and at the same time the consequences from the irrational resources use and emissions are becoming more evident. In this work we study three of the most common agricultural problems and model them through optimization techniques, showing ways to assess conflicting objectives together as a system and provide overall optimum solutions. The studied problems refer to: i) a water-scarce area with overexploited surface and groundwater resources due to over-pumping for irrigation (Central Greece), ii) a water-abundant area with issues of water quality deterioration caused by agriculture (Southern Ontario, Canada), iii) and a case of intensified agriculture based on animal farming that causes issues of water, soil quality degradation, and increased greenhouse gases emissions (Central Ireland). Linear, non-linear, and Goal Programming optimization techniques have been developed and applied for each case to maximize farmers welfare, make a less intensive use of environmental resources, and control the emission of pollutants. The proposed approaches and their solutions are novel applications for each case-study, compared to the existing literature and practice. Furthermore, they provide useful insights for most countries facing similar problems, they are easily applicable, and developed and solved in publicly available tools such as Python.