pwc-archive/papers-with-abstracts huggingface dataset:
Solving QUBO on the Loihi 2 Neuromorphic Processor
Authors: Alessandro Pierro, Philipp Stratmann, Gabriel Andres Fonseca Guerra, Sumedh Risbud, Timothy Shea, Ashish Rao Mangalore, Andreas Wild
Date: 2024-08-06 00:00:00
Tasks: CPU, Edge-computing

In this article, we describe an algorithm for solving Quadratic Unconstrained Binary Optimization problems on the Intel Loihi 2 neuromorphic processor. The solver is based on a hardware-aware fine-grained parallel simulated annealing algorithm developed for Intel's neuromorphic research chip Loihi 2. Preliminary results show that our approach can generate feasible solutions in as little as 1 ms and up to 37x more energy efficient compared to two baseline solvers running on a CPU. These advantages could be especially relevant for size-, weight-, and power-constrained edge computing applications.
Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW
Authors: Elia Cereda, Alessandro Giusti, Daniele Palossi
Date: 2024-08-06 00:00:00
Tasks: Pose Estimation, Self-Supervised Learning

Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Their small form factor (i.e., ~10cm diameter) ensures vast applicability, ranging from the exploration of narrow disaster scenarios to safe human-robot interaction. Simple electronics make these CPSes inexpensive, but strongly limit the computational, memory, and sensing resources available on board. In real-world applications, these limitations are further exacerbated by domain shift. This fundamental machine learning problem implies that model perception performance drops when moving from the training domain to a different deployment one. To cope with and mitigate this general problem, we present a novel on-device fine-tuning approach that relies only on the limited ultra-low power resources available aboard nano-drones. Then, to overcome the lack of ground-truth training labels aboard our CPS, we also employ a self-supervised method based on ego-motion consistency. Albeit our work builds on top of a specific real-world vision-based human pose estimation task, it is widely applicable for many embedded TinyML use cases. Our 512-image on-device training procedure is fully deployed aboard an ultra-low power GWT GAP9 System-on-Chip and requires only 1MB of memory while consuming as low as 19mW or running in just 510ms (at 38mW). Finally, we demonstrate the benefits of our on-device learning approach by field-testing our closed-loop CPS, showing a reduction in horizontal position error of up to 26% vs. a non-fine-tuned state-of-the-art baseline. In the most challenging never-seen-before environment, our on-device learning procedure makes the difference between succeeding or failing the mission.
Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors
Authors: Kunkun Hao, Yonggang Luo, Wen Cui, Yuqiao Bai, Jucheng Yang, Songyang Yan, Yuxi Pan, Zijiang Yang
Date: 2024-08-06 00:00:00
Tasks: Autonomous Vehicles, Imitation Learning

Evaluating the decision-making system is indispensable in developing autonomous vehicles, while realistic and challenging safety-critical test scenarios play a crucial role. Obtaining these scenarios is non-trivial, thanks to the long-tailed distribution, sparsity, and rarity in real-world data sets. To tackle this problem, in this paper, we introduce a natural adversarial scenario generation solution using naturalistic human driving priors and reinforcement learning techniques. By doing this, we can obtain large-scale test scenarios that are both diverse and realistic. Specifically, we build a simulation environment that mimics natural traffic interaction scenarios. Informed by this environment, we implement a two-stage procedure. The first stage incorporates conventional rule-based models, e.g., IDM~(Intelligent Driver Model) and MOBIL~(Minimizing Overall Braking Induced by Lane changes) model, to coarsely and discretely capture and calibrate key control parameters from the real-world dataset. Next, we leverage GAIL~(Generative Adversarial Imitation Learning) to represent driver behaviors continuously. The derived GAIL can be further used to design a PPO~(Proximal Policy Optimization)-based actor-critic network framework to fine-tune the reward function, and then optimizes our natural adversarial scenario generation solution. Extensive experiments have been conducted in the NGSIM dataset including the trajectory of 3,000 vehicles. Essential traffic parameters were measured in comparison with the baseline model, e.g., the collision rate, accelerations, steering, and the number of lane changes. Our findings demonstrate that the proposed model can generate realistic safety-critical test scenarios covering both naturalness and adversariality, which can be a cornerstone for the development of autonomous vehicles.
Static IR Drop Prediction with Attention U-Net and Saliency-Based Explainability
Date: 2024-08-06 00:00:00
Tasks: Image-to-Image Translation

