pwc-archive/papers-with-abstracts huggingface dataset:
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation
Authors: Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia
Date: 2024-03-07 00:00:00
Tasks: Benchmarking, Multimodal Recommendation

In this work, we introduce Ducho 2.0, the latest stable version of our framework. Differently from Ducho, Ducho 2.0 offers a more personalized user experience with the definition and import of custom extraction models fine-tuned on specific tasks and datasets. Moreover, the new version is capable of extracting and processing features through multimodal-by-design large models. Notably, all these new features are supported by optimized data loading and storing to the local memory. To showcase the capabilities of Ducho 2.0, we demonstrate a complete multimodal recommendation pipeline, from the extraction/processing to the final recommendation. The idea is to provide practitioners and experienced scholars with a ready-to-use tool that, put on top of any multimodal recommendation framework, may permit them to run extensive benchmarking analyses. All materials are accessible at: \url{https://github.com/sisinflab/Ducho}.
The 2nd Workshop on Recommendation with Generative Models
Authors: Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong liu, Xiangyu Zhao, Wayne Xin Zhao, Yang song, Xiangnan He
Date: 2024-03-07 00:00:00
Tasks: Recommendation Systems

The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems.
DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Authors: Leilei Ding, Dazhong Shen, Chao Wang, Tianfu Wang, Le Zhang, Yanyong Zhang
Date: 2024-03-07 00:00:00
Tasks: Recommendation Systems

Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node information and topology. However, these models often face the famous over-smoothing issue, leading to indistinct user and item embeddings and reduced personalization. Traditional desmoothing methods in GCN-based systems are model-specific, lacking a universal solution. This paper introduces a novel, model-agnostic approach named \textbf{D}esmoothing Framework for \textbf{G}CN-based \textbf{R}ecommendation Systems (\textbf{DGR}). It effectively addresses over-smoothing on general GCN-based recommendation models by considering both global and local perspectives. Specifically, we first introduce vector perturbations during each message passing layer to penalize the tendency of node embeddings approximating overly to be similar with the guidance of the global topological structure. Meanwhile, we further develop a tailored-design loss term for the readout embeddings to preserve the local collaborative relations between users and their neighboring items. In particular, items that exhibit a high correlation with neighboring items are also incorporated to enhance the local topological information. To validate our approach, we conduct extensive experiments on 5 benchmark datasets based on 5 well-known GCN-based recommendation models, demonstrating the effectiveness and generalization of our proposed framework.
SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation
Authors: Chi Zhang, Qilong Han, Rui Chen, Xiangyu Zhao, Peng Tang, Hongtao Song
Date: 2024-03-07 00:00:00
Tasks: Denoising, Sequential Recommendation

Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored modeling sequentiality and correlations in sequences to implicitly or explicitly reduce noise's influence. However, relying on only available intra-sequence information (i.e., sequentiality and correlations in a sequence) is insufficient and may result in over-denoising and under-denoising problems (OUPs), especially for short sequences. To improve reliability, we propose to augment sequences by inserting items before denoising. However, due to the data sparsity issue and computational costs, it is challenging to select proper items from the entire item universe to insert into proper positions in a target sequence. Motivated by the above observation, we propose a novel framework--Self-augmented Sequence Denoising for sequential Recommendation (SSDRec) with a three-stage learning paradigm to solve the above challenges. In the first stage, we empower SSDRec by a global relation encoder to learn multi-faceted inter-sequence relations in a data-driven manner. These relations serve as prior knowledge to guide subsequent stages. In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs. Finally, we employ a hierarchical denoising module in the third stage to reduce the risk of false augmentations and pinpoint all noise in raw sequences. Extensive experiments on five real-world datasets demonstrate the superiority of \model over state-of-the-art denoising methods and its flexible applications to mainstream sequential recommendation models. The source code is available at https://github.com/zc-97/SSDRec.
Towards Robustness Analysis of E-Commerce Ranking System
Authors: Ningfei Wang, Yupin Huang, Han Cheng, Jiri Gesi, Xiaojie Wang, Vivek Mittal
Date: 2024-03-07 00:00:00
Tasks: Information Retrieval

