pwc-archive/papers-with-abstracts huggingface dataset:
Turning Logic Against Itself : Probing Model Defenses Through Contrastive Questions
Authors: Rachneet Sachdeva, Rima Hazra, Iryna Gurevych
Date: 2025-01-03 00:00:00
Tasks: Language Modeling, Language Modelling

Large language models, despite extensive alignment with human values and ethical principles, remain vulnerable to sophisticated jailbreak attacks that exploit their reasoning abilities. Existing safety measures often detect overt malicious intent but fail to address subtle, reasoning-driven vulnerabilities. In this work, we introduce POATE (Polar Opposite query generation, Adversarial Template construction, and Elaboration), a novel jailbreak technique that harnesses contrastive reasoning to provoke unethical responses. POATE crafts semantically opposing intents and integrates them with adversarial templates, steering models toward harmful outputs with remarkable subtlety. We conduct extensive evaluation across six diverse language model families of varying parameter sizes to demonstrate the robustness of the attack, achieving significantly higher attack success rates (~44%) compared to existing methods. To counter this, we propose Intent-Aware CoT and Reverse Thinking CoT, which decompose queries to detect malicious intent and reason in reverse to evaluate and reject harmful responses. These methods enhance reasoning robustness and strengthen the model's defense against adversarial exploits.
Long Context vs. RAG for LLMs: An Evaluation and Revisits
Authors: Xinze Li, Yixin Cao, Yubo Ma, Aixin Sun
Date: 2024-12-27 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Extending context windows (i.e., Long Context, LC) and using retrievers to selectively access relevant information (i.e., Retrieval-Augmented Generation, RAG) are the two main strategies to enable LLMs to incorporate extremely long external contexts. This paper revisits recent studies on this topic, highlighting their key insights and discrepancies. We then provide a more comprehensive evaluation by filtering out questions answerable without external context, identifying the most effective retrieval methods, and expanding the datasets. We show that LC generally outperforms RAG in question-answering benchmarks, especially for Wikipedia-based questions. Summarization-based retrieval performs comparably to LC, while chunk-based retrieval lags behind. However, RAG has advantages in dialogue-based and general question queries. These insights underscore the trade-offs between RAG and LC strategies, offering guidance for future optimization of LLMs with external knowledge sources. We also provide an in-depth discussion on this topic, highlighting the overlooked importance of context relevance in existing studies.
Metadata Conditioning Accelerates Language Model Pre-training
Authors: Tianyu Gao, Alexander Wettig, Luxi He, Yihe Dong, Sadhika Malladi, Danqi Chen
Date: 2025-01-03 00:00:00
Tasks: Language Modeling, Language Modelling, model

The vast diversity of styles, domains, and quality levels present in language model pre-training corpora is essential in developing general model capabilities, but efficiently learning and deploying the correct behaviors exemplified in each of these heterogeneous data sources is challenging. To address this, we propose a new method, termed Metadata Conditioning then Cooldown (MeCo), to incorporate additional learning cues during pre-training. MeCo first provides metadata (e.g., URLs like en.wikipedia.org) alongside the text during training and later uses a cooldown phase with only the standard text, thereby enabling the model to function normally even without metadata. MeCo significantly accelerates pre-training across different model scales (600M to 8B parameters) and training sources (C4, RefinedWeb, and DCLM). For instance, a 1.6B language model trained with MeCo matches the downstream task performance of standard pre-training while using 33% less data. Additionally, MeCo enables us to steer language models by conditioning the inference prompt on either real or fabricated metadata that encodes the desired properties of the output: for example, prepending wikipedia.org to reduce harmful generations or factquizmaster.com (fabricated) to improve common knowledge task performance. We also demonstrate that MeCo is compatible with different types of metadata, such as model-generated topics. MeCo is remarkably simple, adds no computational overhead, and demonstrates promise in producing more capable and steerable language models.
Multi-view Bayesian optimisation in reduced dimension for engineering design
Authors: Thomas A. Archbold, Ieva Kazlauskaite, Fehmi Cirak
Date: 2025-01-02 00:00:00
Tasks: Bayesian Optimisation, Gaussian Processes, MULTI-VIEW LEARNING

