pwc-archive/papers-with-abstracts huggingface dataset:
Commercialized Generative AI: A Critical Study of the Feasibility and Ethics of Generating Native Advertising Using Large Language Models in Conversational Web Search
Authors: Ines Zelch, Matthias Hagen, Martin Potthast
Date: 2023-10-07 00:00:00
Tasks: Ethics

How will generative AI pay for itself? Unless charging users for access, selling advertising is the only alternative. Especially in the multi-billion dollar web search market with ads as the main source of revenue, the introduction of a subscription model seems unlikely. The recent disruption of search by generative large language models could thus ultimately be accompanied by generated ads. Our concern is that the commercialization of generative AI in general and large language models in particular could lead to native advertising in the form of quite subtle brand or product placements. In web search, the evolution of search engine results pages (SERPs) from traditional lists of ``ten blue links'' (lists SERPs) to generated text with web page references (text SERPs) may further blur the line between advertising-based and organic search results, making it difficult for users to distinguish between the two, depending on how advertising is integrated and disclosed. To raise awareness of this potential development, we conduct a pilot study analyzing the capabilities of current large language models to blend ads with organic search results. Although the models still struggle to subtly frame ads in an unrelated context, their potential is evident when integrating ads into related topics which calls for further investigation.
Investigating the Influence of Legal Case Retrieval Systems on Users' Decision Process
Authors: Beining Wang, Ruizhe Zhang, Yueyue Wu, Qingyao Ai, Min Zhang, Yiqun Liu
Date: 2023-10-07 00:00:00
Tasks: Decision Making, Information Retrieval, Retrieval

Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand. Previous studies on user behavior analysis have shown that information retrieval (IR) systems can significantly influence users' decisions by presenting results in varying orders and formats. However, whether such influence exists in legal case retrieval remains largely unknown. This study presents the first investigation into the influence of legal case retrieval systems on the decision-making process of legal users. We conducted an online user study involving more than ninety participants, and our findings suggest that the result distribution of legal case retrieval systems indeed affect users' judgements on the sentences in cases. Notably, when users are presented with biased results that involve harsher sentences, they tend to impose harsher sentences on the current case as well. This research highlights the importance of optimizing the unbiasedness of legal case retrieval systems.
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation
Authors: Xinhua Wang, Houping Yue, Zizheng Wang, Liancheng Xu, Jinyu Zhang
Date: 2023-10-07 00:00:00
Tasks: Contrastive Learning, Multi-Task Learning, Self-Supervised Learning, Sequential Recommendation

Cross-domain sequential recommenders (CSRs) are gaining considerable research attention as they can capture user sequential preference by leveraging side information from multiple domains. However, these works typically follow an ideal setup, i.e., different domains obey similar data distribution, which ignores the bias brought by asymmetric interaction densities (a.k.a. the inter-domain density bias). Besides, the frequently adopted mechanism (e.g., the self-attention network) in sequence encoder only focuses on the interactions within a local view, which overlooks the global correlations between different training batches. To this end, we propose an External Attention-enhanced Graph Contrastive Learning framework, namely EA-GCL. Specifically, to remove the impact of the inter-domain density bias, an auxiliary Self-Supervised Learning (SSL) task is attached to the traditional graph encoder under a multi-task learning manner. To robustly capture users' behavioral patterns, we develop an external attention-based sequence encoder that contains an MLP-based memory-sharing structure. Unlike the self-attention mechanism, such a structure can effectively alleviate the bias interference from the batch-based training scheme. Extensive experiments on two real-world datasets demonstrate that EA-GCL outperforms several state-of-the-art baselines on CSR tasks. The source codes and relevant datasets are available at https://github.com/HoupingY/EA-GCL.
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
Authors: Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Zhongxuan Han, Dan Meng, Jun Wang
Date: 2023-10-06 00:00:00
Tasks: Attribute, Recommendation Systems

