pwc-archive/papers-with-abstracts huggingface dataset:
TELII: Temporal Event Level Inverted Indexing for Cohort Discovery on a Large Covid-19 EHR Dataset
Authors: Yan Huang
Date: 2024-10-22 00:00:00

Cohort discovery is a crucial step in clinical research on Electronic Health Record (EHR) data. Temporal queries, which are common in cohort discovery, can be time-consuming and prone to errors when processed on large EHR datasets. In this work, we introduce TELII, a temporal event level inverted indexing method designed for cohort discovery on large EHR datasets. TELII is engineered to pre-compute and store the relations along with the time difference between events, thereby providing fast and accurate temporal query capabilities. We implemented TELII for the OPTUM de-identified COVID-19 EHR dataset, which contains data from 8.87 million patients. We demonstrate four common temporal query tasks and their implementation using TELII with a MongoDB backend. Our results show that the temporal query speed for TELII is up to 2000 times faster than that of existing non-temporal inverted indexes. TELII achieves millisecond-level response times, enabling users to quickly explore event relations and find preliminary evidence for their research questions. Not only is TELII practical and straightforward to implement, but it also offers easy adaptability to other EHR datasets. These advantages underscore TELII's potential to serve as the query engine for EHR-based applications, ensuring fast, accurate, and user-friendly query responses.
Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities
Authors: Jesús Bobadilla, Abraham Gutiérrez, Santiago Alonso, Ángel González-Prieto
Date: 2024-10-22 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. The extra information (prediction reliabilities) can be used in a variety of relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences. Additionally, recommendation reliabilities can be gracefully provided to users: "probably you will like this film", "almost certainly you will like this song", etc. This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines. Remarkably, individual rating predictions are improved by using the proposed architecture compared to baselines. Experiments have been performed making use of four popular public datasets, showing generalizable quality results. Overall, the proposed architecture improves individual rating predictions quality, maintains recommendation results and opens the doors to a set of relevant collaborative filtering fields.
Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?
Authors: Gustavo Penha, Ali Vardasbi, Enrico Palumbo, Marco De Nadai, Hugues Bouchard
Date: 2024-10-22 00:00:00
Tasks: Collaborative Filtering, Information Retrieval, Re-Ranking, Response Generation, Retrieval

Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches. Instead, generative models directly associate inputs with item IDs. Given the breakthroughs of Large Language Models (LLMs), these generative systems can play a crucial role in centralizing a variety of Information Retrieval (IR) tasks in a single model that performs tasks such as query understanding, retrieval, recommendation, explanation, re-ranking, and response generation. Despite the growing interest in such a unified generative approach for IR systems, the advantages of using a single, multi-task model over multiple specialized models are not well established in the literature. This paper investigates whether and when such a unified approach can outperform task-specific models in the IR tasks of search and recommendation, broadly co-existing in multiple industrial online platforms, such as Spotify, YouTube, and Netflix. Previous work shows that (1) the latent representations of items learned by generative recommenders are biased towards popularity, and (2) content-based and collaborative-filtering-based information can improve an item's representations. Motivated by this, our study is guided by two hypotheses: [H1] the joint training regularizes the estimation of each item's popularity, and [H2] the joint training regularizes the item's latent representations, where search captures content-based aspects of an item and recommendation captures collaborative-filtering aspects. Our extensive experiments with both simulated and real-world data support both [H1] and [H2] as key contributors to the effectiveness improvements observed in the unified search and recommendation generative models over the single-task approaches.
Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation
Authors: Chang Meng, Chenhao Zhai, Xueliang Wang, Shuchang Liu, Xiaoqiang Feng, Lantao Hu, Xiu Li, Han Li, Kun Gai
Date: 2024-10-22 00:00:00
Tasks: Marketing

With the rise of short video platforms, video recommendation technology faces more complex challenges. Currently, there are multiple non-personalized modules in the video recommendation pipeline that urgently need personalized modeling techniques for improvement. Inspired by the success of uplift modeling in online marketing, we attempt to implement uplift modeling in the video recommendation scenario. However, we face two main challenges: 1) Design and utilization of treatments, and 2) Capture of user real-time interest. To address them, we design adjusting the distribution of videos with varying durations as the treatment and propose Coarse-to-fine Dynamic Uplift Modeling (CDUM) for real-time video recommendation. CDUM consists of two modules, CPM and FIC. The former module fully utilizes the offline features of users to model their long-term preferences, while the latter module leverages online real-time contextual features and request-level candidates to model users' real-time interests. These two modules work together to dynamically identify and targeting specific user groups and applying treatments effectively. Further, we conduct comprehensive experiments on the offline public and industrial datasets and online A/B test, demonstrating the superiority and effectiveness of our proposed CDUM. Our proposed CDUM is eventually fully deployed on the Kuaishou platform, serving hundreds of millions of users every day. The source code will be provided after the paper is accepted.
Cutting Through the Confusion and Hype: Understanding the True Potential of Generative AI
Authors: Ante Prodan, Jo-An Occhipinti, Rehez Ahlip, Goran Ujdur, Harris A. Eyre, Kyle Goosen, Luke Penza, Mark Heffernan
Date: 2024-10-22 00:00:00