Information retrieval (IR) is a pivotal component in various applications. Recent advances in machine learning (ML) have enabled the integration of ML algorithms into IR, particularly in ranking systems. While there is a plethora of research on the robustness of ML-based ranking systems, these studies largely neglect commercial e-commerce systems and fail to establish a connection between real-world and manipulated query relevance. In this paper, we present the first systematic measurement study on the robustness of e-commerce ranking systems. We define robustness as the consistency of ranking outcomes for semantically identical queries. To quantitatively analyze robustness, we propose a novel metric that considers both ranking position and item-specific information that are absent in existing metrics. Our large-scale measurement study with real-world data from e-commerce retailers reveals an open opportunity to measure and improve robustness since semantically identical queries often yield inconsistent ranking results. Based on our observations, we propose several solution directions to enhance robustness, such as the use of Large Language Models. Note that the issue of robustness discussed herein does not constitute an error or oversight. Rather, in scenarios where there exists a vast array of choices, it is feasible to present a multitude of products in various permutations, all of which could be equally appealing. However, this extensive selection may lead to customer confusion. As e-commerce retailers use various techniques to improve the quality of search results, we hope that this research offers valuable guidance for measuring the robustness of the ranking systems.
GopGAN: Gradients Orthogonal Projection Generative Adversarial Network With Continual Learning
Authors: Xiaobin Li; Weiqiang Wang
Date: 2021-07-16 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2021 7
Tasks: Continual Learning, Generative Adversarial Network

The generative adversarial networks (GANs) in continual learning suffer from catastrophic forgetting. In continual learning, GANs tend to forget about previous generation tasks and only remember the tasks they just learned. In this article, we present a novel conditional GAN, called the gradients orthogonal projection GAN (GopGAN), which updates the weights in the orthogonal subspace of the space spanned by the representations of training examples, and we also mathematically demonstrate its ability to retain the old knowledge about learned tasks in learning a new task. Furthermore, the orthogonal projection matrix for modulating gradients is mathematically derived and its iterative calculation algorithm for continual learning is given so that training examples for learned tasks do not need to be stored when learning a new task. In addition, a task-dependent latent vector construction is presented and the constructed conditional latent vectors are used as the inputs of generator in GopGAN to avoid the disappearance of orthogonal subspace of learned tasks. Extensive experiments on MNIST, EMNIST, SVHN, CIFAR10, and ImageNet-200 generation tasks show that the proposed GopGAN can effectively cope with the issue of catastrophic forgetting and stably retain learned knowledge.
QBox: Partial Transfer Learning with Active Querying for Object Detection
Authors: Ying-Peng Tang，Xiu-Shen Wei，Borui Zhao，Sheng-Jun Huang
Date: 2021-09-27 00:00:00
Proceeding: journal 2021 9
Tasks: Image Retrieval, Informativeness, object-detection, Object Detection, Transfer Learning

Object detection requires plentiful data annotated with bounding boxes for model training. However, in many applications, it is difficult or even impossible to acquire a large set of labeled examples for the target task due to the privacy concern or lack of reliable annotators. On the other hand, due to the high-quality image search engines, such as Flickr and Google , it is relatively easy to obtain resource-rich unlabeled datasets, whose categories are a superset of those of target data. In this article, to improve the target model with cost-effective supervision from source data, we propose a partial transfer learning approach QBox to actively query labels for bounding boxes of source images. Specifically, we design two criteria, i.e., informativeness and transferability, to measure the potential utility of a bounding box for improving the target model. Based on these criteria, QBox actively queries the labels of the most useful boxes from the source domain and, thus, requires fewer training examples to save the labeling cost. Furthermore, the proposed query strategy allows annotators to simply labeling a specific region, instead of the whole image, and, thus, significantly reduces the labeling difficulty. Extensive experiments are performed on various partial transfer benchmarks and a real COVID-19 detection task. The results validate that QBox improves the detection accuracy with lower labeling cost compared to state-of-the-art query strategies for object detection.
Common solution to a pair of non-linear matrix equations via fixed point results
Authors: Hiranmoy Garai and Lakshmi Kanta Dey
Date: 2019-04-26 00:00:00
Proceeding: Springer 2019 4

