pwc-archive/papers-with-abstracts huggingface dataset:
Memories in the Making: Predicting Video Memorability with Encoding Phase EEG
Authors: Lorin Sweeney, Graham Healy, Alan F. Smeaton
Date: 2023-08-16 00:00:00
Tasks: EEG

In a world of ephemeral moments, our brain diligently sieves through a cascade of experiences, like a skilled gold prospector searching for precious nuggets amidst the river's relentless flow. This study delves into the elusive "moment of memorability" -- a fleeting, yet vital instant where experiences are prioritised for consolidation in our memory. By transforming subjects' encoding phase electroencephalography (EEG) signals into the visual domain using scaleograms and leveraging deep learning techniques, we investigate the neural signatures that underpin this moment, with the aim of predicting subject-specific recognition of video. Our findings not only support the involvement of theta band (4-8Hz) oscillations over the right temporal lobe in the encoding of declarative memory, but also support the existence of a distinct moment of memorability, akin to the gold nuggets that define our personal river of experiences.
Current Methods for Drug Property Prediction in the Real World
Authors: Jacob Green, Cecilia Cabrera Diaz, Maximilian A. H. Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan D. Greenhalgh
Date: 2023-07-25 00:00:00
Tasks: Decision Making, Deep Learning, Drug Discovery, Gaussian Processes, Property Prediction, Uncertainty Quantification

Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials, and to find highly active compounds faster. Interest from the Machine Learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods; thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and therefore cost of applying these methods in the drug development decision-making cycle. We discover that the best method depends on the dataset, and that engineered features with classical ML methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes while ADMET datasets are sometimes better described by Trees or Deep Learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on, and sets the precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.
Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management
Authors: Paraskevi Nousi, Loukia Avramelou, Georgios Rodinos, Maria Tzelepi, Theodoros Manousis, Konstantinos Tsampazis, Kyriakos Stefanidis, Dimitris Spanos, Manos Kirtas, Pavlos Tosidis, Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas
Date: 2023-07-23 00:00:00
Tasks: Deep Learning, Management

Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations. Deep Learning (DL) methods have been consistently excelling at various tasks and automated financial trading is one of the most complex one of those. This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes. At the same time, taking into consideration sentiment information regarding the traded assets, we discuss and demonstrate their usefulness through corresponding research studies. Finally, we discuss commonly found problems in training such financial agents and equip the reader with the necessary knowledge to avoid these problems and apply the discussed methods in practice.
Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning
Authors: Michel Aractingi, Pierre-Alexandre Léziart, Thomas Flayols, Julien Perez, Tomi Silander, Philippe Souères
Date: 2023-08-02 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Quadruped robots require robust and general locomotion skills to exploit their mobility potential in complex and challenging environments. In this work, we present the first implementation of a robust end-to-end learning-based controller on the Solo12 quadruped. Our method is based on deep reinforcement learning of joint impedance references. The resulting control policies follow a commanded velocity reference while being efficient in its energy consumption, robust and easy to deploy. We detail the learning procedure and method for transfer on the real robot. In our experiments, we show that the Solo12 robot is a suitable open-source platform for research combining learning and control because of the easiness in transferring and deploying learned controllers.
Leveraging Side Information for Ligand Conformation Generation using Diffusion-Based Approaches
Authors: Jiamin Wu, He Cao, Yuan YAO
Date: 2023-08-02 00:00:00
Tasks: Denoising, Drug Discovery

Ligand molecule conformation generation is a critical challenge in drug discovery. Deep learning models have been developed to tackle this problem, particularly through the use of generative models in recent years. However, these models often generate conformations that lack meaningful structure and randomness due to the absence of essential side information. Examples of such side information include the chemical and geometric features of the target protein, ligand-target compound interactions, and ligand chemical properties. Without these constraints, the generated conformations may not be suitable for further selection and design of new drugs. To address this limitation, we propose a novel method for generating ligand conformations that leverage side information and incorporate flexible constraints into standard diffusion models. Drawing inspiration from the concept of message passing, we introduce ligand-target massage passing block, a mechanism that facilitates the exchange of information between target nodes and ligand nodes, thereby incorporating target node features. To capture non-covalent interactions, we introduce ligand-target compound inter and intra edges. To further improve the biological relevance of the generated conformations, we train energy models using scalar chemical features. These models guide the progress of the standard Denoising Diffusion Probabilistic Models, resulting in more biologically meaningful conformations. We evaluate the performance of SIDEGEN using the PDBBind-2020 dataset, comparing it against other methods. The results demonstrate improvements in both Aligned RMSD and Ligand RMSD evaluations. Specifically, our model outperforms GeoDiff (trained on PDBBind-2020) by 20% in terms of the median aligned RMSD metric.
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning
Authors: Nhat Khang Ngo, Truong Son Hy
Date: 2023-08-02 00:00:00
Tasks: Drug Discovery, Representation Learning

