pwc-archive/papers-with-abstracts huggingface dataset:
A computational approach to visual ecology with deep reinforcement learning
Authors: Sacha Sokoloski, Jure Majnik, Philipp Berens
Date: 2024-02-07 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Animal vision is thought to optimize various objectives from metabolic efficiency to discrimination performance, yet its ultimate objective is to facilitate the survival of the animal within its ecological niche. However, modeling animal behavior in complex environments has been challenging. To study how environments shape and constrain visual processing, we developed a deep reinforcement learning framework in which an agent moves through a 3-d environment that it perceives through a vision model, where its only goal is to survive. Within this framework we developed a foraging task where the agent must gather food that sustains it, and avoid food that harms it. We first established that the complexity of the vision model required for survival on this task scaled with the variety and visual complexity of the food in the environment. Moreover, we showed that a recurrent network architecture was necessary to fully exploit complex vision models on the most visually demanding tasks. Finally, we showed how different network architectures learned distinct representations of the environment and task, and lead the agent to exhibit distinct behavioural strategies. In summary, this paper lays the foundation for a computational approach to visual ecology, provides extensive benchmarks for future work, and demonstrates how representations and behaviour emerge from an agent's drive for survival.
Low-degree phase transitions for detecting a planted clique in sublinear time
Authors: Jay Mardia, Kabir Aladin Verchand, Alexander S. Wein
Date: 2024-02-08 00:00:00

We consider the problem of detecting a planted clique of size $k$ in a random graph on $n$ vertices. When the size of the clique exceeds $\Theta(\sqrt{n})$, polynomial-time algorithms for detection proliferate. We study faster -- namely, sublinear time -- algorithms in the high-signal regime when $k = \Theta(n^{1/2 + \delta})$, for some $\delta > 0$. To this end, we consider algorithms that non-adaptively query a subset $M$ of entries of the adjacency matrix and then compute a low-degree polynomial function of the revealed entries. We prove a computational phase transition for this class of non-adaptive low-degree algorithms: under the scaling $\lvert M \rvert = \Theta(n^{\gamma})$, the clique can be detected when $\gamma > 3(1/2 - \delta)$ but not when $\gamma < 3(1/2 - \delta)$. As a result, the best known runtime for detecting a planted clique, $\widetilde{O}(n^{3(1/2-\delta)})$, cannot be improved without looking beyond the non-adaptive low-degree class. Our proof of the lower bound -- based on bounding the conditional low-degree likelihood ratio -- reveals further structure in non-adaptive detection of a planted clique. Using (a bound on) the conditional low-degree likelihood ratio as a potential function, we show that for every non-adaptive query pattern, there is a highly structured query pattern of the same size that is at least as effective.
Transparent and Scrutable Recommendations Using Natural Language User Profiles
Authors: Jerome Ramos, Hossen A. Rahmani, Xi Wang, Xiao Fu, Aldo Lipani
Date: 2024-02-08 00:00:00
Tasks: Benchmarking, Descriptive, Recommendation Systems

Recent state-of-the-art recommender systems predominantly rely on either implicit or explicit feedback from users to suggest new items. While effective in recommending novel options, many recommender systems often use uninterpretable embeddings to represent user preferences. This lack of transparency not only limits user understanding of why certain items are suggested but also reduces the user's ability to scrutinize and modify their preferences, thereby affecting their ability to receive a list of preferred recommendations. Given the recent advances in Large Language Models (LLMs), we investigate how a properly crafted prompt can be used to summarize a user's preferences from past reviews and recommend items based only on language-based preferences. In particular, we study how LLMs can be prompted to generate a natural language (NL) user profile that holistically describe a user's preferences. These NL profiles can then be leveraged to fine-tune a LLM using only NL profiles to make transparent and scrutable recommendations. Furthermore, we validate the scrutability of our user profile-based recommender by investigating the impact on recommendation changes after editing NL user profiles. According to our evaluations of the model's rating prediction performance on two benchmarking rating prediction datasets, we observe that this novel approach maintains a performance level on par with established recommender systems in a warm-start setting. With a systematic analysis into the effect of updating user profiles and system prompts, we show the advantage of our approach in easier adjustment of user preferences and a greater autonomy over users' received recommendations.
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Authors: Jun Wang, Haoxuan Li, Chi Zhang, Dongxu Liang, Enyun Yu, Wenwu Ou, Wenjia Wang
Date: 2024-02-08 00:00:00
Tasks: Contrastive Learning, counterfactual, Imputation, Recommendation Systems, Selection bias

Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction. However, the observed feedback usually suffer from two issues: selection bias and data sparsity, where biased and insufficient feedback seriously degrade the performance of recommender systems in terms of accuracy and ranking. Existing solutions for handling the issues, such as data imputation and inverse propensity score, are highly susceptible to additional trained imputation or propensity models. In this work, we propose a novel counterfactual contrastive learning framework for recommendation, named CounterCLR, to tackle the problem of non-random missing data by exploiting the advances in contrast learning. Specifically, the proposed CounterCLR employs a deep representation network, called CauNet, to infer non-random missing data in recommendations and perform user preference modeling by further introducing a self-supervised contrastive learning task. Our CounterCLR mitigates the selection bias problem without the need for additional models or estimators, while also enhancing the generalization ability in cases of sparse data. Experiments on real-world datasets demonstrate the effectiveness and superiority of our method.
Enhancing textual textbook question answering with large language models and retrieval augmented generation
Authors: Hessa Abdulrahman Alawwad, Areej Alhothali, Usman Naseem, Ali Alkhathlan, Amani Jamal
Date: 2024-02-05 00:00:00
Tasks: Multiple-choice, Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Transfer Learning

Textbook question answering (TQA) is a challenging task in artificial intelligence due to the complex nature of context needed to answer complex questions. Although previous research has improved the task, there are still some limitations in textual TQA, including weak reasoning and inability to capture contextual information in the lengthy context. We propose a framework (PLRTQA) that incorporates the retrieval augmented generation (RAG) technique to handle the out-of-domain scenario where concepts are spread across different lessons, and utilize transfer learning to handle the long context and enhance reasoning abilities. Our architecture outperforms the baseline, achieving an accuracy improvement of 4. 12% in the validation set and 9. 84% in the test set for textual multiple-choice questions. While this paper focuses on solving challenges in the textual TQA, It provides a foundation for future work in multimodal TQA where the visual components are integrated to address more complex educational scenarios. Code: https://github.com/hessaAlawwad/PLR-TQA
Batch-Mix Negative Sampling for Learning Recommendation Retrievers
Authors: Yongfu Fan, Jin Chen, Yongquan Jiang, Defu Lian, Fangda Guo, Kai Zheng
Date: 2023-10-21 00:00:00
Proceeding: CIKM 2023 10
Tasks: Collaborative Filtering, Selection bias

Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss. Under real scenarios, the number of items becomes considerably large, making it exceedingly difficult to calculate the partition function with the whole item corpus. Negative sampling, which samples a subset from the item corpus, is widely used to accelerate the model training. Among different samplers, the in-batch sampling is commonly adopted for online recommendation retrievers, which regards the other items within the mini-batch as the negative samples for the given query, owing to its time and memory efficiency. However, the sample selection bias occurs due to the skewed feedback, harming the retrieval quality. In this paper, we propose a negative sampling approach named Batch-Mix Negative Sampling (BMNS), which adopts batch mixing operation to generate additional negatives for model training. Concretely, BMNS first generates new negative items with the sampled mix coefficient from the Beta distribution, after which a tailored correct strategy guided by frequency is designed to match the sampled softmax loss. In this way, the effort of re-encoding items out of the mini-batch is reduced while also improving the representation space of the negative set. The empirical experiments on four real-world datasets demonstrate BMNS is superior to the competitive negative inbatch sampling method.
Surrogate Modeling and Control of Medical Digital Twins
Authors: Luis L. Fonseca, Lucas Böttcher, Borna Mehrad, Reinhard C. Laubenbacher
Date: 2024-02-08 00:00:00

The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for the optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.
The ECM and tissue architecture are major determinants of early invasion mediated by E-cadherin dysfunction
Authors: Soraia Melo, Pilar Guerrero, Maurício Moreira Soares, José Rafael Bordin, Fátima Carneiro, Patrícia Carneiro, Maria Beatriz Dias, João Carvalho, Joana Figueiredo, Raquel Seruca, Rui D. M. Travasso
Date: 2023-08-11 00:00:00
Proceeding: Communications Biology 2023 8

