pwc-archive/papers-with-abstracts huggingface dataset:
Utilizing ChatGPT to Enhance Clinical Trial Enrollment
Authors: Georgios Peikos, Symeon Symeonidis, Pranav Kasela, Gabriella Pasi
Date: 2023-06-03 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Retrieval

Clinical trials are a critical component of evaluating the effectiveness of new medical interventions and driving advancements in medical research. Therefore, timely enrollment of patients is crucial to prevent delays or premature termination of trials. In this context, Electronic Health Records (EHRs) have emerged as a valuable tool for identifying and enrolling eligible participants. In this study, we propose an automated approach that leverages ChatGPT, a large language model, to extract patient-related information from unstructured clinical notes and generate search queries for retrieving potentially eligible clinical trials. Our empirical evaluation, conducted on two benchmark retrieval collections, shows improved retrieval performance compared to existing approaches when several general-purposed and task-specific prompts are used. Notably, ChatGPT-generated queries also outperform human-generated queries in terms of retrieval performance. These findings highlight the potential use of ChatGPT to enhance clinical trial enrollment while ensuring the quality of medical service and minimizing direct risks to patients.
Fast Interactive Search with a Scale-Free Comparison Oracle
Authors: Daniyar Chumbalov, Lars Klein, Lucas Maystre, Matthias Grossglauser
Date: 2023-06-02 00:00:00
Tasks: Navigate, Triplet

A comparison-based search algorithm lets a user find a target item $t$ in a database by answering queries of the form, ``Which of items $i$ and $j$ is closer to $t$?'' Instead of formulating an explicit query (such as one or several keywords), the user navigates towards the target via a sequence of such (typically noisy) queries. We propose a scale-free probabilistic oracle model called $\gamma$-CKL for such similarity triplets $(i,j;t)$, which generalizes the CKL triplet model proposed in the literature. The generalization affords independent control over the discriminating power of the oracle and the dimension of the feature space containing the items. We develop a search algorithm with provably exponential rate of convergence under the $\gamma$-CKL oracle, thanks to a backtracking strategy that deals with the unavoidable errors in updating the belief region around the target. We evaluate the performance of the algorithm both over the posited oracle and over several real-world triplet datasets. We also report on a comprehensive user study, where human subjects navigate a database of face portraits.
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions
Authors: Boxiang Lyu, Zhe Feng, Zachary Robertson, Sanmi Koyejo
Date: 2023-06-01 00:00:00
Tasks: Learning-To-Rank

We study the design of loss functions for click-through rates (CTR) to optimize (social) welfare in advertising auctions. Existing works either only focus on CTR predictions without consideration of business objectives (e.g., welfare) in auctions or assume that the distribution over the participants' expected cost-per-impression (eCPM) is known a priori, then use various additional assumptions on the parametric form of the distribution to derive loss functions for predicting CTRs. In this work, we bring back the welfare objectives of ad auctions into CTR predictions and propose a novel weighted rankloss to train the CTR model. Compared to existing literature, our approach provides a provable guarantee on welfare but without assumptions on the eCPMs' distribution while also avoiding the intractability of naively applying existing learning-to-rank methods. Further, we propose a theoretically justifiable technique for calibrating the losses using labels generated from a teacher network, only assuming that the teacher network has bounded $\ell_2$ generalization error. Finally, we demonstrate the advantages of the proposed loss on synthetic and real-world data.
Beyond Rankings: Exploring the Impact of SERP Features on Organic Click-through Rates
Authors: Erik Fubel, Niclas Michael Groll, Patrick Gundlach, Qiwei Han, Maximilian Kaiser
Date: 2023-05-31 00:00:00

Search Engine Result Pages (SERPs) serve as the digital gateways to the vast expanse of the internet. Past decades have witnessed a surge in research primarily centered on the influence of website ranking on these pages, to determine the click-through rate (CTR). However, during this period, the landscape of SERPs has undergone a dramatic evolution: SERP features, encompassing elements such as knowledge panels, media galleries, FAQs, and more, have emerged as an increasingly prominent facet of these result pages. Our study examines the crucial role of these features, revealing them to be not merely aesthetic components, but strongly influence CTR and the associated behavior of internet users. We demonstrate how these features can significantly modulate web traffic, either amplifying or attenuating it. We dissect these intricate interaction effects leveraging a unique dataset of 67,000 keywords and their respective Google SERPs, spanning over 40 distinct US-based e-commerce domains, generating over 6 million clicks from 24 million views. This cross-website dataset, unprecedented in its scope, enables us to assess the impact of 24 different SERP features on organic CTR. Through an ablation study modeling CTR, we illustrate the incremental predictive power these features hold.
The Effect of News Article Quality on Ad Consumption
Authors: Kojiro Iizuka, Yoshifumi Seki, Makoto P. Kato
Date: 2023-05-31 00:00:00
Tasks: Articles