Without knowledge of specific pockets, generating ligands based on the global structure of a protein target plays a crucial role in drug discovery as it helps reduce the search space for potential drug-like candidates in the pipeline. However, contemporary methods require optimizing tailored networks for each protein, which is arduous and costly. To address this issue, we introduce TargetVAE, a target-aware variational auto-encoder that generates ligands with high binding affinities to arbitrary protein targets, guided by a novel multimodal deep neural network built based on graph Transformers as the prior for the generative model. This is the first effort to unify different representations of proteins (e.g., sequence of amino-acids, 3D structure) into a single model that we name as Protein Multimodal Network (PMN). Our multimodal architecture learns from the entire protein structures and is able to capture their sequential, topological and geometrical information. We showcase the superiority of our approach by conducting extensive experiments and evaluations, including the assessment of generative model quality, ligand generation for unseen targets, docking score computation, and binding affinity prediction. Empirical results demonstrate the promising performance of our proposed approach. Our software package is publicly available at https://github.com/HySonLab/Ligand_Generation
ecoBLE: A Low-Computation Energy Consumption Prediction Framework for Bluetooth Low Energy
Authors: Luisa Schuhmacher, Sofie Pollin, Hazem Sallouha
Date: 2023-08-02 00:00:00
Tasks: Prediction

Bluetooth Low Energy (BLE) is a de-facto technology for Internet of Things (IoT) applications, promising very low energy consumption. However, this low energy consumption accounts only for the radio part, and it overlooks the energy consumption of other hardware and software components. Monitoring and predicting the energy consumption of IoT nodes after deployment can substantially aid in ensuring low energy consumption, calculating the remaining battery lifetime, predicting needed energy for energy-harvesting nodes, and detecting anomalies. In this paper, we introduce a Long Short-Term Memory Projection (LSTMP)-based BLE energy consumption prediction framework together with a dataset for a healthcare application scenario where BLE is widely adopted. Unlike radio-focused theoretical energy models, our framework provides a comprehensive energy consumption prediction, considering all components of the IoT node, including the radio, sensor as well as microcontroller unit (MCU). Our measurement-based results show that the proposed framework predicts the energy consumption of different BLE nodes with a Mean Absolute Percentage Error (MAPE) of up to 12%, giving comparable accuracy to state-of-the-art energy consumption prediction with a five times smaller prediction model size.
Extension of Transformational Machine Learning: Classification Problems
Authors: Adnan Mahmud, Oghenejokpeme Orhobor, Ross D. King
Date: 2023-08-07 00:00:00
Tasks: Classification, Drug Discovery, Management, Meta-Learning

This study explores the application and performance of Transformational Machine Learning (TML) in drug discovery. TML, a meta learning algorithm, excels in exploiting common attributes across various domains, thus developing composite models that outperform conventional models. The drug discovery process, which is complex and time-consuming, can benefit greatly from the enhanced prediction accuracy, improved interpretability and greater generalizability provided by TML. We explore the efficacy of different machine learning classifiers, where no individual classifier exhibits distinct superiority, leading to the consideration of ensemble classifiers such as the Random Forest. Our findings show that TML outperforms base Machine Learning (ML) as the number of training datasets increases, due to its capacity to better approximate the correct hypothesis, overcome local optima, and expand the space of representable functions by combining separate classifiers capabilities. However, this superiority is relative to the resampling methods applied, with Near Miss demonstrating poorer performance due to noisy data, overlapping classes, and nonlinear class boundaries. Conversely, Random Over Sampling (ROS) provides a more robust performance given its resistance to noise and outliers, improved class overlap management, and suitability for nonlinear class boundaries.
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery
Authors: Jinyin Chen, Jie Ge, Shilian Zheng, Linhui Ye, Haibin Zheng, Weiguo Shen, Keqiang Yue, Xiaoniu Yang
Date: 2023-08-17 00:00:00
Tasks: Adversarial Attack