Germline mutations of E-cadherin cause Hereditary Diffuse Gastric Cancer (HDGC), a highly invasive cancer syndrome characterised by the occurrence of diffuse-type gastric carcinoma and lobular breast cancer. In this disease, E-cadherin-defective cells are detected invading the adjacent stroma since very early stages. Although E-cadherin loss is well established as a triggering event, other determinants of the invasive process persist largely unknown. Herein, we develop an experimental strategy that comprises in vitro extrusion assays using E-cadherin mutants associated to HDGC, as well as mathematical models epitomising epithelial dynamics and its interaction with the extracellular matrix (ECM). In vitro, we verify that E-cadherin dysfunctional cells detach from the epithelial monolayer and extrude basally into the ECM. Through phase-field modelling we demonstrate that, aside from loss of cell-cell adhesion, increased ECM attachment further raises basal extrusion efficiency. Importantly, by combining phase-field and vertex model simulations, we show that the cylindrical structure of gastric glands strongly promotes the cell’s invasive ability. Moreover, we validate our findings using a dissipative particle dynamics simulation of epithelial extrusion. Overall, we provide the first evidence that cancer cell invasion is the outcome of defective cell-cell linkages, abnormal interplay with the ECM, and a favourable 3D tissue structure.
Is the price right? Reconceptualizing price and income elasticity to anticipate price perception issues
Authors: Shawn Berry
Date: 2024-02-07 00:00:00

Price perception by consumers represents a challenge to the ability of a business to correctly and profitably price and sell their products or services in a given market and any new target market. Complicating the perception of prices is the dynamics of price and income elasticity, both of which are key for estimating demand. This article proposes a novel and elegant identity that conceptualizes elasticity as a means of quantifying the potential for price perception problems in a market. Elasticity studies from 1990 to 2023 (n=30) were sampled to evaluate the relationship between price and income elasticity for various consumer commodities using the identity. The results suggest that, given known price and income elasticity values, a business can anticipate pricing perception problems in advance and address the potential for damaging distortion of their value proposition. Further, the business can use this insight to correctly choose a strategic pricing approach.
Distribution of the search of evolutionary product unit neural networks for classification
Authors: A. J. Tallón-Ballesteros, P. A. Gutiérrez-Peña, C. Hervás-Martínez
Date: 2012-05-15 00:00:00
Tasks: Evolutionary Algorithms

This paper deals with the distributed processing in the search for an optimum classification model using evolutionary product unit neural networks. For this distributed search we used a cluster of computers. Our objective is to obtain a more efficient design than those net architectures which do not use a distributed process and which thus result in simpler designs. In order to get the best classification models we use evolutionary algorithms to train and design neural networks, which require a very time consuming computation. The reasons behind the need for this distribution are various. It is complicated to train this type of nets because of the difficulty entailed in determining their architecture due to the complex error surface. On the other hand, the use of evolutionary algorithms involves running a great number of tests with different seeds and parameters, thus resulting in a high computational cost
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Authors: Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein
Date: 2022-08-19 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Image Restoration, Variational Inference

Standard diffusion models involve an image transform -- adding Gaussian noise -- and an image restoration operator that inverts this degradation. We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice. Even when using completely deterministic degradations (e.g., blur, masking, and more), the training and test-time update rules that underlie diffusion models can be easily generalized to create generative models. The success of these fully deterministic models calls into question the community's understanding of diffusion models, which relies on noise in either gradient Langevin dynamics or variational inference, and paves the way for generalized diffusion models that invert arbitrary processes. Our code is available at https://github.com/arpitbansal297/Cold-Diffusion-Models
PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
Authors: Tyler Rose, Nicolo Monti, Navvye Anand, Tianyu Shen
Date: 2024-02-08 00:00:00
Proceeding: bioRxiv 2024 2
Tasks: Drug Discovery, Prediction, Protein-Ligand Affinity Prediction, Transfer Learning

Understanding protein-ligand binding affinity is crucial for drug discovery, enabling the identification of promising drug candidates efficiently. We introduce PLAPT, a novel model leveraging transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPT's superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.
Quantifying Similarity: Text-Mining Approaches to Evaluate ChatGPT and Google Bard Content in Relation to BioMedical Literature
Authors: Jakub Klimczak, Ahmed Abdeen Hamed
Date: 2024-01-19 00:00:00
Tasks: Prompt Engineering