Practical news feed platforms generate a hybrid list of news articles and advertising items (e.g., products, services, or information) and many platforms optimize the position of news articles and advertisements independently. However, they should be arranged with careful consideration of each other, as we show in this study, since user behaviors toward advertisements are significantly affected by the news articles. This paper investigates the effect of news articles on users' ad consumption and shows the dependency between news and ad effectiveness. We conducted a service log analysis and showed that sessions with high-quality news article exposure had more ad consumption than those with low-quality news article exposure. Based on this result, we hypothesized that exposure to high-quality articles will lead to a high ad consumption rate. Thus, we conducted million-scale A/B testing to investigate the effect of high-quality articles on ad consumption, in which we prioritized high-quality articles in the ranking for the treatment group. The A/B test showed that the treatment group's ad consumption, such as the number of clicks, conversions, and sales, increased significantly while the number of article clicks decreased. We also found that users who prefer a social or economic topic had more ad consumption by stratified analysis. These insights regarding news articles and advertisements will help optimize news and ad effectiveness in rankings considering their mutual influence.
MECTA: Memory-Economic Continual Test-Time Model Adaptation
Authors: Junyuan Hong, Lingjuan Lyu, Jiayu Zhou, Michael Spranger
Date: 2023-02-01 00:00:00
Proceeding: ICLR 2023 2
Tasks: model, Test-time Adaptation

Continual Test-time Adaptation (CTA) is a promising art to secure accuracy gains in continually-changing environments. The state-of-the-art adaptations improve out-of-distribution model accuracy via computation-efficient online test-time gradient descents but meanwhile cost about times of memory versus the inference, even if only a small portion of parameters are updated. Such high memory consumption of CTA substantially impedes wide applications of advanced CTA on memoryconstrained devices. In this paper, we provide a novel solution, dubbed MECTA, to drastically improve the memory efficiency of gradient-based CTA. Our profiling
shows that the major memory overhead comes from the intermediate cache for backpropagation, which scales by the batch size, channel, and layer number. Therefore, we propose to reduce batch sizes, adopt an adaptive normalization layer to maintain
stable and accurate predictions, and stop the back-propagation caching heuristically. On the other hand, we prune the networks to reduce the computation and memory overheads in optimization and recover the parameters afterward to avoid forgetting. The proposed MECTA is efficient and can be seamlessly plugged into state-of-theart CTA algorithms at negligible overhead on computation and memory. On three datasets, CIFAR10, CIFAR100, and ImageNet, MECTA improves the accuracy by at least 6% with constrained memory and significantly reduces the memory costs of ResNet50 on ImageNet by at least 70% with comparable accuracy. Our codes can be accessed at https://github.com/SonyAI/MECTA.
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
Authors: Shuyang Yu, Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou
Date: 2023-02-01 00:00:00
Proceeding: ICLR 2023 2
Tasks: Autonomous Driving, Federated Learning, Out-of-Distribution Detection

Deep neural networks have witnessed huge successes in many challenging prediction tasks and yet they often suffer from out-of-distribution (OoD) samples, misclassifying them with high confidence. Recent advances show promising OoD detection performance for centralized training, and however, OoD detection in federated learning (FL) is largely overlooked, even though many security sensitive applications such as autonomous driving and voice recognition authorization are commonly trained using FL for data privacy concerns. The main challenge that prevents previous state-of-the-art OoD detection methods from being incorporated to FL is that they require large amount of real OoD samples. However, in real-world scenarios, such large-scale OoD training data can be costly or even infeasible to obtain, especially for resource-limited local devices. On the other hand, a notorious challenge in FL is data heterogeneity where each client collects non-identically and independently distributed (non-iid) data. We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL. The key is that for each client, non-iid data from other clients (unseen external classes) can serve as an alternative to real OoD samples. Specifically, we propose a novel Federated Out-of-Distribution Synthesizer (FOSTER), which learns a class-conditional generator to synthesize virtual external-class OoD samples, and maintains data confidentiality and communication efficiency required by FL. Experimental results show that our method outperforms the state-of-the-art by 2.49%, 2.88%, 1.42% AUROC, and 0.01%, 0.89%, 1.74% ID accuracy, on CIFAR-10, CIFAR-100, and STL10, respectively.
Explaining AI in Finance: Past, Present, Prospects
Authors: Barry Quinn
Date: 2023-06-05 00:00:00
Tasks: Decision Making, Explainable Artificial Intelligence (XAI), regression