Bayesian optimisation is an adaptive sampling strategy for constructing a Gaussian process surrogate to emulate a black-box computational model with the aim of efficiently searching for the global minimum. However, Gaussian processes have limited applicability for engineering problems with many design variables. Their scalability can be significantly improved by identifying a low-dimensional vector of latent variables that serve as inputs to the Gaussian process. In this paper, we introduce a multi-view learning strategy that considers both the input design variables and output data representing the objective or constraint functions, to identify a low-dimensional space of latent variables. Adopting a fully probabilistic viewpoint, we use probabilistic partial least squares (PPLS) to learn an orthogonal mapping from the design variables to the latent variables using training data consisting of inputs and outputs of the black-box computational model. The latent variables and posterior probability densities of the probabilistic partial least squares and Gaussian process models are determined sequentially and iteratively, with retraining occurring at each adaptive sampling iteration. We compare the proposed probabilistic partial least squares Bayesian optimisation (PPLS-BO) strategy to its deterministic counterpart, partial least squares Bayesian optimisation (PLS-BO), and classical Bayesian optimisation, demonstrating significant improvements in convergence to the global minimum.
Nonparametric estimation of a factorizable density using diffusion models
Authors: Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn, Minwoo Chae
Date: 2025-01-03 00:00:00
Tasks: Audio Generation, Density Estimation

In recent years, diffusion models, and more generally score-based deep generative models, have achieved remarkable success in various applications, including image and audio generation. In this paper, we view diffusion models as an implicit approach to nonparametric density estimation and study them within a statistical framework to analyze their surprising performance. A key challenge in high-dimensional statistical inference is leveraging low-dimensional structures inherent in the data to mitigate the curse of dimensionality. We assume that the underlying density exhibits a low-dimensional structure by factorizing into low-dimensional components, a property common in examples such as Bayesian networks and Markov random fields. Under suitable assumptions, we demonstrate that an implicit density estimator constructed from diffusion models adapts to the factorization structure and achieves the minimax optimal rate with respect to the total variation distance. In constructing the estimator, we design a sparse weight-sharing neural network architecture, where sparsity and weight-sharing are key features of practical architectures such as convolutional neural networks and recurrent neural networks.
On robust recovery of signals from indirect observations
Authors: Yannis Bekri, Anatoli Juditsky, Arkadi Nemirovski
Date: 2025-01-03 00:00:00

We consider an uncertain linear inverse problem as follows. Given observation $\omega=Ax_*+\zeta$ where $A\in {\bf R}^{m\times p}$ and $\zeta\in {\bf R}^{m}$ is observation noise, we want to recover unknown signal $x_*$, known to belong to a convex set ${\cal X}\subset{\bf R}^{n}$. As opposed to the "standard" setting of such problem, we suppose that the model noise $\zeta$ is "corrupted" -- contains an uncertain (deterministic dense or singular) component. Specifically, we assume that $\zeta$ decomposes into $\zeta=N\nu_*+\xi$ where $\xi$ is the random noise and $N\nu_*$ is the "adversarial contamination" with known $\cal N\subset {\bf R}^n$ such that $\nu_*\in \cal N$ and $N\in {\bf R}^{m\times n}$. We consider two "uncertainty setups" in which $\cal N$ is either a convex bounded set or is the set of sparse vectors (with at most $s$ nonvanishing entries). We analyse the performance of "uncertainty-immunized" polyhedral estimates -- a particular class of nonlinear estimates as introduced in [15, 16] -- and show how "presumably good" estimates of the sort may be constructed in the situation where the signal set is an ellitope (essentially, a symmetric convex set delimited by quadratic surfaces) by means of efficient convex optimization routines.
A Secure and Lightweight NDN-Based Vehicular Network Using Edge Computing and Certificateless Signcryption
Authors: Christian Nii Aflah Cobblah, QiXia, Jianbin Gao, Member, IEEE, HuXia, Goodlet Akwasi Kusi, and Isaac Amankona Obiri
Date: 2024-08-15 00:00:00
Proceeding: IEEE Internet of Things Journal 2024 8
Tasks: Edge-computing

