pwc-archive/papers-with-abstracts huggingface dataset:
Rule-based Evolving Fuzzy System for Time Series Forecasting: New Perspectives Based on Type-2 Fuzzy Sets Measures Approach
Authors: Eduardo Santos de Oliveira Marques, Arthur Caio Vargas Pinto, Kaike Sa Teles Rocha Alves, Eduardo Pestana de Aguiar
Date: 2025-02-05 00:00:00
Tasks: Time Series, Time Series Forecasting

Real-world data contain uncertainty and variations that can be correlated to external variables, known as randomness. An alternative cause of randomness is chaos, which can be an important component of chaotic time series. One of the existing methods to deal with this type of data is the use of the evolving Fuzzy Systems (eFSs), which have been proven to be a powerful class of models for time series forecasting, due to their autonomy to handle the data and highly complex problems in real-world applications. However, due to its working structure, type-2 fuzzy sets can outperform type-1 fuzzy sets for highly uncertain scenarios. We then propose ePL-KRLS-FSM+, an enhanced class of evolving fuzzy modeling approach that combines participatory learning (PL), a kernel recursive least squares method (KRLS), type-2 fuzzy logic and data transformation into fuzzy sets (FSs). This improvement allows to create and measure type-2 fuzzy sets for better handling uncertainties in the data, generating a model that can predict chaotic data with increased accuracy. The model is evaluated using two complex datasets: the chaotic time series Mackey-Glass delay differential equation with different degrees of chaos, and the main stock index of the Taiwan Capitalization Weighted Stock Index - TAIEX. Model performance is compared to related state-of-the-art rule-based eFS models and classical approaches and is analyzed in terms of error metrics, runtime and the number of final rules. Forecasting results show that the proposed model is competitive and performs consistently compared with type-1 models, also outperforming other forecasting methods by showing the lowest error metrics and number of final rules.
STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery
Authors: Nicla Notarangelo, Charlotte Wirion, Frankwin van Winsen
Date: 2025-02-06 00:00:00
Proceeding: The Big Earth Data 2025 2
Tasks: Management

Flooding is a major global natural disaster exacerbated by climate change and urbanization. Timely assessment and mapping of inundations are crucial for preventive and emergency measures, driving the demand for curated global geospatial data to implement novel algorithms. This study introduces the STURM-Flood dataset, a high-quality, open-access, and DL-ready resource for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery, combined with ground-truth data from the Copernicus Emergency Management Service. The dataset encompasses 21,602 Sentinel-1 tiles and 2,675 Sentinel-2 tiles, each measuring 128 × 128 pixels at 10 m resolution, alongside corresponding water masks covering 60 flood events globally. Two U-Net models evaluated the dataset: Sentinel-1 achieved 83.61% test accuracy and 0.8327 weighted F1-score, while Sentinel-2 yielded 92.75% test accuracy and 0.9243 weighted F1-score. These results underscore the dataset’s potential in developing robust models for water extent mapping. STURM-Flood dataset aims to provide a valuable resource for research and development in flood mapping and disaster management. Future research could focus on expanding and refining different approaches and data sources for broader applications. The reference data and code are openly available in the Zenodo repository https://doi.org/10.5281/zenodo.12748983 and GitHub repository https://github.com/STURM-WEO/STURM-Flood.
Muizalix Pro
Authors: Muizalix Pro
Date: 2025-02-07 00:00:00
Proceeding: 02/07 2025 2
Tasks: Marketing

At Muizalix, we’re a dedicated team of creative professionals and digital experts passionate about helping businesses grow. Specializing in social media marketing, SEO, content creation, and website design, we provide comprehensive solutions to elevate your brand’s online presence. From crafting engaging campaigns to designing stunning visuals and optimizing digital strategies, the Muizalix team ensures your business stands out in today’s competitive market. Let’s collaborate to turn your vision into reality.
Scalable Framework for Classifying AI-Generated Content Across Modalities
Authors: Anh-Kiet Duong, Petra Gomez-Krämer
Date: 2025-02-01 00:00:00
Tasks: image-classification, Image Classification