Background: The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown powerful capabilities in generating content. To date, the assessment of the usefulness of such content, generated by what is known as prompt engineering, has become an interesting research question. Objectives Using the mean of prompt engineering, we assess the similarity and closeness of such contents to real literature produced by scientists. Methods In this exploratory analysis, (1) we prompt-engineer ChatGPT and Google Bard to generate clinical content to be compared with literature counterparts, (2) we assess the similarities of the contents generated by comparing them with counterparts from biomedical literature. Our approach is to use text-mining approaches to compare documents and associated bigrams and to use network analysis to assess the terms' centrality. Results The experiments demonstrated that ChatGPT outperformed Google Bard in cosine document similarity (38% to 34%), Jaccard document similarity (23% to 19%), TF-IDF bigram similarity (47% to 41%), and term network centrality (degree and closeness). We also found new links that emerged in ChatGPT bigram networks that did not exist in literature bigram networks. Conclusions: The obtained similarity results show that ChatGPT outperformed Google Bard in document similarity, bigrams, and degree and closeness centrality. We also observed that ChatGPT offers linkage to terms that are connected in the literature. Such connections could inspire asking interesting questions and generate new hypotheses.
History of generative Artificial Intelligence (AI) chatbots: past, present, and future development
Authors: Md. Al-Amin, Mohammad Shazed Ali, Abdus Salam, Arif Khan, Ashraf Ali, Ahsan Ullah, Md Nur Alam, Shamsul Kabir Chowdhury
Date: 2024-02-04 00:00:00
Tasks: Chatbot

This research provides an in-depth comprehensive review of the progress of chatbot technology over time, from the initial basic systems relying on rules to today's advanced conversational bots powered by artificial intelligence. Spanning many decades, the paper explores the major milestones, innovations, and paradigm shifts that have driven the evolution of chatbots. Looking back at the very basic statistical model in 1906 via the early chatbots, such as ELIZA and ALICE in the 1960s and 1970s, the study traces key innovations leading to today's advanced conversational agents, such as ChatGPT and Google Bard. The study synthesizes insights from academic literature and industry sources to highlight crucial milestones, including the introduction of Turing tests, influential projects such as CALO, and recent transformer-based models. Tracing the path forward, the paper highlights how natural language processing and machine learning have been integrated into modern chatbots for more sophisticated capabilities. This chronological survey of the chatbot landscape provides a holistic reference to understand the technological and historical factors propelling conversational AI. By synthesizing learnings from this historical analysis, the research offers important context about the developmental trajectory of chatbots and their immense future potential across various field of application which could be the potential take ways for the respective research community and stakeholders.
TexShape: Information Theoretic Sentence Embedding for Language Models
Authors: Kaan Kale, Homa Esfahanizadeh, Noel Elias, Oguzhan Baser, Muriel Medard, Sriram Vishwanath
Date: 2024-02-05 00:00:00
Tasks: Data Compression, Fairness, Language Modeling, Language Modelling, Sentence, Sentence Embedding, Sentence-Embedding

With the exponential growth in data volume and the emergence of data-intensive applications, particularly in the field of machine learning, concerns related to resource utilization, privacy, and fairness have become paramount. This paper focuses on the textual domain of data and addresses challenges regarding encoding sentences to their optimized representations through the lens of information-theory. In particular, we use empirical estimates of mutual information, using the Donsker-Varadhan definition of Kullback-Leibler divergence. Our approach leverages this estimation to train an information-theoretic sentence embedding, called TexShape, for (task-based) data compression or for filtering out sensitive information, enhancing privacy and fairness. In this study, we employ a benchmark language model for initial text representation, complemented by neural networks for information-theoretic compression and mutual information estimations. Our experiments demonstrate significant advancements in preserving maximal targeted information and minimal sensitive information over adverse compression ratios, in terms of predictive accuracy of downstream models that are trained using the compressed data.
Consensus-driven Deviated Pursuit for Guaranteed Simultaneous Interception of Moving Targets
Authors: Abhinav Sinha, Dwaipayan Mukherjee, Shashi Ranjan Kumar
Date: 2024-02-08 00:00:00

This work proposes a cooperative strategy that employs deviated pursuit guidance to simultaneously intercept a moving (but not manoeuvring) target. As opposed to many existing cooperative guidance strategies which use estimates of time-to-go, based on proportional-navigation guidance, the proposed strategy uses an exact expression for time-to-go to ensure simultaneous interception. The guidance design considers nonlinear engagement kinematics, allowing the proposed strategy to remain effective over a large operating regime. Unlike existing strategies on simultaneous interception that achieve interception at the average value of their initial time-to-go estimates, this work provides flexibility in the choice of impact time. By judiciously choosing the edge weights of the communication network, a weighted consensus in time-to-go can be achieved. It has been shown that by allowing an edge weight to be negative, consensus in time-to-go can even be achieved for an impact time that lies outside the convex hull of the set of initial time-to-go values of the individual interceptors. The bounds on such negative weights have been analysed for some special graphs, using Nyquist criterion. Simulations are provided to vindicate the efficacy of the proposed strategy.