pwc-archive/papers-with-abstracts huggingface dataset:
Multi-View Empowered Structural Graph Wordification for Language Models
Authors: Zipeng Liu, Likang Wu, Ming He, Zhong Guan, Hongke Zhao, Nan Feng
Date: 2024-06-19 00:00:00
Tasks: Graph Neural Network, Node Classification

Significant efforts have been dedicated to integrating the powerful Large Language Models (LLMs) with diverse modalities, particularly focusing on the fusion of language, vision and audio data. However, the graph-structured data, which is inherently rich in structural and domain-specific knowledge, has not yet been gracefully adapted to LLMs. Existing methods either describe the graph with raw text, suffering the loss of graph structural information, or feed Graph Neural Network (GNN) embeddings into LLMs at the cost of losing explainable prompt semantics. To bridge this gap, we introduce an end-to-end modality-aligning framework for LLM-graph alignment: Dual-Residual Vector Quantized-Variational AutoEncoder, namely Dr.E. Our approach is purposefully designed to facilitate token-level alignment with LLMs, enabling an effective translation of the intrinsic `language' of graphs into comprehensible natural language. We also manage to enhance LLMs' more robust structural understanding of graphs by incorporating multiple views of the central nodes based on their surrounding nodes at various distances. Our experimental evaluations on standard graph tasks demonstrate competitive performance against other state-of-the-art (SOTA) approaches. Additionally, our framework ensures certain visual interpretability, efficiency, and robustness, marking the promising successful endeavor to achieve token-level alignment between LLMs and GNNs. Our code is available at: https://github.com/Timothy914/Dr.E.
Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation
Authors: Haochen Liu, Song Wang, Chen Chen, Jundong Li
Date: 2024-06-19 00:00:00
Tasks: Knowledge Graphs, Relational Reasoning

Few-shot Knowledge Graph (KG) Relational Reasoning aims to predict unseen triplets (i.e., query triplets) for rare relations in KGs, given only several triplets of these relations as references (i.e., support triplets). This task has gained significant traction due to the widespread use of knowledge graphs in various natural language processing applications. Previous approaches have utilized meta-training methods and manually constructed meta-relation sets to tackle this task. Recent efforts have focused on edge-mask-based methods, which exploit the structure of the contextualized graphs of target triplets (i.e., a subgraph containing relevant triplets in the KG). However, existing edge-mask-based methods have limitations in extracting insufficient information from KG and are highly influenced by spurious information in KG. To overcome these challenges, we propose SAFER (Subgraph Adaptation for Few-shot Relational Reasoning), a novel approach that effectively adapts the information in contextualized graphs to various subgraphs generated from support and query triplets to perform the prediction. Specifically, SAFER enables the extraction of more comprehensive information from support triplets while minimizing the impact of spurious information when predicting query triplets. Experimental results on three prevalent datasets demonstrate the superiority of our proposed framework SAFER.
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding
Authors: Haneul Yoo, Yongjin Yang, Hwaran Lee
Date: 2024-06-17 00:00:00
Tasks: 16k, Language Modelling, Red Teaming, Safety Alignment

As large language models (LLMs) have advanced rapidly, concerns regarding their safety have become prominent. In this paper, we discover that code-switching in red-teaming queries can effectively elicit undesirable behaviors of LLMs, which are common practices in natural language. We introduce a simple yet effective framework, CSRT, to synthesize code-switching red-teaming queries and investigate the safety and multilingual understanding of LLMs comprehensively. Through extensive experiments with ten state-of-the-art LLMs and code-switching queries combining up to 10 languages, we demonstrate that the CSRT significantly outperforms existing multilingual red-teaming techniques, achieving 46.7% more attacks than standard attacks in English and being effective in conventional safety domains. We also examine the multilingual ability of those LLMs to generate and understand code-switching texts. Additionally, we validate the extensibility of the CSRT by generating code-switching attack prompts with monolingual data. We finally conduct detailed ablation studies exploring code-switching and propound unintended correlation between resource availability of languages and safety alignment in existing multilingual LLMs.
An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction
Authors: Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
Date: 2024-04-29 00:00:00
Tasks: Anomaly Detection, Trajectory Prediction, Video Anomaly Detection