There has been significant recent progress to reduce the computational effort of static IR drop analysis using neural networks, and modeling as an image-to-image translation task. A crucial issue is the lack of sufficient data from real industry designs to train these networks. Additionally, there is no methodology to explain a high-drop pixel in a predicted IR drop image to its specific root-causes. In this work, we first propose a U-Net neural network model with attention gates which is specifically tailored to achieve fast and accurate image-based static IR drop prediction. Attention gates allow selective emphasis on relevant parts of the input data without supervision which is desired because of the often sparse nature of the IR drop map. We propose a two-phase training process which utilizes a mix of artificially-generated data and a limited number of points from real designs. The results are, on-average, 18% (53%) better in MAE and 14% (113%) in F1 score compared to the winner of the ICCAD 2023 contest (and U-Net only) when tested on real designs. Second, we propose a fast method using saliency maps which can explain a predicted IR drop in terms of specific input pixels contributing the most to a drop. In our experiments, we show the number of high IR drop pixels can be reduced on-average by 18% by mimicking upsize of a tiny portion of PDN's resistive edges.
Enhanced Human Activity Recognition Using Inertial Sensor Data from Smart Wearables: A Neural Network Approach with Residual Connections
Date: 2024-05-23 00:00:00

Human Activity Recognition (HAR) has gained significant interest in various research circles due to its wide-ranging applications, including patient monitoring, gaming, and education. While computer vision has traditionally played a key role in HAR, it encounters challenges like privacy concerns and environmental factors such as occlusion. To address these issues, inertial sensors like accelerometers and gyroscopic sensors have gained popularity, offering advantages such as cost-effectiveness and increased mobility. In this study, we propose a Convolutional Neural Network (CNN) architecture for HAR that incorporates residual connections. We rigorously evaluate our model using a freely available dataset WISDM (2011) from the wireless sensor data mining lab and compare its performance with state-of-the-art techniques. Our results indicate that our proposed model not only outperforms existing methods in terms of accuracy but also exhibits reduced complexity, requiring only 38,342 trainable parameters. Our HAR model achieves an impressive average accuracy of 98.32%, along with notable F1-score, Recall, and Precision values of 97.50%. These findings underscore the efficacy of our approach in advancing HAR technology while maintaining efficiency.
LoLi-IEA: low-light image enhancement algorithm
Date: 2023-10-04 00:00:00
Tasks: Image Enhancement, Low-light Image Deblurring and Enhancement, Low-Light Image Enhancement

Low-light image enhancement has posed a significant challenge in recent years due to non-uniform luminance
in real-world images. Color restoration, luminance mapping, and estimation of the curve and levels are some
techniques used by the algorithms to enhance the image. However, real-world images often have non-uniform
luminance, requiring local enhancement in certain areas rather than global enhancement. In order to tackle this
issue, this paper introduces a novel methodology based on deep learning, employing two convolutional network
architectures. The first one classifies the brightness level of the input image, while the second one enhances
the brightness level based on information obtained through the first architecture. To train this model, two
commonly used datasets in state-of-the-art research are used: the LOL (Low-Light) and Synthetic Low-light.
Both datasets contain low-light and ground-truth image pairs, which makes it possible to make a proper estimate
between non-uniform and uniform luminosity. The proposed algorithm is applied over resized images from the
UHD-LOL4k dataset, with a performance evaluation through the metrics: Peak Signal-to-Noise Ratio (PSNR),
Structural Similarity Index (SSIM), Image Quality Evaluator (NIQE), and Blind /Unreferenced Image Spatial
Quality Evaluator (BRISQUE). According to the results, the proposed method outperforms algorithms with more
complex architectures in the literature. The double convolutional architecture emphasizes local enhancement in
real-world scenes and global enhancement in images with very low luminosity. Overall, this paper presents a
significant contribution to low-light image enhancement offering an effective solution to the challenges posed by
non-uniform luminance in real-world images
RRAM-Based Bio-Inspired Circuits for Mobile Epileptic Correlation Extraction and Seizure Prediction
Authors: Hao Wang, Lingfeng Zhang, Erjia Xiao, Xin Wang, Zhongrui Wang, Renjing Xu
Date: 2024-07-29 00:00:00
Tasks: EEG, Prediction, Seizure prediction