Named data networking (NDN), which is an implementation of information-centric networking (ICN), has emerged as a promising new direction in networking as a result of the shortcomings of the transmission control protocol/Internet protocol (TCP/IP) architecture. NDN differs from the conventional networking protocols since it places more importance on the data than where it originated. Vehicular-NDN (VNDN) is a game-changing architecture due to its name-based forwarding and in-network caching. VNDN is designed to facilitate effective management of vehicular ad-hoc network (VANET) characteristics, such as high mobility, connection intermittency, and dynamic topology. However, the VNDN’s data verification procedure might lead to lengthy wait times, particularly for the mobile and vehicle networks. So, it is sometimes unfit for uses, where speed and security are paramount, such as exchanging safety messages. Moreover, the performance of the vehicle networks is negatively affected by the longer reaction times brought about by the computing-intensive jobs. Therefore, we present a lightweight VNDN-based certificateless signcryption strategy that leverages the hyperelliptic curve cryptosystem’s security hardness and edge computing. Analysis of the scheme’s security and comparisons with the similar systems demonstrate its effectiveness. Our suggested approach offers superior security with fewer computing and communication requirements, as verified by the final findings.
scGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph
Authors: Kexin Huang
Date: 2020-08-07 00:00:00
Tasks: Imputation

Single-cell RNA sequencing provides tremendous insights to understand biological systems. However, the noise from dropout can corrupt the downstream biological analysis. Hence, it is desirable to impute the dropouts accurately. In this work, we propose a simple and powerful dropout imputation method (scGNN) by applying a bottlenecked Graph Convolutional Neural Network on an induced hierarchical cell similarity graph. We show scGNN has competitive performance against state-of-the-art baselines across three datasets and can improve downstream analysis.
Solving automatic check-out with fine-tuned YOLO models
Authors: Skinderowicz Rafał
Date: 2024-11-28 00:00:00
Proceeding: Procedia Computer Science 2024 11

The Automatic Check-Out (ACO) problem entails the automatic detection and classification of products within a checkout image. Contrary to conventional methods that depend on synthetic images for training, this paper introduces a methodology that involves fine-tuning pretrained YOLOv8 and YOLOv9 models using a selected subset of validation images from the Large-Scale Retail Product Checkout (RPC) dataset. We demonstrate that the fine-tuning outcomes can be enhanced through the generation of additional examples by merging pairs of existing checkout images. Further enhancement of the models’ accuracy can be achieved by implementing a modified non-maximum suppression algorithm to sift through the detection results. The employed approach yields an average checkout accuracy of 95.16% on the test segment of the RPC dataset, showcasing the efficacy of leveraging pretrained models to address the complexities of real-world image recognition challenges in retail settings.
Multivariate Human Activity Segmentation: Systematic Benchmark with ClaSP
Authors: Arik Ermshaus, Patrick Schäfer, Ulf Leser
Date: 2025-01-01 00:00:00
Proceeding: Advanced Analytics and Learning on Temporal Data 2025 1
Tasks: Activity Recognition, Change Point Detection, Dimensionality Reduction, Human Activity Recognition, Segmentation, Semantic Segmentation, Time Series, Time Series Analysis