Video Anomaly Detection (VAD) represents a challenging and prominent research task within computer vision. In recent years, Pose-based Video Anomaly Detection (PAD) has drawn considerable attention from the research community due to several inherent advantages over pixel-based approaches despite the occasional suboptimal performance. Specifically, PAD is characterized by reduced computational complexity, intrinsic privacy preservation, and the mitigation of concerns related to discrimination and bias against specific demographic groups. This paper introduces TSGAD, a novel human-centric Two-Stream Graph-Improved Anomaly Detection leveraging Variational Autoencoders (VAEs) and trajectory prediction. TSGAD aims to explore the possibility of utilizing VAEs as a new approach for pose-based human-centric VAD alongside the benefits of trajectory prediction. We demonstrate TSGAD's effectiveness through comprehensive experimentation on benchmark datasets. TSGAD demonstrates comparable results with state-of-the-art methods showcasing the potential of adopting variational autoencoders. This suggests a promising direction for future research endeavors. The code base for this work is available at https://github.com/TeCSAR-UNCC/TSGAD.
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton
Authors: Jerry Chun-Wei Lin, Pi-Wei Chen, Chao-Chun Chen
Date: 2024-04-30 00:00:00
Tasks: Anomaly Detection, Decoder, Out of Distribution (OOD) Detection

Reconstruction networks are prevalently used in unsupervised anomaly and Out-of-Distribution (OOD) detection due to their independence from labeled anomaly data. However, in multi-class datasets, the effectiveness of anomaly detection is often compromised by the models' generalized reconstruction capabilities, which allow anomalies to blend within the expanded boundaries of normality resulting from the added categories, thereby reducing detection accuracy. We introduce the FUTUREG framework, which incorporates two innovative modules: the Feature Purification Module (FPM) and the CFG Decoder. The FPM constrains the normality boundary within the latent space to effectively filter out anomalous features, while the CFG Decoder uses layer-wise encoder representations to guide the reconstruction of filtered features, preserving fine-grained details. Together, these modules enhance the reconstruction error for anomalies, ensuring high-quality reconstructions for normal samples. Our results demonstrate that FUTUREG achieves state-of-the-art performance in multi-class OOD settings and remains competitive in industrial anomaly detection scenarios.
Mixing Paint: An analysis of color value transformations in multiple coordinate spaces using multivariate linear regression
Authors: Alexander Messick
Date: 2024-04-01 00:00:00

I explore the mathematical transformation that occurs in color coordinate space when physically mixing paints of two different colors. I tested 120 pairs of 16 paint colors and used a linear regression to find the most accurate combination of input parameters, both in RGB space and several other color spaces. I found that the fit with the strongest coefficient of determination was a geometrically symmetrized linear combination of the colors in CIEXYZ space, while this same mapping in RGB space returns a better mean squared error.
Model Callers for Transforming Predictive and Generative AI Applications
Authors: Mukesh Dalal
Date: 2024-04-17 00:00:00
Tasks: Management

We introduce a novel software abstraction termed "model caller," acting as an intermediary for AI and ML model calling, advocating its transformative utility beyond existing model-serving frameworks. This abstraction offers multiple advantages: enhanced accuracy and reduced latency in model predictions, superior monitoring and observability of models, more streamlined AI system architectures, simplified AI development and management processes, and improved collaboration and accountability across AI/ML/Data Science, software, data, and operations teams. Model callers are valuable for both creators and users of models within both predictive and generative AI applications. Additionally, we have developed and released a prototype Python library for model callers, accessible for installation via pip or for download from GitHub.
What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMs
Authors: Raeid Saqur
Date: 2024-06-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, World Knowledge

Machine learning techniques applied to the problem of financial market forecasting struggle with dynamic regime switching, or underlying correlation and covariance shifts in true (hidden) market variables. Drawing inspiration from the success of reinforcement learning in robotics, particularly in agile locomotion adaptation of quadruped robots to unseen terrains, we introduce an innovative approach that leverages world knowledge of pretrained LLMs (aka. 'privileged information' in robotics) and dynamically adapts them using intrinsic, natural market rewards using LLM alignment technique we dub as "Reinforcement Learning from Market Feedback" (**RLMF**). Strong empirical results demonstrate the efficacy of our method in adapting to regime shifts in financial markets, a challenge that has long plagued predictive models in this domain. The proposed algorithmic framework outperforms best-performing SOTA LLM models on the existing (FLARE) benchmark stock-movement (SM) tasks by more than 15\% improved accuracy. On the recently proposed NIFTY SM task, our adaptive policy outperforms the SOTA best performing trillion parameter models like GPT-4. The paper details the dual-phase, teacher-student architecture and implementation of our model, the empirical results obtained, and an analysis of the role of language embeddings in terms of Information Gain.
Machine Learning Visualization Tool for Exploring Parameterized Hydrodynamics
Authors: C. F. Jekel, D. M. Sterbentz, T. M. Stitt, P. Mocz, R. N. Rieben, D. A. White, J. L. Belof
Date: 2024-06-20 00:00:00
Tasks: Sensitivity