Non-invasive mobile electroencephalography (EEG) acquisition systems have been utilized for long-term monitoring of seizures, yet they suffer from limited battery life. Resistive random access memory (RRAM) is widely used in computing-in-memory(CIM) systems, which offers an ideal platform for reducing the computational energy consumption of seizure prediction algorithms, potentially solving the endurance issues of mobile EEG systems. To address this challenge, inspired by neuronal mechanisms, we propose a RRAM-based bio-inspired circuit system for correlation feature extraction and seizure prediction. This system achieves a high average sensitivity of 91.2% and a low false positive rate per hour (FPR/h) of 0.11 on the CHB-MIT seizure dataset. The chip under simulation demonstrates an area of approximately 0.83 mm2 and a latency of 62.2 {\mu}s. Power consumption is recorded at 24.4 mW during the feature extraction phase and 19.01 mW in the seizure prediction phase, with a cumulative energy consumption of 1.515 {\mu}J for a 3-second window data processing, predicting 29.2 minutes ahead. This method exhibits an 81.3% reduction in computational energy relative to the most efficient existing seizure prediction approaches, establishing a new benchmark for energy efficiency.
COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs
Authors: Xinhe Li, Zhuoying Feng, Yezeng Chen, Weichen Dai, Zixu He, Yi Zhou, Shuhong Jiao
Date: 2024-07-24 00:00:00
Tasks: feature selection

To reduce the experimental validation workload for chemical researchers and accelerate the design and optimization of high-energy-density lithium metal batteries, we aim to leverage models to automatically predict Coulombic Efficiency (CE) based on the composition of liquid electrolytes. There are mainly two representative paradigms in existing methods: machine learning and deep learning. However, the former requires intelligent input feature selection and reliable computational methods, leading to error propagation from feature estimation to model prediction, while the latter (e.g. MultiModal-MoLFormer) faces challenges of poor predictive performance and overfitting due to limited diversity in augmented data. To tackle these issues, we propose a novel method COEFF (COlumbic EFficiency prediction via Fine-tuned models), which consists of two stages: pre-training a chemical general model and fine-tuning on downstream domain data. Firstly, we adopt the publicly available MoLFormer model to obtain feature vectors for each solvent and salt in the electrolyte. Then, we perform a weighted average of embeddings for each token across all molecules, with weights determined by the respective electrolyte component ratios. Finally, we input the obtained electrolyte features into a Multi-layer Perceptron or Kolmogorov-Arnold Network to predict CE. Experimental results on a real-world dataset demonstrate that our method achieves SOTA for predicting CE compared to all baselines. Data and code used in this work will be made publicly available after the paper is published.
Parsimonious Universal Function Approximator for Elastic and Elasto-Plastic Cavity Expansion Problems
Authors: Xiao-Xuan Chen, Pin Zhang, Hai-Sui Yu, Zhen-Yu Yin, Brian Sheil
Date: 2024-07-08 00:00:00