Human activity recognition (HAR) systems extract activities from observational data, such as sensor measurements from mobile devices, to provide for instance medical, fitness, or security information. A crucial initial step in these data analysis workflows is segmenting continuous numerical measurements into variable-sized segments that correspond to single activities. Human activity segmentation (HAS) enables downstream classification algorithms to label entire activities. Unfortunately, current time series segmentation (TSS) algorithms exhibit limited performance on multivariate sensor data due to complex temporal dynamics and irrelevant dimensions. This limits their applicability in HAR workflows. In this review, we provide a systematic benchmark of dimensionality reduction, model aggregation, and change point selection applied to the ClaSP TSS algorithm for real-world, multidimensional mobile sensing data. We evaluated the accuracy of the techniques in an experimental study using 250 data sets from the HAS challenge at ECML/PKDD and AALTD 2023. Our findings indicate that extending ClaSP for multivariate data, by aggregating internal representations, yields better results compared to reducing data dimensionality or selecting change points (CPs) from different channels. We report a new state of the art with 73% average accuracy on the challenge benchmark.
Controlled Spectral Uplifting for Indirect-Light-Metamerism
Authors: Mark van de Ruit, Elmar Eisemann
Date: 2024-12-03 00:00:00
Proceeding: SIGGRAPH Asia 2024 Conference Papers 2024 12
Tasks: Metamerism

Spectral rendering has received increasing attention in recent years. Yet, solutions to define spectral reflectances are mostly limited to uplifting techniques which deterministically augment existing RGB inputs. Only recently has uplifting been able to ensure a certain surface appearance under direct illuminants. Yet, prior work in this area limits artist expressiveness and is not well suited for designing the appearance of a scene, as indirect illumination is ignored entirely.

We present an uplifting technique with fine-grained spectral appearance control under direct and indirect illumination, even enabling the placement of spectral constraints in a specific scene. Our approach allows for a flexible authoring process, and solves for the resulting spectra efficiently. Additionally, we show that our method’s memory overhead during rendering is kept small, by introducing a compact spectral texture format.
An Efficient Dual-Hierarchy t-SNE Minimization
Authors: Mark van de Ruit, Markus Billeter, Elmar Eisemann
Date: 2021-09-29 00:00:00
Proceeding: IEEE Transactions on Visualization and Computer Graphics 2021 9

t-distributed Stochastic Neighbour Embedding (t-SNE) has become a standard for exploratory data analysis, as it is capable of revealing clusters even in complex data while requiring minimal user input. While its run-time complexity limited it to small datasets in the past, recent efforts improved upon the expensive similarity computations and the previously quadratic minimization. Nevertheless, t-SNE still has high runtime and memory costs when operating on millions of points. We present a novel method for executing the t-SNE minimization. While our method overall retains a linear runtime complexity, we obtain a significant performance increase in the most expensive part of the minimization. We achieve a significant improvement without a noticeable decrease in accuracy even when targeting a 3D embedding. Our method constructs a pair of spatial hierarchies over the embedding, which are simultaneously traversed to approximate many N-body interactions at once. We demonstrate an efficient GPGPU implementation and evaluate its performance against state-of-the-art methods on a variety of datasets.
A Family of Maximum Entropy Densities Matching Call Option Prices
Authors: Cassio Neri, Lorenz Schneider
Date: 2011-02-01 00:00:00

We investigate the position of the Buchen-Kelly density in a family of entropy maximising densities which all match European call option prices for a given maturity observed in the market. Using the Legendre transform which links the entropy function and the cumulant generating function, we show that it is both the unique continuous density in this family and the one with the greatest entropy. We present a fast root-finding algorithm that can be used to calculate the Buchen-Kelly density, and give upper boundaries for three different discrepancies that can be used as convergence criteria. Given the call prices, arbitrage-free digital prices at the same strikes can only move within upper and lower boundaries given by left and right call spreads. As the number of call prices increases, these bounds become tighter, and we give two examples where the densities converge to the Buchen-Kelly density in the sense of relative entropy when we use centered call spreads as proxies for digital prices. As pointed out by Breeden and Litzenberger, in the limit a continuous set of call prices completely determines the density.
HMM-LSTM Fusion Model for Economic Forecasting
Authors: Guhan Sivakumar
Date: 2025-01-01 00:00:00
Tasks: model