With the growing privacy concerns in recommender systems, recommendation unlearning, i.e., forgetting the impact of specific learned targets, is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as the unlearning target. However, we find that attackers can extract private information, i.e., gender, race, and age, from a trained model even if it has not been explicitly encountered during training. We name this unseen information as attribute and treat it as the unlearning target. To protect the sensitive attribute of users, Attribute Unlearning (AU) aims to degrade attacking performance and make target attributes indistinguishable. In this paper, we focus on a strict but practical setting of AU, namely Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be performed after the training of the recommendation model is completed. To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance. Specifically, we investigate two types of distinguishability measurements, i.e., user-to-user and distribution-to-distribution. We use the stochastic gradient descent algorithm to optimize our proposed loss. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed methods.
"ABySS: A parallel assembler for short read sequence data"
Authors: Jared T. Simpson, 1 Kim Wong, Shaun D. Jackman, Jacqueline E. Schein, Steven J.M. Jones, and _ Inanc Birol
Date: 2009-02-27 00:00:00
Proceeding: Genome Res. 2009 2

Widespread adoption of massively parallel deoxyribonucleic acid (DNA) sequencing instruments has prompted the recent development of de novo short read assembly algorithms. A common shortcoming of the available tools is their inability to efficiently assemble vast amounts of data generated from large-scale sequencing projects, such as the sequencing of individual human genomes to catalog natural genetic variation. To address this limitation, we developed ABySS (Assembly By Short Sequences), a parallelized sequence assembler. As a demonstration of the capability of our software, we assembled 3.5 billion paired-end reads from the genome of an African male publicly released by Illumina, Inc. Approximately 2.76 million contigs ≥100 base pairs (bp) in length were created with an N50 size of 1499 bp, representing 68% of the reference human genome. Analysis of these contigs identified polymorphic and novel sequences not present in the human reference assembly, which were validated by alignment to alternate human assemblies and to other primate genomes.
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
Authors: Jiayu Chen, Zelai Xu, Yunfei Li, Chao Yu, Jiaming Song, Huazhong Yang, Fei Fang, Yu Wang, Yi Wu
Date: 2023-10-07 00:00:00
Tasks: Multi-agent Reinforcement Learning

Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent reinforcement learning (MARL) can be extremely computationally expensive. Curriculum learning is an effective way to accelerate learning, but an under-explored dimension for generating a curriculum is the difficulty-to-learn of the subgames -- games induced by starting from a specific state. In this work, we present a novel subgame curriculum learning framework for zero-sum games. It adopts an adaptive initial state distribution by resetting agents to some previously visited states where they can quickly learn to improve performance. Building upon this framework, we derive a subgame selection metric that approximates the squared distance to NE values and further adopt a particle-based state sampler for subgame generation. Integrating these techniques leads to our new algorithm, Subgame Automatic Curriculum Learning (SACL), which is a realization of the subgame curriculum learning framework. SACL can be combined with any MARL algorithm such as MAPPO. Experiments in the particle-world environment and Google Research Football environment show SACL produces much stronger policies than baselines. In the challenging hide-and-seek quadrant environment, SACL produces all four emergent stages and uses only half the samples of MAPPO with self-play. The project website is at https://sites.google.com/view/sacl-rl.
DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
Authors: Longxiang He, Li Shen, Linrui Zhang, Junbo Tan, Xueqian Wang
Date: 2023-10-09 00:00:00
Tasks: D4RL, Offline RL, reinforcement-learning

Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning, which is generally solved by advantage weighted regression (AWR). However, previous methods may still encounter out-of-distribution actions due to the limited expressivity of Gaussian-based policies. On the other hand, directly applying the state-of-the-art models with distribution expression capabilities (i.e., diffusion models) in the AWR framework is intractable since AWR requires exact policy probability densities, which is intractable in diffusion models. In this paper, we propose a novel approach, $\textbf{Diffusion-based Constrained Policy Search}$ (dubbed DiffCPS), which tackles the diffusion-based constrained policy search with the primal-dual method. The theoretical analysis reveals that strong duality holds for diffusion-based CPS problems, and upon introducing parameter approximation, an approximated solution can be obtained after $\mathcal{O}(1/\epsilon)$ number of dual iterations, where $\epsilon$ denotes the representation ability of the parametrized policy. Extensive experimental results based on the D4RL benchmark demonstrate the efficacy of our approach. We empirically show that DiffCPS achieves better or at least competitive performance compared to traditional AWR-based baselines as well as recent diffusion-based offline RL methods. The code is now available at https://github.com/felix-thu/DiffCPS.
MAM Faster R-CNN: Improved Faster R-CNN based on Malformed Attention Module for object detection on X-ray security inspection
Authors: Wenming Zhang, Qikai Zhu, Yaqian Li, Haibin Li
Date: 2023-07-01 00:00:00
Proceeding: journal 2023 7
Tasks: object-detection, Object Detection