This paper explores the nuanced landscape of generative AI (genAI), particularly focusing on neural network-based models like Large Language Models (LLMs). While genAI garners both optimistic enthusiasm and sceptical criticism, this work seeks to provide a balanced examination of its capabilities, limitations, and the profound impact it may have on societal functions and personal interactions. The first section demystifies language-based genAI through detailed discussions on how LLMs learn, their computational needs, distinguishing features from supporting technologies, and the inherent limitations in their accuracy and reliability. Real-world examples illustrate the practical applications and implications of these technologies. The latter part of the paper adopts a systems perspective, evaluating how the integration of LLMs with existing technologies can enhance productivity and address emerging concerns. It highlights the need for significant investment to understand the implications of recent advancements, advocating for a well-informed dialogue to ethically and responsibly integrate genAI into diverse sectors. The paper concludes with prospective developments and recommendations, emphasizing a forward-looking approach to harnessing genAI`s potential while mitigating its risks.
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
Authors: Allen Nie, Yuhui Zhang, Atharva Amdekar, Chris Piech, Tatsunori Hashimoto, Tobias Gerstenberg
Date: 2023-10-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Language Modeling, Language Modelling

Human commonsense understanding of the physical and social world is organized around intuitive theories. These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why? A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments, such as the violation of norms and whether the harm is avoidable or inevitable. We collected a dataset of stories from 24 cognitive science papers and developed a system to annotate each story with the factors they investigated. Using this dataset, we test whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with those of human participants. On the aggregate level, alignment has improved with more recent LLMs. However, using statistical analyses, we find that LLMs weigh the different factors quite differently from human participants. These results show how curated, challenge datasets combined with insights from cognitive science can help us go beyond comparisons based merely on aggregate metrics: we uncover LLMs implicit tendencies and show to what extent these align with human intuitions.
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
Authors: Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma, Quoc-Huy Trinh, Koushik Biswas, Hongyi Pan, Ritika K. Jha, Gorkem Durak, Alexander Hann, Jonas Varkey, Hang Viet Dao, Long Van Dao, Binh Phuc Nguyen, Nikolaos Papachrysos, Brandon Rieders, Peter Thelin Schmidt, Enrik Geissler, Tyler Berzin, Pål Halvorsen, Michael A. Riegler, Thomas de Lange, Ulas Bagci
Date: 2024-08-19 00:00:00
Tasks: Federated Learning, Segmentation

Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparation, and the complex nature of the large intestine contribute to high polyp miss-rate. These missed polyps can develop into cancer later, underscoring the importance of improving the detection methods. To address this gap of lack of publicly available, multi-center large and diverse datasets for developing automatic methods for polyp detection and segmentation, we introduce PolypDB, a large scale publicly available dataset that contains 3934 still polyp images and their corresponding ground truth from real colonoscopy videos. PolypDB comprises images from five modalities: Blue Light Imaging (BLI), Flexible Imaging Color Enhancement (FICE), Linked Color Imaging (LCI), Narrow Band Imaging (NBI), and White Light Imaging (WLI) from three medical centers in Norway, Sweden, and Vietnam. We provide a benchmark on each modality and center, including federated learning settings using popular segmentation and detection benchmarks. PolypDB is public and can be downloaded at \url{https://osf.io/pr7ms/}. More information about the dataset, segmentation, detection, federated learning benchmark and train-test split can be found at \url{https://github.com/DebeshJha/PolypDB}.
High-Order Associative Learning Based on Memristive Circuits for Efficient Learning
Authors: Shengbo Wang, Xuemeng Li, Jialin Ding, Weihao Ma, Ying Wang, Luigi Occhipinti, Arokia Nathan, Shuo Gao
Date: 2024-10-22 00:00:00

Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative learning framework with a biologically realistic structure. By utilizing memristors as synaptic modules and their state information to bridge different orders of associative learning, our design effectively establishes associations between multiple stimuli and replicates the transient nature of high-order associative learning. In Pavlov's classical conditioning experiments, our design achieves a 230% improvement in learning efficiency compared to previous works, with memristor power consumption in the synaptic modules remaining below 11 {\mu}W. In large-scale image recognition tasks, we utilize a 20*20 memristor array to represent images, enabling the system to recognize and label test images with semantic information at 100% accuracy. This scalability across different tasks highlights the framework's potential for a wide range of applications, offering enhanced learning efficiency for current memristor-based neuromorphic systems.
The Neuromorphic Analog Electronic Nose
Authors: Shavika Rastogi, Nik Dennler, Michael Schmuker, André van Schaik
Date: 2024-10-22 00:00:00

Rapid detection of gas concentration is important in different domains like gas leakage monitoring, pollution control, and so on, for the prevention of health hazards. Out of different types of gas sensors, Metal oxide (MOx) sensors are extensively used in such applications because of their portability, low cost, and high sensitivity for specific gases. However, how to effectively sample the MOx data for the real-time detection of gas and its concentration level remains an open question. Here we introduce a simple analog front-end for one MOx sensor that encodes the gas concentration in the time difference between pulses of two separate pathways. This front-end design is inspired by the spiking output of a mammalian olfactory bulb. We show that for a gas pulse injected in a constant airflow, the time difference between pulses decreases with increasing gas concentration, similar to the spike time difference between the two principal output neurons in the olfactory bulb. The circuit design is further extended to a MOx sensor array and this sensor array front-end was tested in the same environment for gas identification and concentration estimation. Encoding of gas stimulus features in analog spikes at the sensor level itself may result in data and power-efficient real-time gas sensing systems in the future that can ultimately be used in uncontrolled and turbulent environments for longer periods without data explosion.
Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineering
Authors: Joris Postmus, Steven Abreu
Date: 2024-10-09 00:00:00
Tasks: In-Context Learning

Large language models have transformed AI, yet reliably controlling their outputs remains a challenge. This paper explores activation engineering, where outputs of pre-trained LLMs are controlled by manipulating their activations at inference time. Unlike traditional methods using a single steering vector, we introduce conceptors - mathematical constructs that represent sets of activation vectors as ellipsoidal regions. Conceptors act as soft projection matrices and offer more precise control over complex activation patterns. Our experiments demonstrate that conceptors outperform traditional methods across multiple steering tasks. We further use Boolean operations on conceptors for combined steering goals that empirically outperform additively combining steering vectors on a set of tasks. These results highlight conceptors as a promising tool for more effective steering of LLMs. Our code is available on github.com/jorispos/conceptorsteering.
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
Authors: Niki van Stein, Diederick Vermetten, Thomas Bäck
Date: 2024-10-07 00:00:00
Tasks: Code Generation, Code Search, Language Modelling, Large Language Model

Large Language Models (LLMs) have shown great potential in automatically generating and optimizing (meta)heuristics, making them valuable tools in heuristic optimization tasks. However, LLMs are generally inefficient when it comes to fine-tuning hyper-parameters of the generated algorithms, often requiring excessive queries that lead to high computational and financial costs. This paper presents a novel hybrid approach, LLaMEA-HPO, which integrates the open source LLaMEA (Large Language Model Evolutionary Algorithm) framework with a Hyper-Parameter Optimization (HPO) procedure in the loop. By offloading hyper-parameter tuning to an HPO procedure, the LLaMEA-HPO framework allows the LLM to focus on generating novel algorithmic structures, reducing the number of required LLM queries and improving the overall efficiency of the optimization process. We empirically validate the proposed hybrid framework on benchmark problems, including Online Bin Packing, Black-Box Optimization, and the Traveling Salesperson Problem. Our results demonstrate that LLaMEA-HPO achieves superior or comparable performance compared to existing LLM-driven frameworks while significantly reducing computational costs. This work highlights the importance of separating algorithmic innovation and structural code search from parameter tuning in LLM-driven code optimization and offers a scalable approach to improve the efficiency and effectiveness of LLM-based code generation.
Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
Authors: Anagha Nimbekar, Prabodh Katti, Chen Li, Bashir M. Al-Hashimi, Amit Acharyya, Bipin Rajendran
Date: 2024-10-07 00:00:00