The rapid growth of generative AI technologies has heightened the importance of effectively distinguishing between human and AI-generated content, as well as classifying outputs from diverse generative models. This paper presents a scalable framework that integrates perceptual hashing, similarity measurement, and pseudo-labeling to address these challenges. Our method enables the incorporation of new generative models without retraining, ensuring adaptability and robustness in dynamic scenarios. Comprehensive evaluations on the Defactify4 dataset demonstrate competitive performance in text and image classification tasks, achieving high accuracy across both distinguishing human and AI-generated content and classifying among generative methods. These results highlight the framework's potential for real-world applications as generative AI continues to evolve. Source codes are publicly available at https://github.com/ffyyytt/defactify4.
Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss Function
Authors: Mehrdad Asadi, Komi Sodoké, Ian J. Gerard, Marta Kersten-Oertel
Date: 2025-02-05 00:00:00
Tasks: Hierarchical Multi-label Classification, image-classification, Image Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this work, we present a novel approach to multi-label chest X-ray (CXR) image classification that enhances clinical interpretability while maintaining a streamlined, single-model, single-run training pipeline. Leveraging the CheXpert dataset and VisualCheXbert-derived labels, we incorporate hierarchical label groupings to capture clinically meaningful relationships between diagnoses. To achieve this, we designed a custom hierarchical binary cross-entropy (HBCE) loss function that enforces label dependencies using either fixed or data-driven penalty types. Our model achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.903 on the test set. Additionally, we provide visual explanations and uncertainty estimations to further enhance model interpretability. All code, model configurations, and experiment details are made available.
The Uniformly Rotated Mondrian Kernel
Authors: Calvin Osborne, Eliza O'Reilly
Date: 2025-02-06 00:00:00

Random feature maps are used to decrease the computational cost of kernel machines in large-scale problems. The Mondrian kernel is one such example of a fast random feature approximation of the Laplace kernel, generated by a computationally efficient hierarchical random partition of the input space known as the Mondrian process. In this work, we study a variation of this random feature map by applying a uniform random rotation to the input space before running the Mondrian process to approximate a kernel that is invariant under rotations. We obtain a closed-form expression for the isotropic kernel that is approximated, as well as a uniform convergence rate of the uniformly rotated Mondrian kernel to this limit. To this end, we utilize techniques from the theory of stationary random tessellations in stochastic geometry and prove a new result on the geometry of the typical cell of the superposition of uniformly rotated Mondrian tessellations. Finally, we test the empirical performance of this random feature map on both synthetic and real-world datasets, demonstrating its improved performance over the Mondrian kernel on a dataset that is debiased from the standard coordinate axes.
FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal Unalignment for Large Language Model
Authors: Jinwei Hu, Zhenglin Huang, Xiangyu Yin, Wenjie Ruan, Guangliang Cheng, Yi Dong, Xiaowei Huang
Date: 2025-02-03 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Machine Unlearning

Large language models have been widely applied, but can inadvertently encode sensitive or harmful information, raising significant safety concerns. Machine unlearning has emerged to alleviate this concern; however, existing training-time unlearning approaches, relying on coarse-grained loss combinations, have limitations in precisely separating knowledge and balancing removal effectiveness with model utility. In contrast, we propose Fine-grained Activation manipuLation by Contrastive Orthogonal uNalignment (FALCON), a novel representation-guided unlearning approach that leverages information-theoretic guidance for efficient parameter selection, employs contrastive mechanisms to enhance representation separation, and projects conflict gradients onto orthogonal subspaces to resolve conflicts between forgetting and retention objectives. Extensive experiments demonstrate that FALCON achieves superior unlearning effectiveness while maintaining model utility, exhibiting robust resistance against knowledge recovery attempts.
Transfer learning for semantic similarity measures based on symbolic regression
Authors: Jorge Martinez-Gil, Jose M. Chaves Gonzalez
Date: 2023-07-02 00:00:00
Proceeding: Journal of Intelligent & Fuzzy Systems 2023 7
Tasks: regression, Semantic Similarity, Semantic Textual Similarity, Symbolic Regression, Transfer Learning