We are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding $\mathcal{O}\left({\rm TB}\right)$ of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize "what-if" situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.
Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots
Authors: Mehmet Sıddık Çadırcı, Musa Çadırcı
Date: 2024-06-20 00:00:00
Tasks: Property Prediction

Perovskite Quantum Dots (PQDs) have a promising future for several applications due to their unique properties. This study investigates the effectiveness of Machine Learning (ML) in predicting the size, absorbance (1S abs) and photoluminescence (PL) properties of $\mathrm{CsPbCl}_3$ PQDs using synthesizing features as the input dataset. the study employed ML models of Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT) and Deep Learning (DL). Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high $\mathrm{R}^2$ and low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. Given that ML is becoming more superior, its ability to understand the QDs field could prove invaluable to shape the future of nanomaterials designing.
Self Training and Ensembling Frequency Dependent Networks with Coarse Prediction Pooling and Sound Event Bounding Boxes
Authors: Hyeonuk Nam, Deokki Min, Seungdeok Choi, Inhan Choi, Yong-Hwa Park
Date: 2024-06-22 00:00:00
Tasks: Change Detection, Data Augmentation, Event Detection, Pseudo Label, Sound Event Detection

To tackle sound event detection (SED), we propose frequency dependent networks (FreDNets), which heavily leverage frequency-dependent methods. We apply frequency warping and FilterAugment, which are frequency-dependent data augmentation methods. The model architecture consists of 3 branches: audio teacher-student transformer (ATST) branch, BEATs branch and CNN branch including either partial dilated frequency dynamic convolution (PDFD conv) or squeeze-and-Excitation (SE) with time-frame frequency-wise SE (tfwSE). To train MAESTRO labels with coarse temporal resolution, we applied max pooling on prediction for the MAESTRO dataset. Using best ensemble model, we applied self training to obtain pseudo label from DESED weak set, unlabeled set and AudioSet. AudioSet pseudo labels, filtered to focus on high-confidence labels, are used to train on DESED dataset only. We used change-detection-based sound event bounding boxes (cSEBBs) as post processing for ensemble models on self training and submission models. The resulting FreDNet was ranked 2nd in DCASE 2024 Challenge Task 4.
On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation
Authors: Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein
Date: 2024-06-24 00:00:00

Reconstructing a signal on a graph from noisy observations of a subset of the vertices is a fundamental problem in the field of graph signal processing. This paper investigates how sample size affects reconstruction error in the presence of noise via an in-depth theoretical analysis of the two most common reconstruction methods in the literature, least-squares reconstruction (LS) and graph-Laplacian regularised reconstruction (GLR). Our theorems show that at sufficiently low signal-to-noise ratios (SNRs), under these reconstruction methods we may simultaneously decrease sample size and decrease average reconstruction error. We further show that at sufficiently low SNRs, for LS reconstruction we have a $\Lambda$-shaped error curve and for GLR reconstruction, a sample size of $ \mathcal{O}(\sqrt{N})$, where $N$ is the total number of vertices, results in lower reconstruction error than near full observation. We present thresholds on the SNRs, $\tau$ and $\tau_{GLR}$, below which the error is non-monotonic, and illustrate these theoretical results with experiments across multiple random graph models, sampling schemes and SNRs. These results demonstrate that any decision in sample-size choice has to be made in light of the noise levels in the data.
Fuel-Optimal Formation Reconfiguration by Means of Unidirectional Low-Thrust Propulsion System
Authors: Ahmed Mahfouz, Gabriella Gaias, Florio Dalla Vedova, Holger Voos
Date: 2024-06-24 00:00:00

