pwc-archive/papers-with-abstracts huggingface dataset:
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Authors: Lina Magoula, Nikolaos Koursioumpas, Alexandros-Ioannis Thanopoulos, Theodora Panagea, Nikolaos Petropouleas, M. A. Gutierrez-Estevez, Ramin Khalili
Date: 2023-06-25 00:00:00
Tasks: Federated Learning

Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. Towards mitigating the carbon footprint of FL, the current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization, by orchestrating the computational and communication resources of the involved devices, while guaranteeing a certain FL model performance target. A penalty function is introduced in the offline phase of the GA that penalizes the strategies that violate the constraints of the environment, ensuring a safe GA process. Evaluation results show the effectiveness of the proposed scheme compared to two state-of-the-art baseline solutions, achieving a decrease of up to 83% in the total energy consumption.
Conformal link prediction for false discovery rate control
Authors: Ariane Marandon
Date: 2023-06-26 00:00:00
Tasks: Link Prediction, Prediction

Most link prediction methods return estimates of the connection probability of missing edges in a graph. Such output can be used to rank the missing edges from most to least likely to be a true edge, but does not directly provide a classification into true and non-existent. In this work, we consider the problem of identifying a set of true edges with a control of the false discovery rate (FDR). We propose a novel method based on high-level ideas from the literature on conformal inference. The graph structure induces intricate dependence in the data, which we carefully take into account, as this makes the setup different from the usual setup in conformal inference, where data exchangeability is assumed. The FDR control is empirically demonstrated for both simulated and real data.
Private Networked Federated Learning for Nonsmooth Objectives
Authors: François Gauthier, Cristiano Gratton, Naveen K. D. Venkategowda, Stefan Werner
Date: 2023-06-24 00:00:00
Tasks: Federated Learning, Privacy Preserving

This paper develops a networked federated learning algorithm to solve nonsmooth objective functions. To guarantee the confidentiality of the participants with respect to each other and potential eavesdroppers, we use the zero-concentrated differential privacy notion (zCDP). Privacy is achieved by perturbing the outcome of the computation at each client with a variance-decreasing Gaussian noise. ZCDP allows for better accuracy than the conventional $(\epsilon, \delta)$-DP and stronger guarantees than the more recent R\'enyi-DP by assuming adversaries aggregate all the exchanged messages. The proposed algorithm relies on the distributed Alternating Direction Method of Multipliers (ADMM) and uses the approximation of the augmented Lagrangian to handle nonsmooth objective functions. The developed private networked federated learning algorithm has a competitive privacy accuracy trade-off and handles nonsmooth and non-strongly convex problems. We provide complete theoretical proof for the privacy guarantees and the algorithm's convergence to the exact solution. We also prove under additional assumptions that the algorithm converges in $O(1/n)$ ADMM iterations. Finally, we observe the performance of the algorithm in a series of numerical simulations.
Learned Mappings for Targeted Free Energy Perturbation between Peptide Conformations
Authors: Soohaeng Yoo Willow, Lulu Kang, David D. L. Minh
Date: 2023-06-24 00:00:00

Targeted free energy perturbation uses an invertible mapping to promote configuration space overlap and the convergence of free energy estimates. However, developing suitable mappings can be challenging. Wirnsberger et al. (2020) demonstrated the use of machine learning to train deep neural networks that map between Boltzmann distributions for different thermodynamic states. Here, we adapt their approach to free energy differences of a flexible bonded molecule, deca-alanine, with harmonic biases with different spring centers. When the neural network is trained until ``early stopping'' - when the loss value of the test set increases - we calculate accurate free energy differences between thermodynamic states with spring centers separated by 1 \r{A} and sometimes 2 \r{A}. For more distant thermodynamic states, the mapping does not produce structures representative of the target state and the method does not reproduce reference calculations.
Regularized Multivariate Functional Principal Component Analysis
Authors: Hossein Haghbin, Yue Zhao, Mehdi Maadooliat
Date: 2023-06-24 00:00:00

Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data. However, controlling the roughness of the extracted Principal Components (PCs) can be challenging. This paper introduces a novel approach called regularized MFPCA (ReMFPCA) to address this issue and enhance the smoothness and interpretability of the multivariate functional PCs. ReMFPCA incorporates a roughness penalty within a penalized framework, using a parameter vector to regulate the smoothness of each functional variable. The proposed method generates smoothed multivariate functional PCs, providing a concise and interpretable representation of the data. Extensive simulations and real data examples demonstrate the effectiveness of ReMFPCA and its superiority over alternative methods. The proposed approach opens new avenues for analyzing and uncovering relationships in complex multivariate functional datasets.
Selective inference using randomized group lasso estimators for general models
Authors: Yiling Huang, Sarah Pirenne, Snigdha Panigrahi, Gerda Claeskens
Date: 2023-06-24 00:00:00
Tasks: Nutrition

Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions. The method includes the use of exponential family distributions, as well as quasi-likelihood modeling for overdispersed count data, for example, and allows for categorical or grouped covariates as well as continuous covariates. A randomized group-regularized optimization problem is studied. The added randomization allows us to construct a post-selection likelihood which we show to be adequate for selective inference when conditioning on the event of the selection of the grouped covariates. This likelihood also provides a selective point estimator, accounting for the selection by the group lasso. Confidence regions for the regression parameters in the selected model take the form of Wald-type regions and are shown to have bounded volume. The selective inference method for grouped lasso is illustrated on data from the national health and nutrition examination survey while simulations showcase its behaviour and favorable comparison with other methods.
Estimating the Value of Evidence-Based Decision Making
Authors: Alberto Abadie, Anish Agarwal, Guido Imbens, Siwei Jia, James McQueen, Serguei Stepaniants
Date: 2023-06-21 00:00:00
Tasks: Decision Making

Business/policy decisions are often based on evidence from randomized experiments and observational studies. In this article we propose an empirical framework to estimate the value of evidence-based decision making (EBDM) and the return on the investment in statistical precision.
Taming the Exponential Action Set: Sublinear Regret and Fast Convergence to Nash Equilibrium in Online Congestion Games
Authors: Jing Dong, Jingyu Wu, Siwei Wang, Baoxiang Wang, Wei Chen
Date: 2023-06-19 00:00:00

The congestion game is a powerful model that encompasses a range of engineering systems such as traffic networks and resource allocation. It describes the behavior of a group of agents who share a common set of $F$ facilities and take actions as subsets with $k$ facilities. In this work, we study the online formulation of congestion games, where agents participate in the game repeatedly and observe feedback with randomness. We propose CongestEXP, a decentralized algorithm that applies the classic exponential weights method. By maintaining weights on the facility level, the regret bound of CongestEXP avoids the exponential dependence on the size of possible facility sets, i.e., $\binom{F}{k} \approx F^k$, and scales only linearly with $F$. Specifically, we show that CongestEXP attains a regret upper bound of $O(kF\sqrt{T})$ for every individual player, where $T$ is the time horizon. On the other hand, exploiting the exponential growth of weights enables CongestEXP to achieve a fast convergence rate. If a strict Nash equilibrium exists, we show that CongestEXP can converge to the strict Nash policy almost exponentially fast in $O(F\exp(-t^{1-\alpha}))$, where $t$ is the number of iterations and $\alpha \in (1/2, 1)$.
Reciprocal Sequential Recommendation
Authors: Bowen Zheng, Yupeng Hou, Wayne Xin Zhao, Yang song, HengShu Zhu
Date: 2023-06-26 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties. Although dynamic user modeling has been well-studied in sequential recommender systems, existing solutions are developed in a user-oriented manner. Therefore, it is non-trivial to adapt sequential recommendation algorithms to reciprocal recommendation. In this paper, we formulate RRS as a distinctive sequence matching task, and further propose a new approach ReSeq for RRS, which is short for Reciprocal Sequential recommendation. To capture dual-perspective matching, we propose to learn fine-grained sequence similarities by co-attention mechanism across different time steps. Further, to improve the inference efficiency, we introduce the self-distillation technique to distill knowledge from the fine-grained matching module into the more efficient student module. In the deployment stage, only the efficient student module is used, greatly speeding up the similarity computation. Extensive experiments on five real-world datasets from two scenarios demonstrate the effectiveness and efficiency of the proposed method. Our code is available at https://github.com/RUCAIBox/ReSeq/.
RecBaselines2023: a new dataset for choosing baselines for recommender models
Authors: Veronika Ivanova, Oleg Lashinin, Marina Ananyeva, Sergey Kolesnikov
Date: 2023-06-25 00:00:00
Tasks: Collaborative Filtering, Descriptive

The number of proposed recommender algorithms continues to grow. The authors propose new approaches and compare them with existing models, called baselines. Due to the large number of recommender models, it is difficult to estimate which algorithms to choose in the article. To solve this problem, we have collected and published a dataset containing information about the recommender models used in 903 papers, both as baselines and as proposed approaches. This dataset can be seen as a typical dataset with interactions between papers and previously proposed models. In addition, we provide a descriptive analysis of the dataset and highlight possible challenges to be investigated with the data. Furthermore, we have conducted extensive experiments using a well-established methodology to build a good recommender algorithm under the dataset. Our experiments show that the selection of the best baselines for proposing new recommender approaches can be considered and successfully solved by existing state-of-the-art collaborative filtering models. Finally, we discuss limitations and future work.
Enhancing Dynamic Image Advertising with Vision-Language Pre-training
Authors: Zhoufutu Wen, Xinyu Zhao, Zhipeng Jin, Yi Yang, Wei Jia, Xiaodong Chen, Shuanglong Li, Lin Liu
Date: 2023-06-25 00:00:00
Tasks: Image Retrieval, Retrieval