Recently, transfer learning strategies have become ideal for reusing acquired knowledge through a training phase. The key idea is that reusing such knowledge brings advantages such as increased accuracy and considerable resource savings. In this work, we design a novel strategy for effective and efficient transfer learning in semantic similarity. Our approach is based on generating and transferring optimal models obtained through a symbolic regression process being able to stack evaluation scores from several fundamental techniques. After an exhaustive empirical study, the results lead to high accuracy in addition to significant savings in terms of training time consumed in most of the scenarios considered.
Multi-granularity Temporal Question Answering over Knowledge Graphs
Authors: Ziyang Chen, Jinzhi Liao, Xiang Zhao
Date: 2023-07-09 00:00:00
Proceeding: ACL 2023 7
Tasks: Knowledge Graphs, Question Answering

Recently, question answering over temporal knowledge graphs (i.e., TKGQA) has been introduced and investigated, in quest of reasoning about dynamic factual knowledge. To foster research on TKGQA, a few datasets have been curated (e.g., CronQuestions and Complex-CronQuestions), and various models have been proposed based on these datasets. Nevertheless, existing efforts overlook the fact that real-life applications of TKGQA also tend to be complex in temporal granularity, i.e., the questions may concern mixed temporal granularities (e.g., both day and month). To overcome the limitation, in this paper, we motivate the notion of multi-granularity temporal question answering over knowledge graphs and present a large scale dataset for multi-granularity TKGQA, namely MultiTQ. To the best of our knowledge, MultiTQis among the first of its kind, and compared with existing datasets on TKGQA, MultiTQfeatures at least two desirable aspects—ample relevant facts and multiple temporal granularities. It is expected to better reflect real-world challenges, and serve as a test bed for TKGQA models. In addition, we propose a competing baseline MultiQA over MultiTQ, which is experimentally demonstrated to be effective in dealing with TKGQA. The data and code are released at https://github.com/czy1999/MultiTQ.
TempQuestions: A Benchmark for Temporal Question Answering
Authors: Jia, Zhen and Abujabal, Abdalghani and Saha Roy, Rishiraj and Str\"{o}tgen, Jannik and Weikum, Gerhard
Date: 2018-04-23 00:00:00
Proceeding: WWW 2018 4
Tasks: Question Answering

Answering complex questions is one of the challenges that question-answering (QA) systems face today. While complexity has several facets, question dimensions like temporal and spatial intents necessitate specialized treatment. Methods geared towards such questions need benchmarks that reflect the desired aspects and challenges. Here, we take a key step in this direction, and release a new benchmark, TempQuestions, containing 1,271 questions, that are all temporal in nature, paired with their answers. As a key contribution that enabled the creation of this resource, we provide a crisp definition for temporal questions. Most questions require decomposing them into sub-questions, and the questions are of a kind that they would be best evaluated on a combination of structured data and unstructured text sources. Experiments with two QA systems demonstrate the need for further research on complex questions.
Explaining Context Length Scaling and Bounds for Language Models
Authors: Jingzhe Shi, Qinwei Ma, Hongyi Liu, Hang Zhao, Jeng-Neng Hwang, Lei LI
Date: 2025-02-03 00:00:00
Tasks: Language Modeling, Language Modelling