The use of electric low-thrust propulsion systems for orbit maneuvers is becoming a popular choice among satellite manufacturers due to their inherent merits over their chemical counterparts. Many designers choose to incorporate multiple of such thrusters to insure omnidirectional orbit maneuverability, while others choose to equip their satellite with only one thruster nozzle, aiming to reduce the required power, weight, and size of the orbit control system. This paper proposes guidance and control schemes to address the problem of autonomous optimal relative orbit reconfiguration for a formation of two satellites, one of which utilizes a single low-thrust throttleable nozzle. Such under-actuated orbit control system requires the controlled spacecraft to constantly slew to direct the nozzle to the desired thrust direction. These redirection attitude maneuvers are treated within the guidance layer by accommodating recurrent no-thrust periods during which slew maneuvers take place. The control loop is then closed with an MPC-like scheme. The main motivation of this article is to support the future missions of LuxSpace's flagship satellite, Triton-X. Since the proposed guidance and control schemes are meant to answer realistic market needs, they are designed to have some specific qualities that makes them attractive from the practical point of view. Namely, they require minimal computational loads, besides being able to accommodate operational time constraints, e.g. no thrusting during eclipse or during ground contact, within the guidance layer.
Koopman Operator-based Detection-Isolation of Cyberattack: A Case Study on Electric Vehicle Charging
Authors: Sanchita Ghosh, Tanushree Roy
Date: 2024-06-24 00:00:00

One of the key challenges towards the reliable operation of cyber-physical systems (CPS) is the threat of cyberattacks on system actuation signals and measurements. In recent years, system theoretic research has focused on effectively detecting and isolating these cyberattacks to ensure proper restorative measures. Although both model-based and model-free approaches have been used in this context, the latter are increasingly becoming more popular as complexities and model uncertainties in CPS increases. Thus, in this paper we propose a Koopman operator-based model-free cyberattack detection-isolation scheme for CPS. The algorithm uses limited system measurements for its training and generates real-time detection-isolation flags. Furthermore, we present a simulation case study to detect and isolate actuation and sensor attacks in a Lithium-ion battery system of a plug-in electric vehicle during charging.
Generalized Modal Analysis in Power System with High CIG Penetration: Concept and Quantitative Assessment
Authors: Le Zheng, Jiajie Zheng, Chongru Liu
Date: 2024-06-24 00:00:00

This paper presents a Generalized Modal Analysis (GMA) concept for the small-signal stability analysis of power systems with high penetration of Converter-Interfaced Generation (CIG). GMA quantitatively assesses interactions between various elements in the power system, offering intuitive and transparent physical interpretations. The method's versatility in selecting physical quantities at different input and output ports makes it broadly applicable. Based on the concept of GMA, the study further defines interaction quantification indices by selecting voltage ports, examining the impact of grid disturbances on power sources and the support from the power sources to the grid at connection points. Numerical simulations on modified 14-bus and 68-bus systems validate GMA's effectiveness in capturing the coupling of the dynamic characteristics between grid elements. This research provides a theoretical foundation and analytical framework for future analyses of power system stability with diverse power sources.
Tuning a Cascaded Online Feedback Optimization Controller for Provision of Distributed Flexibility
Authors: Irina Zettl, Florian Klein-Helmkamp, Florian Schmidtke, Lukas Ortmann, Andreas Ulbig
Date: 2024-06-24 00:00:00

Coordinating a high number of flexibility providing units (e.g. to provide ancillary services for the transmission system) across various grid layers requires new control concepts. A flexibility request at a point of common coupling can be met by utilizing a cascaded control structure based on online feedback optimization. In this paper the influence of the parameterization of the individual controllers on the performance of the hierarchical flexibility provision is studied on a three-level test system. The results show a high interdependency between the choice of control parameters of one controller and the behavior of other controllers as well as a significant impact on the accuracy and speed of flexibility provision. With a careful tuning, a cascaded structure based on online feedback optimization can achieve efficient vertical coordination of flexibility providing units.
Recent advancements on MPC for tracking: periodic and harmonic formulations
Authors: Pablo Krupa, Daniel Limon, Teodoro Alamo
Date: 2024-06-24 00:00:00
Tasks: Model Predictive Control

The main benefit of model predictive control (MPC) is its ability to steer the system to a given reference without violating the constraints while minimizing some objective. Furthermore, a suitably designed MPC controller guarantees asymptotic stability of the closed-loop system to the given reference as long as its optimization problem is feasible at the initial state of the system. Therefore, one of the limitations of classical MPC is that changing the reference may lead to an unfeasible MPC problem. Furthermore, due to a lack of deep knowledge of the system, it is possible for the user to provide a desired reference that is unfeasible or non-attainable for the MPC controller, leading to the same problem. This chapter summarizes MPC formulations recently proposed that have been designed to address these issues. In particular, thanks to the addition of an artificial reference as decision variable, the formulations achieve asymptotic stability and recursive feasibility guarantees regardless of the reference provided by the user, even if it is changed online or if it violates the system constraints. We show a recent formulation which extends this idea, achieving better performance and larger domains of attraction when working with small prediction horizons. Additional benefits of these formulations, when compared to classical MPC, are also discussed and highlighted with illustrative examples.