X-ray security inspection machine is currently the most widely used security inspection technology in our country. It is widely used in urban rail transit, railways, airports, key venues, logistics delivery and other scenarios. Using artificial intelligence technology to assist front-line security inspectors in X-ray security inspection can effectively reduce problems such as missed reports due to personnel fatigue or inattention. However, in actual scenes, the detection accuracy is not high due to the diversity of item types, the particularity of imaging angles, and the limitations of detection algorithms. In order to solve related problems, we propose MAM Faster R-CNN. First, in order to expand the receptive field of the feature map and effectively extract the regional features of the target object with shape distortion in the feature map, we propose the Malformed Attention Module (MAM). Secondly, the Large Kernel Attention (LKA) block is used to connect the corresponding backbone output feature layer to use the adaptive selection feature of self-attention module to better focus on the effective feature information in the feature map. Finally, for the neck part, we replace feature pyramid network (FPN) with path aggregation network (PAN), and add the Conv-MLP block to the bottom-up feature fusion part on the right side of the PAN network to reduce the loss of some low-level details. In this paper, the Faster R-CNN benchmark model with ResNet50 as the backbone network is used to evaluate our proposed MAM Faster R-CNN model on HiXray and OPIXray datasets, and the results show that it outperforms the SOTA detection method. We do ablation experiments on the HiXray dataset, and the results show that our proposed MAM Faster R-CNN improves about 2.0%, compared to baseline model (Faster R-CNN).
Unified speech and gesture synthesis using flow matching
Authors: Shivam Mehta, Ruibo Tu, Simon Alexanderson, Jonas Beskow, Éva Székely, Gustav Eje Henter
Date: 2023-10-08 00:00:00
Tasks: Audio Synthesis, Motion Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

As text-to-speech technologies achieve remarkable naturalness in read-aloud tasks, there is growing interest in multimodal synthesis of verbal and non-verbal communicative behaviour, such as spontaneous speech and associated body gestures. This paper presents a novel, unified architecture for jointly synthesising speech acoustics and skeleton-based 3D gesture motion from text, trained using optimal-transport conditional flow matching (OT-CFM). The proposed architecture is simpler than the previous state of the art, has a smaller memory footprint, and can capture the joint distribution of speech and gestures, generating both modalities together in one single process. The new training regime, meanwhile, enables better synthesis quality in much fewer steps (network evaluations) than before. Uni- and multimodal subjective tests demonstrate improved speech naturalness, gesture human-likeness, and cross-modal appropriateness compared to existing benchmarks. Please see https://shivammehta25.github.io/Match-TTSG/ for video examples and code.
Visual and Textual Deep Feature Fusion for Document Image Classification
Authors: Souhail Bakkali, Ziheng Ming, Mickael Coustaty, Marçal Rusiñol
Date: 2020-06-16 00:00:00
Proceeding: CVPRW 2020 6
Tasks: Classification, document-image-classification, Document Image Classification, image-classification, Image Classification, Retrieval, text-classification, Text Classification

The topic of text document image classification has been explored extensively over the past few years. Most recent approaches handled this task by jointly learning the visual features of document images and their corresponding textual contents. Due to the various structures of document images, the extraction of semantic information from its textual content is beneficial for document image processing tasks such as document retrieval, information extraction, and text classification. In this work, a two-stream neural architecture is proposed to perform the document image classification task. We conduct an exhaustive investigation of nowadays widely used neural networks as well as word embedding procedures used as backbones, in order to extract both visual and textual features from document images. Moreover, a joint feature learning approach that combines image features and text embeddings is introduced as a late fusion methodology. Both the theoretical analysis and the experimental results demonstrate the superiority of our proposed joint feature learning method comparatively to the single modalities. This joint learning approach outperforms the state-of-the-art results with a classification accuracy of 97.05% on the large-scale RVL-CDIP dataset.
Multi-image Tranformer for Multi-focus Image Fusion
Authors: Levent Karacan
Date: 2023-09-20 00:00:00
Proceeding: Signal Processing: Image Communication 2023 9
Tasks: Image Enhancement, Multi Focus Image Fusion