Cavity expansion is a canonical problem in geotechnics, which can be described by partial differential equations (PDEs) and ordinary differential equations (ODEs). This study explores the potential of using a new solver, a physics-informed neural network (PINN), to calculate the stress field in an expanded cavity in the elastic and elasto-plastic regimes. Whilst PINNs have emerged as an effective universal function approximator for deriving the solutions of a wide range of governing PDEs/ODEs, their ability to solve elasto-plastic problems remains uncertain. A novel parsimonious loss function is first proposed to balance the simplicity and accuracy of PINN. The proposed method is applied to diverse material behaviours in the cavity expansion problem including isotropic, anisotropic elastic media, and elastic-perfectly plastic media with Tresca and Mohr-Coulomb yield criteria. The results indicate that the use of a parsimonious prior information-based loss function is highly beneficial to deriving the approximate solutions of complex PDEs with high accuracy. The present method allows for accurate derivation of solutions for both elastic and plastic mechanical responses of an expanded cavity. It also provides insights into how PINNs can be further advanced to solve more complex problems in geotechnical practice.
Enhancing Attack Resilience in Real-Time Systems through Variable Control Task Sampling Rates
Authors: Arkaprava Sain, Sunandan Adhikary, Ipsita Koley, Soumyajit Dey
Date: 2024-08-01 00:00:00
Tasks: Scheduling, valid

Cyber-physical systems (CPSs) in modern real-time applications integrate numerous control units linked through communication networks, each responsible for executing a mix of real-time safety-critical and non-critical tasks. To ensure predictable timing behaviour, most safety-critical tasks are scheduled with fixed sampling periods, which supports rigorous safety and performance analyses. However, this deterministic execution can be exploited by attackers to launch inference-based attacks on safety-critical tasks. This paper addresses the challenge of preventing such timing inference or schedule-based attacks by dynamically adjusting the execution rates of safety-critical tasks while maintaining their performance. We propose a novel schedule vulnerability analysis methodology, enabling runtime switching between valid schedules for various control task sampling rates. Leveraging this approach, we present the Multi-Rate Attack-Aware Randomized Scheduling (MAARS) framework for preemptive fixed-priority schedulers, designed to reduce the success rate of timing inference attacks on real-time systems. To our knowledge, this is the first method that combines attack-aware schedule randomization with preserved control and scheduling integrity. The framework's efficacy in attack prevention is evaluated on automotive benchmarks using a Hardware-in-the-Loop (HiL) setup.
Low-Latency Privacy-Preserving Deep Learning Design via Secure MPC
Authors: Ke Lin, Yasir Glani, Ping Luo
Date: 2024-07-24 00:00:00
Tasks: Deep Learning, Privacy Preserving, Privacy Preserving Deep Learning

Secure multi-party computation (MPC) facilitates privacy-preserving computation between multiple parties without leaking private information. While most secure deep learning techniques utilize MPC operations to achieve feasible privacy-preserving machine learning on downstream tasks, the overhead of the computation and communication still hampers their practical application. This work proposes a low-latency secret-sharing-based MPC design that reduces unnecessary communication rounds during the execution of MPC protocols. We also present a method for improving the computation of commonly used nonlinear functions in deep learning by integrating multivariate multiplication and coalescing different packets into one to maximize network utilization. Our experimental results indicate that our method is effective in a variety of settings, with a speedup in communication latency of $10\sim20\%$.
Prompt Injection Attacks on Large Language Models in Oncology
Authors: Jan Clusmann, Dyke Ferber, Isabella C. Wiest, Carolin V. Schneider, Titus J. Brinker, Sebastian Foersch, Daniel Truhn, Jakob N. Kather
Date: 2024-07-23 00:00:00

Vision-language artificial intelligence models (VLMs) possess medical knowledge and can be employed in healthcare in numerous ways, including as image interpreters, virtual scribes, and general decision support systems. However, here, we demonstrate that current VLMs applied to medical tasks exhibit a fundamental security flaw: they can be attacked by prompt injection attacks, which can be used to output harmful information just by interacting with the VLM, without any access to its parameters. We performed a quantitative study to evaluate the vulnerabilities to these attacks in four state of the art VLMs which have been proposed to be of utility in healthcare: Claude 3 Opus, Claude 3.5 Sonnet, Reka Core, and GPT-4o. Using a set of N=297 attacks, we show that all of these models are susceptible. Specifically, we show that embedding sub-visual prompts in medical imaging data can cause the model to provide harmful output, and that these prompts are non-obvious to human observers. Thus, our study demonstrates a key vulnerability in medical VLMs which should be mitigated before widespread clinical adoption.
Assessing AI Utility: The Random Guesser Test for Sequential Decision-Making Systems
Authors: Shun Ide, Allison Blunt, Djallel Bouneffouf
Date: 2024-07-25 00:00:00
Tasks: Decision Making, Recommendation Systems, Sequential Decision Making