A wireless communications system usually consists of a transmitter which transmits the information and a receiver which recovers the original information from the received distorted signal. Deep learning (DL) has been used to improve the performance of the receiver in complicated channel environments and state-of-the-art (SOTA) performance has been achieved. However, its robustness has not been investigated. In order to evaluate the robustness of DL-based information recovery models under adversarial circumstances, we investigate adversarial attacks on the SOTA DL-based information recovery model, i.e., DeepReceiver. We formulate the problem as an optimization problem with power and peak-to-average power ratio (PAPR) constraints. We design different adversarial attack methods according to the adversary's knowledge of DeepReceiver's model and/or testing samples. Extensive experiments show that the DeepReceiver is vulnerable to the designed attack methods in all of the considered scenarios. Even in the scenario of both model and test sample restricted, the adversary can attack the DeepReceiver and increase its bit error rate (BER) above 10%. It can also be found that the DeepReceiver is vulnerable to adversarial perturbations even with very low power and limited PAPR. These results suggest that defense measures should be taken to enhance the robustness of DeepReceiver.
UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning
Authors: Peiyuan Si, Jun Zhao, Kwok-Yan Lam, Qing Yang
Date: 2023-08-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Communication

In this paper, we aim to explore the use of uplink semantic communications with the assistance of UAV in order to improve data collection effiicency for metaverse users in remote areas. To reduce the time for uplink data collection while balancing the trade-off between reconstruction quality and computational energy cost, we propose a hybrid action reinforcement learning (RL) framework to make decisions on semantic model scale, channel allocation, transmission power, and UAV trajectory. The variables are classified into discrete type and continuous type, which are optimized by two different RL agents to generate the combined action. Simulation results indicate that the proposed hybrid action reinforcement learning framework can effectively improve the efficiency of uplink semantic data collection under different parameter settings and outperforms the benchmark scenarios.
A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning
Authors: Hongyin Zhang, Shuyu Yang, Donglin Wang
Date: 2023-09-13 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Online reinforcement learning (RL) methods are often data-inefficient or unreliable, making them difficult to train on real robotic hardware, especially quadruped robots. Learning robotic tasks from pre-collected data is a promising direction. Meanwhile, agile and stable legged robotic locomotion remains an open question in their general form. Offline reinforcement learning (ORL) has the potential to make breakthroughs in this challenging field, but its current bottleneck lies in the lack of diverse datasets for challenging realistic tasks. To facilitate the development of ORL, we benchmarked 11 ORL algorithms in the realistic quadrupedal locomotion dataset. Such dataset is collected by the classic model predictive control (MPC) method, rather than the model-free online RL method commonly used by previous benchmarks. Extensive experimental results show that the best-performing ORL algorithms can achieve competitive performance compared with the model-free RL, and even surpass it in some tasks. However, there is still a gap between the learning-based methods and MPC, especially in terms of stability and rapid adaptation. Our proposed benchmark will serve as a development platform for testing and evaluating the performance of ORL algorithms in real-world legged locomotion tasks.
Physics-Informed Solution of The Stationary Fokker-Plank Equation for a Class of Nonlinear Dynamical Systems: An Evaluation Study
Authors: Hussam Alhussein, Mohammed Khasawneh, Mohammed F. Daqaq
Date: 2023-09-25 00:00:00
Tasks: Transfer Learning

The Fokker-Planck (FP) equation is a linear partial differential equation which governs the temporal and spatial evolution of the probability density function (PDF) associated with the response of stochastic dynamical systems. An exact analytical solution of the FP equation is only available for a limited subset of dynamical systems. Semi-analytical methods are available for larger, yet still a small subset of systems, while traditional computational methods; e.g. Finite Elements and Finite Difference require dividing the computational domain into a grid of discrete points, which incurs significant computational costs for high-dimensional systems. Physics-informed learning offers a potentially powerful alternative to traditional computational schemes. To evaluate its potential, we present a data-free, physics-informed neural network (PINN) framework to solve the FP equation for a class of nonlinear stochastic dynamical systems. In particular, through several examples concerning the stochastic response of the Duffing, Van der Pol, and the Duffing-Van der Pol oscillators, we assess the ability and accuracy of the PINN framework in $i)$ predicting the PDF under the combined effect of additive and multiplicative noise, $ii)$ capturing P-bifurcations of the PDF, and $iii)$ effectively treating high-dimensional systems. Through comparisons with Monte-Carlo simulations and the available literature, we show that PINN can effectively address all of the afore-described points. We also demonstrate that the computational time associated with the PINN solution can be substantially reduced by using transfer learning.
Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning
Authors: Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta
Date: 2023-08-02 00:00:00