In the multimedia era, image is an effective medium in search advertising. Dynamic Image Advertising (DIA), a system that matches queries with ad images and generates multimodal ads, is introduced to improve user experience and ad revenue. The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling. Current query-image matching suffers from limited and inconsistent data, and insufficient cross-modal interaction. Also, the separate optimization of retrieval and relevance models affects overall performance. To address this issue, we propose a vision-language framework consisting of two parts. First, we train a base model on large-scale image-text pairs to learn general multimodal representation. Then, we fine-tune the base model on advertising business data, unifying relevance modeling and retrieval through multi-objective learning. Our framework has been implemented in Baidu search advertising system "Phoneix Nest". Online evaluation shows that it improves cost per mille (CPM) and click-through rate (CTR) by 1.04% and 1.865%.
Cross-domain Recommender Systems via Multimodal Domain Adaptation
Authors: Ramya Kamani, Vikas Kumar, Venkateswara Rao Kagita
Date: 2023-06-24 00:00:00
Tasks: Collaborative Filtering, Domain Adaptation, Entity Alignment, Recommendation Systems, Transfer Learning

Collaborative Filtering (CF) has emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of individuals to generate personalized recommendations. CF techniques, especially for newly launched platforms, often face a critical issue known as the data sparsity problem, which greatly limits their performance. Several approaches in the literature have been proposed to tackle the problem of data sparsity, among which cross-domain collaborative filtering (CDCF) has gained significant attention in the recent past. In order to compensate for the scarcity of available feedback in a target domain, the CDCF approach utilizes information available in other auxiliary domains. Traditional CDCF approaches primarily focus on finding a common set of entities (users or items) across the domains, which then act as a conduit for knowledge transfer. Nevertheless, most real-world datasets are collected from different domains, so they often lack information about anchor points or reference information for entity alignment. This paper introduces a domain adaptation technique to align the embeddings of entities across the two domains. Our approach first exploits the available textual and visual information to independently learn a multi-view latent representation for each entity in the auxiliary and target domains. The different representations of the entity are then fused to generate the corresponding unified representation. A domain classifier is then trained to learn the embedding for the domain alignment by fixing the unified features as the anchor points. Experiments on two publicly benchmark datasets indicate the effectiveness of our proposed approach.
Retrieving Supporting Evidence for LLMs Generated Answers
Authors: Siqing Huo, Negar Arabzadeh, Charles L. A. Clarke
Date: 2023-06-23 00:00:00
Tasks: Open-Domain Question Answering, Question Answering, Retrieval

Current large language models (LLMs) can exhibit near-human levels of performance on many natural language tasks, including open-domain question answering. Unfortunately, they also convincingly hallucinate incorrect answers, so that responses to questions must be verified against external sources before they can be accepted at face value. In this paper, we report a simple experiment to automatically verify generated answers against a corpus. After presenting a question to an LLM and receiving a generated answer, we query the corpus with the combination of the question + generated answer. We then present the LLM with the combination of the question + generated answer + retrieved answer, prompting it to indicate if the generated answer can be supported by the retrieved answer. We base our experiment on questions and passages from the MS MARCO (V1) test collection, exploring three retrieval approaches ranging from standard BM25 to a full question answering stack, including a reader based on the LLM. For a large fraction of questions, we find that an LLM is capable of verifying its generated answer if appropriate supporting material is provided. However, with an accuracy of 70-80%, this approach cannot be fully relied upon to detect hallucinations.
Understand Customer Behavior And Complaints Eight areas of quantifiable data can be integrated into quality assurance decisions
Authors: John Goodman, Steve Newman
Date: 2023-01-01 00:00:00
Proceeding: QUAL ITY PROGRESS 2023 1
Tasks: Marketing

USTOMER COMPLAINTS PROVIDE
valuable quality assurance, service and
marketing data. But the challenge is to
use the data to make decisions that
result in substantive action.
To use complaint data to solve problems in design, marketing, installation, distribution
and after sale use and maintenance, you
should have a basic understanding of
customer complaint and market
behavior.
This understanding will provide a framework for interpreting the data and extrapolating
it to the entire customer base.
The framework will allow
organizations not only to quantify the implications of the data
but also to set priorities and allocate scarce quality assurance resources to mitigate problems.
In fact, unsolicited complaints submitted at the
time a problem occurs are less costly than systematic sampling and inspection and provide more timely information than is typically available from
warranty data.
Eight factors about customer behavior are key to
Intersectionality and Testimonial Injustice in Medical Records
Authors: Kenya S. Andrews, Bhuvani Shah, Lu Cheng
Date: 2023-06-20 00:00:00
Tasks: Fairness