Multi-Focus Image Fusion (MFIF) is an image enhancement task that fuses images in which different regions are in focus to achieve an all-in-focus image. In recent years, Generative Adversarial Networks (GANs)-based approaches have significantly improved the MFIF on Convolutional Neural Network (CNN) architectures. However, despite vision transformers (ViTs) achieving more successful results than CNNs in many high and low-level vision problems due to their ability to provide global connectivity, they have not been employed for MFIF until this study. We develop a Multi-image Transformer (MiT) for MFIF by being inspired by a Spatial-Temporal Transformer Network (STTN) so that global connection can be modeled along multiple input images. We call the proposed transformers-based MFIF model MiT-MFIF as it uses the developed MiT as a core component. We have made various modifications to the baseline transformer to be able to utilize vision transformers in MFIF tasks. Comprehensive experiments on standard MFIF datasets demonstrate the effectiveness of the proposed MiT-MFIF. Moreover, proposed method does not require any post-processing step like in competitor GAN-based methods while outperforming the state-of-the-art MFIF methods.
SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration
Authors: Jingyang Xiang, Siqi Li, Jun Chen, Shipeng Bai, Yukai Ma, Guang Dai, Yong liu
Date: 2023-10-10 00:00:00

The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N consecutive weights along the output channel to be group-wise non-zero, the recent network with 1$\times$N sparsity has received tremendous popularity for its three outstanding advantages: 1) A large amount of storage space saving by a \emph{Block Sparse Row} matrix. 2) Excellent performance at a high sparsity. 3) Significant speedups on CPUs with Advanced Vector Extensions. Recent work requires selecting and fine-tuning 1$\times$N sparse weights based on dense pre-trained weights, leading to the problems such as expensive training cost and memory access, sub-optimal model quality, as well as unbalanced workload across threads (different sparsity across output channels). To overcome them, this paper proposes a novel \emph{\textbf{S}oft \textbf{U}niform \textbf{B}lock \textbf{P}runing} (SUBP) approach to train a uniform 1$\times$N sparse structured network from scratch. Specifically, our approach tends to repeatedly allow pruned blocks to regrow to the network based on block angular redundancy and importance sampling in a uniform manner throughout the training process. It not only makes the model less dependent on pre-training, reduces the model redundancy and the risk of pruning the important blocks permanently but also achieves balanced workload. Empirically, on ImageNet, comprehensive experiments across various CNN architectures show that our SUBP consistently outperforms existing 1$\times$N and structured sparsity methods based on pre-trained models or training from scratch. Source codes and models are available at \url{https://github.com/JingyangXiang/SUBP}.
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning
Authors: Atsushi Ueshima, Shayegan Omidshafiei, Hirokazu Shirado
Date: 2023-10-06 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Cooperation is challenging in biological systems, human societies, and multi-agent systems in general. While a group can benefit when everyone cooperates, it is tempting for each agent to act selfishly instead. Prior human studies show that people can overcome such social dilemmas while choosing interaction partners, i.e., strategic network rewiring. However, little is known about how agents, including humans, can learn about cooperation from strategic rewiring and vice versa. Here, we perform multi-agent reinforcement learning simulations in which two agents play the Prisoner's Dilemma game iteratively. Each agent has two policies: one controls whether to cooperate or defect; the other controls whether to rewire connections with another agent. This setting enables us to disentangle complex causal dynamics between cooperation and network rewiring. We find that network rewiring facilitates mutual cooperation even when one agent always offers cooperation, which is vulnerable to free-riding. We then confirm that the network-rewiring effect is exerted through agents' learning of ostracism, that is, connecting to cooperators and disconnecting from defectors. However, we also find that ostracism alone is not sufficient to make cooperation emerge. Instead, ostracism emerges from the learning of cooperation, and existing cooperation is subsequently reinforced due to the presence of ostracism. Our findings provide insights into the conditions and mechanisms necessary for the emergence of cooperation with network rewiring.
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
Authors: Fred Hohman, Mary Beth Kery, Donghao Ren, Dominik Moritz
Date: 2023-10-06 00:00:00
Tasks: Model Compression

On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday personal devices. However, today's large ML models must be drastically compressed to run efficiently on-device, a hurtle that requires deep, yet currently niche expertise. To engage the broader human-centered ML community in on-device ML experiences, we present the results from an interview study with 30 experts at Apple that specialize in producing efficient models. We compile tacit knowledge that experts have developed through practical experience with model compression across different hardware platforms. Our findings offer pragmatic considerations missing from prior work, covering the design process, trade-offs, and technical strategies that go into creating efficient models. Finally, we distill design recommendations for tooling to help ease the difficulty of this work and bring on-device ML into to more widespread practice.