A normative approach called Similarity Matching was recently introduced for deriving and understanding the algorithmic basis of neural computation focused on unsupervised problems. It involves deriving algorithms from computational objectives and evaluating their compatibility with anatomical and physiological observations. In particular, it introduces neural architectures by considering dual alternatives instead of primal formulations of popular models such as PCA. However, its connection to the Representer theorem remains unexplored. In this work, we propose to use teachings from this approach to explore supervised learning algorithms and clarify the notion of Hebbian learning. We examine regularized supervised learning and elucidate the emergence of neural architecture and additive versus multiplicative update rules. In this work, we focus not on developing new algorithms but on showing that the Representer theorem offers the perfect lens to study biologically plausible learning algorithms. We argue that many past and current advancements in the field rely on some form of dual formulation to introduce biological plausibility. In short, as long as a dual formulation exists, it is possible to derive biologically plausible algorithms. Our work sheds light on the pivotal role of the Representer theorem in advancing our comprehension of neural computation.
Inappropriate Benefits and Identification of ChatGPT Misuse in Programming Tests: A Controlled Experiment
Authors: Hapnes Toba, Oscar Karnalim, Meliana Christianti Johan, Terutoshi Tada, Yenni Merlin Djajalaksana, Tristan Vivaldy
Date: 2023-08-11 00:00:00
Tasks: Survey

While ChatGPT may help students to learn to program, it can be misused to do plagiarism, a breach of academic integrity. Students can ask ChatGPT to complete a programming task, generating a solution from other people's work without proper acknowledgment of the source(s). To help address this new kind of plagiarism, we performed a controlled experiment measuring the inappropriate benefits of using ChatGPT in terms of completion time and programming performance. We also reported how to manually identify programs aided with ChatGPT (via student behavior while using ChatGPT) and student perspective of ChatGPT (via a survey). Seventeen students participated in the experiment. They were asked to complete two programming tests. They were divided into two groups per the test: one group should complete the test without help while the other group should complete it with ChatGPT. Our study shows that students with ChatGPT complete programming tests two times faster than those without ChatGPT, though their programming performance is comparable. The generated code is highly efficient and uses complex data structures like lists and dictionaries. Based on the survey results, ChatGPT is recommended to be used as an assistant to complete programming tasks and other general assignments. ChatGPT will be beneficial as a reference as other search engines do. Logical and critical thinking are needed to validate the result presented by ChatGPT.
GPT-Lab: Next Generation Of Optimal Chemistry Discovery By GPT Driven Robotic Lab
Authors: Xiaokai Qin, Mingda Song, Yangguan Chen, Zhehong Ai, Jing Jiang
Date: 2023-09-15 00:00:00
Tasks: Articles, Experimental Design

The integration of robots in chemical experiments has enhanced experimental efficiency, but lacking the human intelligence to comprehend literature, they seldom provide assistance in experimental design. Therefore, achieving full-process autonomy from experiment design to validation in self-driven laboratories (SDL) remains a challenge. The introduction of Generative Pre-trained Transformers (GPT), particularly GPT-4, into robotic experimentation offers a solution. We introduce GPT-Lab, a paradigm that employs GPT models to give robots human-like intelligence. With our robotic experimentation platform, GPT-Lab mines literature for materials and methods and validates findings through high-throughput synthesis. As a demonstration, GPT-Lab analyzed 500 articles, identified 18 potential reagents, and successfully produced an accurate humidity colorimetric sensor with a root mean square error (RMSE) of 2.68%. This showcases the rapid materials discovery and validation potential of our system.
Alternate Learning based Sparse Semantic Communications for Visual Transmission
Authors: Siyu Tong, Xiaoxue Yu, Rongpeng Li, Kun Lu, Zhifeng Zhao, Honggang Zhang
Date: 2023-07-31 00:00:00
Tasks: Quantization, Semantic Communication

Semantic communication (SemCom) demonstrates strong superiority over conventional bit-level accurate transmission, by only attempting to recover the essential semantic information of data. In this paper, in order to tackle the non-differentiability of channels, we propose an alternate learning based SemCom system for visual transmission, named SparseSBC. Specially, SparseSBC leverages two separate Deep Neural Network (DNN)-based models at the transmitter and receiver, respectively, and learns the encoding and decoding in an alternate manner, rather than the joint optimization in existing literature, so as to solving the non-differentiability in the channel. In particular, a ``self-critic" training scheme is leveraged for stable training. Moreover, the DNN-based transmitter generates a sparse set of bits in deduced ``semantic bases", by further incorporating a binary quantization module on the basis of minimal detrimental effect to the semantic accuracy. Extensive simulation results validate that SparseSBC shows efficient and effective transmission performance under various channel conditions, and outperforms typical SemCom solutions.