Long Context Language Models have drawn great attention in the past few years. There has been work discussing the impact of long context on Language Model performance: some find that long irrelevant context could harm performance, while some experimentally summarize loss reduction by relevant long context as Scaling Laws. This calls for a more thorough understanding on how long context impacts Language Modeling. In this work, we (1) propose a clean and effective theoretical framework for explaining the impact of context length on Language Modeling, from an Intrinsic Space perspective; and (2) conduct experiments on natural language and synthetic data, validating our proposed theoretical assumptions and deductions. Our theoretical framework can provide practical insights such as establishing that training dataset size dictates an optimal context length and bounds context length scaling for certain cases. We hope our work may inspire new long context Language Models, as well as future work studying Physics for Language Models. Code for our experiments is available at: https://github.com/JingzheShi/NLPCtlScalingAndBounds.
LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)
Authors: Aditya Kasliwal, Aryan Kamani, Ishaan Gakhar, Pratinav Seth, Sriya Rallabandi
Date: 2024-03-24 00:00:00
Proceeding: AAAI 2024 3
Tasks: Image-to-Image Translation, Super-Resolution

Recent advances in image-to-image translation involve the integration of non-visual imagery in deep models. Non-visual sensors, although more costly, often produce low-resolution images. To combat this, methods using RGB images to enhance the resolution of these modalities have been introduced. Fusing these modalities to achieve high-resolution results demands models with millions of parameters and extended inference times. We present LaMAR, a lightweight model. It employs Laplacian image pyramids combined with a low-resolution thermal image for Guided Thermal Super Resolution. By decomposing the RGB image into a Laplacian pyramid, LaMAR preserves image details and avoids high-resolution feature map computations, ensuring efficiency. With faster inference times and fewer parameters, our model demonstrates state-of-the-art results.
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution
Authors: Aditya Kasliwal, Ishaan Gakhar, Aryan Kamani
Date: 2024-05-17 00:00:00
Proceeding: AIML Systems 2024 5
Tasks: Contrastive Learning, Decoder, Depth Map Super-Resolution, Generative Adversarial Network, Super-Resolution

Consumer-grade depth sensors provide low-resolution depth maps; however, a high-resolution RGB camera is usually mounted on the same device and acquires a high-resolution image of the same scene. While deep learning and guided filtering methods gave decent results, recent works have highlighted the superiority of using RGB images for Depth Super Resolution. This paper proposes CoReGAN, a generative data fusion model that employs contrastive learning to regularize the extracted features of 2 independent encoders and 1 decoder for Guided Depth Super Resolution, demonstrating state-of-the-art results.
Molecularly stratified hypothalamic astrocytes are cellular foci for obesity
Authors: Tibor Harkany, Evgenii Tretiakov, Luis Varela, Jasna Jarc, Patrick Rebernik, Sylvia Newbold, Erik Keimpema, Alexei Verkhratsky, Tamas Horvath, Roman Romanov
Date: 2024-02-09 00:00:00
Proceeding: Research Square 2024 2
Tasks: Specificity

Astrocytes safeguard the homeostasis of the central nervous system. Despite their prominent morphological plasticity under conditions that challenge the brain’s adaptive capacity, the classification of astrocytes, and relating their molecular make-up to spatially devolved neuronal operations that specify behavior or metabolism, remained mostly futile. Although it seems unexpected in the era of single-cell biology, the lack of a major advance in stratifying astrocytes under physiological conditions rests on the incompatibility of ‘neurocentric’ algorithms that rely on stable developmental endpoints, lifelong transcriptional, neurotransmitter, and neuropeptide signatures for classification with the dynamic functional states, anatomic allocation, and allostatic plasticity of astrocytes. Simplistically, therefore, astrocytes are still grouped as ‘resting’ vs. ‘reactive’, the latter referring to pathological states marked by various inducible genes. Here, we introduced a machine learning-based feature recognition algorithm that benefits from the cumulative power of published single-cell RNA-seq data on astrocytes as a reference map to stepwise eliminate pleiotropic and inducible cellular features. For the healthy hypothalamus, this walk-back approach revealed gene regulatory networks (GRNs) that specified subsets of astrocytes, and could be used as landmarking tools for their anatomical assignment. The core molecular censuses retained by astrocyte subsets were sufficient to stratify them by allostatic competence, chiefly their signaling and metabolic interplay with neurons. Particularly, we found differentially expressed mitochondrial genes in insulin-sensing astrocytes and demonstrated their reciprocal signaling with neurons that work antagonistically within the food intake circuitry. As a proof-of-concept, we showed that disrupting Mfn2 expression in astrocytes reduced their ability to support dynamic circuit reorganization, a time-locked feature of satiety in the hypothalamus, thus leading to obesity in mice. Overall, our results suggest that astrocytes in the healthy brain are fundamentally more heterogeneous than previously thought and topologically mirror the specificity of local neurocircuits.
Generating crossmodal gene expression from cancer histopathology improves multimodal AI predictions
Authors: Samiran Dey, Christopher R. S. Banerji, Partha Basuchowdhuri, Sanjoy K. Saha, Deepak Parashar, Tapabrata Chakraborti
Date: 2025-02-01 00:00:00
Tasks: Prognosis