This paper explores the journey of AI in finance, with a particular focus on the crucial role and potential of Explainable AI (XAI). We trace AI's evolution from early statistical methods to sophisticated machine learning, highlighting XAI's role in popular financial applications. The paper underscores the superior interpretability of methods like Shapley values compared to traditional linear regression in complex financial scenarios. It emphasizes the necessity of further XAI research, given forthcoming EU regulations. The paper demonstrates, through simulations, that XAI enhances trust in AI systems, fostering more responsible decision-making within finance.
Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals
Authors: Jinhan Wang, Vijay Ravi, Abeer Alwan
Date: 2023-06-02 00:00:00
Tasks: Depression Detection, Disentanglement, Privacy Preserving, Speaker Identification

While speech-based depression detection methods that use speaker-identity features, such as speaker embeddings, are popular, they often compromise patient privacy. To address this issue, we propose a speaker disentanglement method that utilizes a non-uniform mechanism of adversarial SID loss maximization. This is achieved by varying the adversarial weight between different layers of a model during training. We find that a greater adversarial weight for the initial layers leads to performance improvement. Our approach using the ECAPA-TDNN model achieves an F1-score of 0.7349 (a 3.7% improvement over audio-only SOTA) on the DAIC-WoZ dataset, while simultaneously reducing the speaker-identification accuracy by 50%. Our findings suggest that identifying depression through speech signals can be accomplished without placing undue reliance on a speaker's identity, paving the way for privacy-preserving approaches of depression detection.
Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images
Authors: Eric Upschulte, Stefan Harmeling, Katrin Amunts, Timo Dickscheid
Date: 2022-11-30 00:00:00
Proceeding: NeurIPS CellSeg 2022 2022 11
Tasks: Cell Segmentation, Instance Segmentation, Object Detection, Segmentation

We present a simple framework for cell segmentation, based on uncertainty-aware Contour Proposal Networks (CPNs).
It is designed to provide high segmentation accuracy while remaining computationally efficient, which makes it an ideal solution for high throughput microscopy applications. Each predicted cell is provided with four uncertainty estimations that give information about the localization accuracy of the detected cell boundaries. Such additional insights are valuable for downstream single-cell analysis in microscopy image-based biology and biomedical research. In the context of the NeurIPS 22 Cell Segmentation Challenge, the proposed solution is shown to generalize well in a multi-modality setting, while respecting domain-specific requirements such as focusing on specific cell types. Without an ensemble or test-time augmentation the method achieves an F1 score of 0.8986 on the challenge's validation set. Code and trained models are available at celldetection.org
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
Authors: Le Zhang, Jiayang Chen, Tao Shen, Yu Li, Siqi Sun
Date: 2023-06-02 00:00:00
Tasks: Language Modeling, Language Modelling, Multiple Sequence Alignment, Protein Folding, Protein Structure Prediction

The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision. As co-evolution is integral to protein structure prediction, AF2's accuracy is significantly influenced by the depth of multiple sequence alignment (MSA), which requires extensive exploration of a large protein database for similar sequences. However, not all protein sequences possess abundant homologous families, and consequently, AF2's performance can degrade on such queries, at times failing to produce meaningful results. To address this, we introduce a novel generative language model, MSA-Augmenter, which leverages protein-specific attention mechanisms and large-scale MSAs to generate useful, novel protein sequences not currently found in databases. These sequences supplement shallow MSAs, enhancing the accuracy of structural property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter can generate de novo sequences that retain co-evolutionary information from inferior MSAs, thereby improving protein structure prediction quality on top of strong AF2.
Rebooting Internet Immunity
Authors: Gregory M. Dickinson
Date: 2023-06-05 00:00:00

We do everything online. We shop, travel, invest, socialize, and even hold garage sales. Even though we may not care whether a company operates online or in the physical world, however, the question has dramatic consequences for the companies themselves. Online and offline entities are governed by different rules. Under Section 230 of the Communications Decency Act, online entities -- but not physical-world entities -- are immune from lawsuits related to content authored by their users or customers. As a result, online entities have been able to avoid claims for harms caused by their negligence and defective product designs simply because they operate online. The reason for the disparate treatment is the internet's dramatic evolution over the last two decades. The internet of 1996 served as an information repository and communications channel and was well governed by Section 230, which treats internet entities as another form of mass media: Because Facebook, Twitter and other online companies could not possibly review the mass of content that flows through their systems, Section 230 immunizes them from claims related to user content. But content distribution is not the internet's only function, and it is even less so now than it was in 1996. The internet also operates as a platform for the delivery of real-world goods and services and requires a correspondingly diverse immunity doctrine. This Article proposes refining online immunity by limiting it to claims that threaten to impose a content-moderation burden on internet defendants. Where a claim is preventable other than by content moderation -- for example, by redesigning an app or website -- a plaintiff could freely seek relief, just as in the physical world. This approach empowers courts to identify culpable actors in the virtual world and treat like conduct alike wherever it occurs.
Toward Textual Internet Immunity
Authors: Gregory M. Dickinson
Date: 2023-06-05 00:00:00
Tasks: Misinformation