In this paper, we propose an idea to derive some sufficient conditions
for the existence and uniqueness of a positive definite common
solution to a pair of non-linear matrix equations. To proceed with this,
we present some interesting common fixed point results involving a couple
of altering distance functions along with another control function in
Banach spaces. Based on these results, we deduce some desired sufficient
conditions for the existence and uniqueness of a positive definite common
solution to the said pair of non-linear matrix equations. We also
point out a probable applicable area of our findings. Finally, we propose
an entirely non-trivial example to validate one of the main results with
a numerical approximation of convergence of iterated sequence using a
diagram.
Distilled Gradual Pruning with Pruned Fine-tuning
Authors: Federico Fontana, Romeo Lanzino, Marco Raoul Marini, Danilo Avola, Luigi Cinque, Francesco Scarcello, Gian Luca Foresti
Date: 2024-02-15 00:00:00
Proceeding: IEEE Transactions on Artificial Intelligence 2024 2
Tasks: Image Classification, Knowledge Distillation, Self-Knowledge Distillation

Neural Networks (NNs) have been driving machine learning progress in recent years, but their larger models present challenges in resource-limited environments. Weight pruning reduces the computational demand, often with performance degradation and long training procedures. This work introduces Distilled Gradual Pruning with Pruned Fine-tuning (DG2PF), a comprehensive algorithm that iteratively prunes pre-trained neural networks using knowledge distillation. We employ a magnitude-based unstructured pruning function that selectively removes a specified proportion of unimportant weights from the network. This function also leads to an efficient compression of the model size while minimizing classification accuracy loss. Additionally, we introduce a simulated pruning strategy with the same effects of weight recovery but while maintaining stable convergence. Furthermore, we propose a multi-step self-knowledge distillation strategy to effectively transfer the knowledge of the full, unpruned network to the pruned counterpart. We validate the performance of our algorithm through extensive experimentation on diverse benchmark datasets, including CIFAR-10 and ImageNet, as well as a set of model architectures. The results highlight how our algorithm prunes and optimizes pretrained neural networks without substantially degrading their classification accuracy while delivering significantly faster and more compact models.
Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
Authors: Philipp Schmidt, Sören Arlt, Carlos Ruiz-Gonzalez, Xuemei Gu, Carla Rodríguez, Mario Krenn
Date: 2024-02-20 00:00:00
Tasks: scientific discovery

Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive Virtual Reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way - as a human-in-the-loop - to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR for increasing a human researcher's ability to derive knowledge from graph-based generative AI that, which is a common abstract data representation used in diverse fields of science.
Tweaking autoregressive methods for inpainting of gaps in audio signals
Authors: Ondřej Mokrý, Pavel Rajmic
Date: 2024-03-07 00:00:00
Tasks: Audio inpainting

A novel variant of the Janssen method for audio inpainting is presented and compared to other popular audio inpainting methods based on autoregressive (AR) modeling. Both conceptual differences and practical implications are discussed. The experiments demonstrate the importance of the choice of the AR model estimator, window/context length, and model order. The results show the superiority of the proposed gap-wise Janssen approach using objective metrics, which is confirmed by a listening test.
Real-time Regulation of Detention Ponds via Feedback Control: Balancing Flood Mitigation and Water Quality
Authors: Marcus Nóbrega Gomes Jr, Ahmad F. Taha, Luis Miguel C. Rápallo, Eduardo M. Mendiondo, Marcio H. Giacomoni
Date: 2024-03-07 00:00:00
Tasks: Model Predictive Control