Emerging research has highlighted that artificial intelligence based multimodal fusion of digital pathology and transcriptomic features can improve cancer diagnosis (grading/subtyping) and prognosis (survival risk) prediction. However, such direct fusion for joint decision is impractical in real clinical settings, where histopathology is still the gold standard for diagnosis and transcriptomic tests are rarely requested, at least in the public healthcare system. With our novel diffusion based crossmodal generative AI model PathoGen, we show that genomic expressions synthesized from digital histopathology jointly predicts cancer grading and patient survival risk with high accuracy (state-of-the-art performance), certainty (through conformal coverage guarantee) and interpretability (through distributed attention maps). PathoGen code is available for open use by the research community through GitHub at https://github.com/Samiran-Dey/PathoGen.
Saving 77% of the Parameters in Large Language Models Technical Report
Authors: Joao Paulo Schwarz Schuler, Alejandra Rojas-Gómez
Date: 2025-02-09 00:00:00
Proceeding: researchgate.net 2025 2
Tasks: GPU, Text Generation

This technical report demonstrates that large language models (LLMs) can maintain their learning capacity while reducing their non-embedding parameters by up to 77%. We achieve this by adapting a parameter reduction technique originally developed for computer vision, replacing dense layers with an optimized subnetwork that contains grouped pointwise convolutions. Using Microsoft's phi-3-mini-4k-instruct as our baseline, we show that our optimized model (kphi-3) achieves comparable validation loss while using only 15-23% of the original non-embedding parameters. All experiments were conducted on a single NVIDIA L2 GPU within a 3-day timeframe, supporting the democratization of AI research. Our findings suggest that current LLM architectures may be substantially overparameterized, opening possibilities for more efficient model training and deployment.
Generating Symbolic World Models via Test-time Scaling of Large Language Models
Authors: Zhouliang Yu, Yuhuan Yuan, Tim Z. Xiao, Fuxiang Frank Xia, Jie Fu, Ge Zhang, Ge Lin, Weiyang Liu
Date: 2025-02-07 00:00:00

Solving complex planning problems requires Large Language Models (LLMs) to explicitly model the state transition to avoid rule violations, comply with constraints, and ensure optimality-a task hindered by the inherent ambiguity of natural language. To overcome such ambiguity, Planning Domain Definition Language (PDDL) is leveraged as a planning abstraction that enables precise and formal state descriptions. With PDDL, we can generate a symbolic world model where classic searching algorithms, such as A*, can be seamlessly applied to find optimal plans. However, directly generating PDDL domains with current LLMs remains an open challenge due to the lack of PDDL training data. To address this challenge, we propose to scale up the test-time computation of LLMs to enhance their PDDL reasoning capabilities, thereby enabling the generation of high-quality PDDL domains. Specifically, we introduce a simple yet effective algorithm, which first employs a Best-of-N sampling approach to improve the quality of the initial solution and then refines the solution in a fine-grained manner with verbalized machine learning. Our method outperforms o1-mini by a considerable margin in the generation of PDDL domains, achieving over 50\% success rate on two tasks (i.e., generating PDDL domains from natural language description or PDDL problems). This is done without requiring additional training. By taking advantage of PDDL as state abstraction, our method is able to outperform current state-of-the-art methods on almost all competition-level planning tasks.