This paper explores the application of Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) neural networks for economic forecasting, focusing on predicting CPI inflation rates. The study explores a new approach that integrates HMM-derived hidden states and means as additional features for LSTM modeling, aiming to enhance the interpretability and predictive performance of the models. The research begins with data collection and preprocessing, followed by the implementation of the HMM to identify hidden states representing distinct economic conditions. Subsequently, LSTM models are trained using the original and augmented data sets, allowing for comparative analysis and evaluation. The results demonstrate that incorporating HMM-derived data improves the predictive accuracy of LSTM models, particularly in capturing complex temporal patterns and mitigating the impact of volatile economic conditions. Additionally, the paper discusses the implementation of Integrated Gradients for model interpretability and provides insights into the economic dynamics reflected in the forecasting outcomes.
Swin Transformer with Enhanced Dropout and Layer-wise Unfreezing for Facial Expression Recognition in Mental Health Detection
Authors: Mr. Mujiyanto, Mr. Arief Setyanto, Prof. Ema Utami and Prof. Kusrini Kusrini
Date: 2024-12-02 00:00:00
Proceeding: Engineering, Technology & Applied Science Research 2024 12
Tasks: Emotion Recognition, Facial Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Image Augmentation, Mental Stress Detection, Social Media Mental Health Detection

This study presents an improved Facial Expression Recognition (FER) model using Swin transformers for enhanced performance in detecting mental health through facial emotion analysis. In addition, some techniques involving better dropout and layer-wise unfreezing were implemented to reduce model overfitting. This study evaluates the proposed models on benchmark datasets such as FER2013 and CK+ and real-time Genius HR data. Model A has no dropout layer, Model B has focal loss, and Model C has enhanced dropout and layer-wise unfreezing. Model C was the best among all proposed models, achieving test accuracies of 71.23% on FER2013 and 78.65% on CK+. Weighted cross-entropy loss and image augmentation were used to handle class imbalance. Based on Model C emotion predictions, a scoring mechanism was designed to analyze employees' mental health for the next 30 days. The higher the score, the higher the risk of mental health. This study demonstrates a practical version of the Swin transformer in FER models for detecting and early mental health intervention.
Holistic Semantic Representation for Navigational Trajectory Generation
Authors: Ji Cao, Tongya Zheng, Qinghong Guo, Yu Wang, Junshu Dai, Shunyu Liu, Jie Yang, Jie Song, Mingli Song
Date: 2025-01-06 00:00:00
Tasks: Few-Shot Learning, Zero-Shot Learning

Trajectory generation has garnered significant attention from researchers in the field of spatio-temporal analysis, as it can generate substantial synthesized human mobility trajectories that enhance user privacy and alleviate data scarcity. However, existing trajectory generation methods often focus on improving trajectory generation quality from a singular perspective, lacking a comprehensive semantic understanding across various scales. Consequently, we are inspired to develop a HOlistic SEmantic Representation (HOSER) framework for navigational trajectory generation. Given an origin-and-destination (OD) pair and the starting time point of a latent trajectory, we first propose a Road Network Encoder to expand the receptive field of road- and zone-level semantics. Second, we design a Multi-Granularity Trajectory Encoder to integrate the spatio-temporal semantics of the generated trajectory at both the point and trajectory levels. Finally, we employ a Destination-Oriented Navigator to seamlessly integrate destination-oriented guidance. Extensive experiments on three real-world datasets demonstrate that HOSER outperforms state-of-the-art baselines by a significant margin. Moreover, the model's performance in few-shot learning and zero-shot learning scenarios further verifies the effectiveness of our holistic semantic representation.
Financial Named Entity Recognition: How Far Can LLM Go?
Authors: Yi-Te Lu, Yintong Huo
Date: 2025-01-04 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight their strengths and limitations, identify five representative failure types, and provide insights into their potential and challenges for domain-specific tasks.