We propose a general approach to quantitatively assessing the risk and vulnerability of artificial intelligence (AI) systems to biased decisions. The guiding principle of the proposed approach is that any AI algorithm must outperform a random guesser. This may appear trivial, but empirical results from a simplistic sequential decision-making scenario involving roulette games show that sophisticated AI-based approaches often underperform the random guesser by a significant margin. We highlight that modern recommender systems may exhibit a similar tendency to favor overly low-risk options. We argue that this "random guesser test" can serve as a useful tool for evaluating the utility of AI actions, and also points towards increasing exploration as a potential improvement to such systems.
BadRobot: Jailbreaking Embodied LLMs in the Physical World
Authors: Hangtao Zhang, Chenyu Zhu, Xianlong Wang, Ziqi Zhou, Changgan Yin, Minghui Li, Lulu Xue, Yichen Wang, Shengshan Hu, Aishan Liu, Peijin Guo, Leo Yu Zhang
Date: 2024-07-16 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Task Planning

Embodied AI represents systems where AI is integrated into physical entities. Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue remains overlooked: could these embodied LLMs perpetrate harmful behaviors? In response, we introduce BadRobot, a novel attack paradigm aiming to make embodied LLMs violate safety and ethical constraints through typical voice-based user-system interactions. Specifically, three vulnerabilities are exploited to achieve this type of attack: (i) manipulation of LLMs within robotic systems, (ii) misalignment between linguistic outputs and physical actions, and (iii) unintentional hazardous behaviors caused by world knowledge's flaws. Furthermore, we construct a benchmark of various malicious physical action queries to evaluate BadRobot's attack performance. Based on this benchmark, extensive experiments against existing prominent embodied LLM frameworks (e.g., Voxposer, Code as Policies, and ProgPrompt) demonstrate the effectiveness of our BadRobot.
NudgeRank: Digital Algorithmic Nudging for Personalized Health
Authors: Jodi Chiam, Aloysius Lim, Ankur Teredesai
Date: 2024-07-16 00:00:00
Tasks: Recommendation Systems

In this paper we describe NudgeRank, an innovative digital algorithmic nudging system designed to foster positive health behaviors on a population-wide scale. Utilizing a novel combination of Graph Neural Networks augmented with an extensible Knowledge Graph, this Recommender System is operational in production, delivering personalized and context-aware nudges to over 1.1 million care recipients daily. This enterprise deployment marks one of the largest AI-driven health behavior change initiatives, accommodating diverse health conditions and wearable devices. Rigorous evaluation reveals statistically significant improvements in health outcomes, including a 6.17% increase in daily steps and 7.61% more exercise minutes. Moreover, user engagement and program enrollment surged, with a 13.1% open rate compared to baseline systems' 4%. Demonstrating scalability and reliability, NudgeRank operates efficiently on commodity compute resources while maintaining automation and observability standards essential for production systems.
Artificial Intelligence from Idea to Implementation. How Can AI Reshape the Education Landscape?
Authors: Catalin Vrabie
Date: 2024-07-14 00:00:00

This introductory chapter provides an overview of the evolution and impact of Artificial Intelligence technologies in today society. Beginning with a historical context while exploring a few general definitions of AI, the author provides a timeline of the used technologies, highlighting its periods of stagnation, commonly referred to as AI winters, and the subsequent resurgence fueled by relentless enthusiasm and investment. The narrative then transitions to focus on the transformative effects of AI on society at large, with a particular emphasis on educational applications. Through examples, the paper shows how AI technologies have moved from theoretical constructs to practical tools that are reshaping pedagogical approaches and student engagement. The essay concludes by discussing the prospects of AI in education, emphasizing the need for a balanced approach that considers both technological advancements and societal implications.