Detention ponds can mitigate flooding and improve water quality by allowing the settlement of pollutants. Typically, they are operated with fully open orifices and weirs (i.e., passive control). Active controls can improve the performance of these systems: orifices can be retrofitted with controlled valves and spillways can have controllable gates. The real-time optimal operation of its hydraulic devices can be achieved with techniques such as Model Predictive Control (MPC). A distributed quasi-2D hydrologic-hydrodynamic coupled with a reservoir flood routing model is developed and integrated with an MPC algorithm to estimate the operation of valves and movable gates. The control optimization problem is adapted to switch from a flood-related algorithm focusing on mitigating floods to a heuristic objective function that aims to increase the detention time when no inflow hydrographs are predicted. The case studies show the potential of applying the methods developed in a catchment in Sao Paulo, Brazil. The performance of MPC compared to alternatives with either fully or partially open valves and gates are tested. Comparisons with HEC-RAS 2D indicate volume and peak flow errors of approximately 1.4% and 0.91% for the watershed module. Simulating two consecutive 10-year storms shows that the MPC strategy can achieve peak flow reductions of 79%. In contrast, passive control has nearly half of the performance. A 1-year continuous simulation results show that the passive scenario with 25% of the valves opened can treat 12% more runoff compared to the developed MPC approach, with an average detention time of approximately 6 hours. For the MPC approach, the average detention time is nearly 14 hours indicating that both control techniques can treat similar volumes; however, the proxy water quality for the MPC approach is enhanced due to the longer detention times.
Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees
Authors: Riccardo Zuliani, Efe C. Balta, John Lygeros
Date: 2024-03-07 00:00:00
Tasks: Model Predictive Control

Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We propose a principled way to tune the cost function and the constraints of linear MPC schemes to improve the closed-loop performance and robust constraint satisfaction on uncertain nonlinear dynamics with additive noise. The tuning is performed using a novel MPC tuning algorithm based on backpropagation developed in our earlier work. Using the scenario approach, we provide probabilistic bounds on the likelihood of closed-loop constraint violation over a finite horizon. We showcase the effectiveness of the proposed method on linear and nonlinear simulation examples.
Implementation of soft-constrained MPC for Tracking using its semi-banded problem structure
Authors: Victor Gracia, Pablo Krupa, Daniel Limon, Teodoro Alamo
Date: 2024-03-07 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function. However, in practice, these constraints can result in feasibility issues, either because the system model is not accurate or due to the existence of external disturbances. To mitigate this problem, a solution adopted by the MPC community is the use of soft constraints. In this article, we consider a not-so-typical methodology to encode soft constraints in a particular MPC formulation known as MPC for Tracking (MPCT), which has several advantages when compared to standard MPC formulations. The motivation behind the proposed encoding is to maintain the semi-banded structure of the ingredients of a recently proposed solver for the considered MPCT formulation, thus providing an efficient and fast solver when compared to alternative approaches from the literature. We show numerical results highlighting the benefits of the formulation and the computational efficiency of the solver.
Tensor Power Flow Formulations for Multidimensional Analyses in Distribution Systems
Authors: Edgar Mauricio Salazar Duque, Juan S. Giraldo, Pedro P. Vergara, Phuong H. Nguyen, Han, Slootweg
Date: 2024-03-07 00:00:00
Tasks: CPU, GPU

In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of power flows in distribution systems. The presented algorithms are the base for a new TensorPowerFlow (TPF) tool and shine for their simplicity, benefiting from multicore \gls{cpu} and \gls{gpu} parallelization. We also focus on the mathematical convergence properties of the algorithm, showing that its unique solution is at the practical operational point, which is the solution of high-voltage and low-current. The proof is validated using numerical simulations showing the robustness of the FPI algorithm compared to the classical \gls{nr} approach. In the case study, a benchmark with different PF solution methods is performed, showing that for applications requiring a yearly simulation at 1-minute resolution the computation time is decreased by a factor of 164, compared to the NR in its sparse formulation.
Model-based pressure tracking using a feedback linearisation technique in thermoplastic injection moulding
Authors: Mandana Kariminejad, David Tormey, Marion McAfee
Date: 2024-03-07 00:00:00

Injection moulding is a well-established automated process for manufacturing a wide variety of plastic components in large volumes and with high precision. There are, however, process control challenges associated with each stage of injection moulding, which should be monitored and controlled precisely to prevent defects in the injection moulded component. One of the process variables is the pressure profile during the injection and packing phases, which has a direct impact on the quality of the manufactured part. This research proposes a model-based controller design for the injection and cavity pressure during the moulding cycle, with a feedback linearisation controller. First, the injection and packing phases were mathematically modelled and converted to a state-space model. The procedure of designing the controller for the process was outlined. A pressure profile was defined as the target trajectory in the proposed controller and the ability of the designed controller to follow the set profile was explored.