Detecting testimonial injustice is an essential element of addressing inequities and promoting inclusive healthcare practices, many of which are life-critical. However, using a single demographic factor to detect testimonial injustice does not fully encompass the nuanced identities that contribute to a patient's experience. Further, some injustices may only be evident when examining the nuances that arise through the lens of intersectionality. Ignoring such injustices can result in poor quality of care or life-endangering events. Thus, considering intersectionality could result in more accurate classifications and just decisions. To illustrate this, we use real-world medical data to determine whether medical records exhibit words that could lead to testimonial injustice, employ fairness metrics (e.g. demographic parity, differential intersectional fairness, and subgroup fairness) to assess the severity to which subgroups are experiencing testimonial injustice, and analyze how the intersectionality of demographic features (e.g. gender and race) make a difference in uncovering testimonial injustice. From our analysis, we found that with intersectionality we can better see disparities in how subgroups are treated and there are differences in how someone is treated based on the intersection of their demographic attributes. This has not been previously studied in clinical records, nor has it been proven through empirical study.
Deceptive AI Ecosystems: The Case of ChatGPT
Authors: Xiao Zhan, Yifan Xu, Stefan Sarkadi
Date: 2023-06-18 00:00:00
Tasks: Chatbot

ChatGPT, an AI chatbot, has gained popularity for its capability in generating human-like responses. However, this feature carries several risks, most notably due to its deceptive behaviour such as offering users misleading or fabricated information that could further cause ethical issues. To better understand the impact of ChatGPT on our social, cultural, economic, and political interactions, it is crucial to investigate how ChatGPT operates in the real world where various societal pressures influence its development and deployment. This paper emphasizes the need to study ChatGPT "in the wild", as part of the ecosystem it is embedded in, with a strong focus on user involvement. We examine the ethical challenges stemming from ChatGPT's deceptive human-like interactions and propose a roadmap for developing more transparent and trustworthy chatbots. Central to our approach is the importance of proactive risk assessment and user participation in shaping the future of chatbot technology.
What drives the acceptance of AI technology?: the role of expectations and experiences
Authors: Minsang Yi, Hanbyul Choi
Date: 2023-06-17 00:00:00

In recent years, Artificial intelligence products and services have been offered potential users as pilots. The acceptance intention towards artificial intelligence is greatly influenced by the experience with current AI products and services, expectations for AI, and past experiences with ICT technology. This study aims to explore the factors that impact AI acceptance intention and understand the process of its formation. The analysis results of this study reveal that AI experience and past ICT experience affect AI acceptance intention in two ways. Through the direct path, higher AI experience and ICT experience are associated with a greater intention to accept AI. Additionally, there is an indirect path where AI experience and ICT experience contribute to increased expectations for AI, and these expectations, in turn, elevate acceptance intention. Based on the findings, several recommendations are suggested for companies and public organizations planning to implement artificial intelligence in the future. It is crucial to manage the user experience of ICT services and pilot AI products and services to deliver positive experiences. It is essential to provide potential AI users with specific information about the features and benefits of AI products and services. This will enable them to develop realistic expectations regarding AI technology.
Efficient and Scalable MIV-transistor with Extended Gate in Monolithic 3D Integration
Authors: Madhava Sarma Vemuri, Umamaheswara Rao Tida
Date: 2023-06-24 00:00:00

Monolithic 3D integration has become a promising solution for future computing needs. The metal inter-layer via (MIV) forms interconnects between substrate layers in Monolithic 3D integration. Despite small size of MIV, the area overhead can become a major limitation for efficient M3D integration and, thus needs to be addressed. Previous works focused on the utilization of the substrate area around MIV to reduce this area overhead significantly but suffers from increased leakage and scaling factors. In this work, we discuss MIV-transistor realization that addresses both leakage and scaling issue along with similar area overhead reduction compared with previous works and, thus can be utilized efficiently. Our simulation results suggest that the leakage current $(I_{D,leak})$ has reduced by $14K\times$ and, the maximum current $(I_{D,max})$ increased by $58\%$ for the proposed MIV-transistor compared with the previous implementation. In addition, performance metrics of the inverter realization with our proposed MIV-transistor specifically the delay, slew time and power consumption reduced by $11.6\%$, $17.9\%$ and, $4.5\%$ respectively compared with the previous implementation with same MIV area overhead reduction.