Internet immunity doctrine is broken. Under Section 230 of the Communications Decency Act of 1996, online entities are absolutely immune from lawsuits related to content authored by third parties. The law has been essential to the internet's development over the last twenty years, but it has not kept pace with the times and is now deeply flawed. Democrats demand accountability for online misinformation. Republicans decry politically motivated censorship. And Congress, President Biden, the Department of Justice, and the Federal Communications Commission all have their own plans for reform. Absent from the fray, however -- until now -- has been the Supreme Court, which has never issued a decision interpreting Section 230. That appears poised to change, however, following Justice Thomas's statement in Malwarebytes v. Enigma in which he urges the Court to prune back decades of lower-court precedent to craft a more limited immunity doctrine. This Essay discusses how courts' zealous enforcement of the early internet's free-information ethos gave birth to an expansive immunity doctrine, warns of potential pitfalls to reform, and explores what a narrower, text-focused doctrine might mean for the tech industry.
Big Tech's Tightening Grip on Internet Speech
Authors: Gregory M. Dickinson
Date: 2023-06-05 00:00:00

Online platforms have completely transformed American social life. They have democratized publication, overthrown old gatekeepers, and given ordinary Americans a fresh voice in politics. But the system is beginning to falter. Control over online speech lies in the hands of a select few -- Facebook, Google, and Twitter -- who moderate content for the entire nation. It is an impossible task. Americans cannot even agree among themselves what speech should be permitted. And, more importantly, platforms have their own interests at stake: Fringe theories and ugly name-calling drive away users. Moderation is good for business. But platform beautification has consequences for society's unpopular members, whose unsightly voices are silenced in the process. With control over online speech so centralized, online outcasts are left with few avenues for expression. Concentrated private control over important resources is an old problem. Last century, for example, saw the rise of railroads and telephone networks. To ensure access, such entities are treated as common carriers and required to provide equal service to all comers. Perhaps the same should be true for social media. This Essay responds to recent calls from Congress, the Supreme Court, and academia arguing that, like common carriers, online platforms should be required to carry all lawful content. The Essay studies users' and platforms' competing expressive interests, analyzes problematic trends in platforms' censorship practices, and explores the costs of common-carrier regulation before ultimately proposing market expansion and segmentation as an alternate pathway to avoid the economic and social costs of common-carrier regulation.
RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems
Authors: Krishna Ronanki, Beatriz Cabrero-Daniel, Jennifer Horkoff, Christian Berger
Date: 2023-05-29 00:00:00
Tasks: Ethics

Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU. However, practitioners lack actionable instructions to operationalise ethics during AI systems development. A literature review of different ethical guidelines revealed inconsistencies in the principles addressed and the terminology used to describe them. Furthermore, requirements engineering (RE), which is identified to foster trustworthiness in the AI development process from the early stages was observed to be absent in a lot of frameworks that support the development of ethical and trustworthy AI. This incongruous phrasing combined with a lack of concrete development practices makes trustworthy AI development harder. To address this concern, we formulated a comparison table for the terminology used and the coverage of the ethical AI principles in major ethical AI guidelines. We then examined the applicability of ethical AI development frameworks for performing effective RE during the development of trustworthy AI systems. A tertiary review and meta-analysis of literature discussing ethical AI frameworks revealed their limitations when developing trustworthy AI. Based on our findings, we propose recommendations to address such limitations during the development of trustworthy AI.
DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference
Authors: Ziyang Zhang, Yang Zhao, Huan Li, Changyao Lin, Jie Liu
Date: 2023-06-02 00:00:00
Tasks: Collaborative Inference, CPU, Deep Reinforcement Learning, GPU

Due to limited resources on edge and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy consumption and end-to-end latency on edge devices. In addition to the dynamic voltage frequency scaling (DVFS) technique, the edge-cloud architecture provides a collaborative approach for efficient DNN inference. However, current edge-cloud collaborative inference methods have not optimized various compute resources on edge devices. Thus, we propose DVFO, a novel DVFS-enabled edge-cloud collaborative inference framework, which co-optimizes DVFS and offloading parameters via deep reinforcement learning (DRL). Specifically, DVFO automatically co-optimizes 1) the CPU, GPU and memory frequencies of edge devices, and 2) the feature maps to be offloaded to cloud servers. In addition, it leverages a thinking-while-moving concurrent mechanism to accelerate the DRL learning process, and a spatial-channel attention mechanism to extract DNN feature maps of secondary importance for workload offloading. This approach improves inference performance for different DNN models under various edge-cloud network conditions. Extensive evaluations using two datasets and six widely-deployed DNN models on three heterogeneous edge devices show that DVFO significantly reduces the energy consumption by 33% on average, compared to state-of-the-art schemes. Moreover, DVFO achieves up to 28.6%-59.1% end-to-end latency reduction, while maintaining accuracy within 1% loss on average.