Spiking Neural Networks (SNNs) have emerged as a promising approach to improve the energy efficiency of machine learning models, as they naturally implement event-driven computations while avoiding expensive multiplication operations. In this paper, we develop a hardware-software co-optimisation strategy to port software-trained deep neural networks (DNN) to reduced-precision spiking models demonstrating fast and accurate inference in a novel event-driven CMOS reconfigurable spiking inference accelerator. Experimental results show that a reduced-precision Resnet-18 and VGG-11 SNN models achieves classification accuracy within 1% of the baseline full-precision DNN model within 8 spike timesteps. We also demonstrate an FPGA prototype implementation of the spiking inference accelerator with a throughput of 38.4 giga operations per second (GOPS) consuming 1.54 Watts on PYNQ-Z2 FPGA. This corresponds to 0.6 GOPS per processing element and 2.25,GOPS/DSP slice, which is 2x and 4.5x higher utilisation efficiency respectively compared to the state-of-the-art. Our co-optimisation strategy can be employed to develop deep reduced precision SNN models and port them to resource-efficient event-driven hardware accelerators for edge applications.
Comparative Analysis of Deep Natural Networks and Large Language Models for Aspect-Based Sentiment Analysis
Authors: NIMRA MUGHAL, Ghulam Mujtaba, SARANG SHAIKH, AVEENASH KUMAR, AND SHER MUHAMMAD DAUDPOTA
Date: 2024-04-17 00:00:00
Proceeding: IEEE Access 2024 4
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Specificity

Sentiment analysis is essential for comprehending public opinion, particularly when considering e-commerce and the expansion of online businesses. Early approaches treated sentiment analysis as a document or sentence-level classification problem, lacking the ability to capture nuanced opinions about specific aspects. This limitation was addressed by the development of aspect-based sentiment analysis (ABSA), which links sentiment to specific aspects that are mentioned explicitly or implicitly in the review. ABSA is relatively a recent field of sentiment analysis and the existing models for ABSA face three main challenges, including domain-specificity, reliance on labeled data, and a lack of exploration into the potential of newer large language models (LLMs) such as GPT, PaLM, and T5. Leveraging a diverse set of datasets, including DOTSA, MAMS, and SemEval16, we evaluate the performance of prominent models such as ATAE-LSTM, flan-t5-large-absa, DeBERTa, PaLM, and GPT-3.5-Turbo. Our findings reveal nuanced strengths and weaknesses of these models across different domains, with DeBERTa emerging as consistently high-performing and PaLM demonstrating remarkable competitiveness for aspect term sentiment analysis (ATSA) tasks. In addition, the PaLM demonstrates competitive performance for all the domains that were used in the experiments including the restaurant, hotel, books, clothing, and laptop reviews. Notably, the analysis underscores the models’ domain sensitivity, shedding light on their varying efficacy for both ATSA and ACSA tasks. These insights contribute to a deeper understanding of model applicability and highlight potential areas for improvement in ABSA research and development.
Sulyman Rofiyah Ayomide
Authors: Sulyman Rofiyah Ayomide
Date: 2024-10-23 00:00:00
Proceeding: 10/23 2024 10
Tasks: Marketing

I'm Sulyman Rofiyah Ayomide by name also known professionally as [JOY CEE].Full Time Freelancer As A Professional Digital Marketing Strategist, SEO & Wiki's Expert, Perfect Talented Marketer, Shopify Store Builder, Specialize In Creating Google Knowledge Panel With An Extensive Experience
I was born into the family of MR & MRS Sulyman on the 14th of may in the year 2005, I was born and raised in Kaiama Kwara State Nigeria. The journey of my life inaugurate pleasantly. I started my education when i clocked 3years, I went to Love Foundation Day Care Nur/Primary School Kaiama, After i graduated from Love Foundation, I was relocated to Ilorin in the year 2016 to stay with my Grandma and i went to Fortunate Standard College Ilorin to further with my junior section. After my junior waec in FSC i further with my SSCE in GHS. During my senior class, i learnt chemist & nurse as an apprentice in year 2020 and did freedom in 2023.When i was in SSS3 i voyage back to Kaiama to sat for my WASSCE & NECO in Supreme Secondary School and i was graduated in the year of 2022.I sat for JAMB in the year 2023 and i scored 211 but i was unable to gain admission into kwara state university as a biochemistry student, Then i did diploma programme for 8 month in CAILS KWASU in year 2023/2024 and register for Direct Entry into Kwara State University Malete.

After some days of registering DE, I was introduce to an organization called 2 in 1 business opportunity also known as [Network Marketing & Freelancing Services], I was summon to the office on a very faithful day, on getting there i was asked to join the business and i concur, My Sponsor and Trainer has opened up the pathway to my new life, Since joining the organization, I had gained more knowledge & acquired skills as a freelancer who render services in Fiverr, KWork, Top Tal, Up Work, Guru.com and other Freelancing Platforms with a multitudinous of a scheme, comparable as marketing specialist, social media marketer, digital marketer and many more also specialized in digital marketing services such as Social Media Promotion SMM, Google Knowledge Panel GKP, Google SEO, Shopify Store Builder & substantially more.