pwc-archive/papers-with-abstracts huggingface dataset:
Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks
Authors: Martin Böckling, Heiko Paulheim, Andreea Iana
Date: 2025-05-22 00:00:00
Tasks: Hallucination, RAG, Retrieval, Retrieval-augmented Generation

Large Language Models (LLMs) have showcased impressive reasoning abilities, but often suffer from hallucinations or outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) remedies these shortcomings by grounding LLM responses in structured external information from a knowledge base. However, many KG-based RAG approaches struggle with (i) aligning KG and textual representations, (ii) balancing retrieval accuracy and efficiency, and (iii) adapting to dynamically updated KGs. In this work, we introduce Walk&Retrieve, a simple yet effective KG-based framework that leverages walk-based graph traversal and knowledge verbalization for corpus generation for zero-shot RAG. Built around efficient KG walks, our method does not require fine-tuning on domain-specific data, enabling seamless adaptation to KG updates, reducing computational overhead, and allowing integration with any off-the-shelf backbone LLM. Despite its simplicity, Walk&Retrieve performs competitively, often outperforming existing RAG systems in response accuracy and hallucination reduction. Moreover, it demonstrates lower query latency and robust scalability to large KGs, highlighting the potential of lightweight retrieval strategies as strong baselines for future RAG research.
CAFF-DINO: Multi-spectral object detection transformers with cross-attention features fusion
Authors: Kevin Helvig, Baptiste Abeloos, Pauline Trouve-Peloux
Date: 2024-09-27 00:00:00
Proceeding: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2024 9
Tasks: Multispectral Object Detection, Object, object-detection, Object Detection, Pedestrian Detection

Object detection on images can find benefit from coupling multiple spectra, each presenting specific useful features. However, building an efficient architecture coupling the different modalities is a complex task. Transformers, due to their ability to extract meaningful correlations between the different regions of the inputs appear as a promising way to perform features fusion across different spectra. This work presents a multi-spectral object detection architecture based on cross-attention features fusion (CAFF), combined with a transformer based detector (DINO). We demonstrate here the performance of the proposed approach in object detection compared with state-of-the-art approaches, on infrared-visible multi-spectral datasets. Moreover the robustness to systematic misalignment between image pairs is studied. The proposed approach is generic to any mono-spectrum transformer based detectors. The model developed in this study will be available in a dedicated github repository.
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study
Authors: Yuke Zhang
Date: 2025-05-22 00:00:00
Tasks: Interpretable Machine Learning

This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project's worldwide news feed using FinBERT -- a Bidirectional Encoder Representations from Transformers (BERT) based model pretrained on finance-specific language -- to construct daily sentiment indices incorporating mean tone, dispersion, and event impact. These indices drive an XGBoost classifier, benchmarked against logistic regression, to predict next-day returns for EUR/USD, USD/JPY, and 10-year U.S. Treasury futures (ZN). Rigorous out-of-sample (OOS) backtesting (5-fold expanding-window cross-validation, OOS period: c. 2017-April 2025) demonstrates exceptional, cost-adjusted performance for the XGBoost strategy: Sharpe ratios achieve 5.87 (EUR/USD), 4.65 (USD/JPY), and 4.65 (Treasuries), with respective compound annual growth rates (CAGRs) exceeding 50% in Foreign Exchange (FX) and 22% in bonds. Shapley Additive Explanations (SHAP) affirm that sentiment dispersion and article impact are key predictive features. Our findings establish that integrating domain-specific Natural Language Processing (NLP) with interpretable ML offers a potent and explainable source of macro alpha.
A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers
Authors: Kaike Sa Teles Rocha Alves, Eduardo Pestana de Aguiar
Date: 2021-11-01 00:00:00
Proceeding: Applied Soft Computing 2021 11
Tasks: Decision Making, Time Series, Time Series Forecasting

Big Data advancements motivate researchers to develop and improve intelligent models to deal efficiently and effectively with data. In this scenario, time series forecasting obtains even more attention. The literature demonstrated the better performance of such models in this subject. Forecasting is widely used in strategic planning to support decision-making, providing competitive differential to organizations. In this paper, a novel rule-based evolving Fuzzy System is proposed for time series forecasting. This is a robust model able to develop and update its structure in unknown environments, capture dynamics and changes of streams, and produce accurate results even when dealing with complex data. The introduced model implements the distance correlation to improve the rules’ quality by reducing their standard deviation. The model is evaluated using two synthetic datasets: the Mackey–Glass time-series and the nonlinear dynamic system identification. And finally, the introduced system is implemented to predict the hot spot temperature using three datasets from a real power transformer. Hot spot monitoring is necessary to maximize the load capacity and the lifespan of power transformers. The proposed method is evaluated in terms of root-mean-square error, non-dimensional index error, mean absolute error, runtime, and the number of final rules. The results are compared with traditional forecasting models and with some related state-of-the-art rule-based evolving Fuzzy Systems. The new evolving Fuzzy System outperformed the compared models for the Mackey–Glass time-series and the power transformers datasets concerning the errors. A statistical test comprised the superior performance of the introduced model. The algorithm also obtained a competitive execution time and number of final rules. The results demonstrate the high level of autonomy and adaptation of the model to predict accurately complex and non-stationary data. Seeing the importance of accurate models to deal with data to support decision-making, the results suggest the model’s implementation as a forecasting tool in strategic planning.
Financial Series Forecasting: A New Fuzzy Inference System for Crisp Values and Interval-Valued Predictions
Authors: Kaike Sa Teles Rocha Alves, Rosangela Ballini, Eduardo Pestana de Aguiar
Date: 2024-07-31 00:00:00
Proceeding: Computational Economics 2024 7

Fuzzy inference systems emerged as a machine learning model that provides accurate and explainable results. Two fuzzy inference systems are reported in the literature, Mamdani and Takagi–Sugeno–Kang. Mamdani implements fuzzy sets in the consequent part and provides more explainable results. On the other hand, Takagi–Sugeno–Kang is more suitable for modeling more complex data because it uses polynomial functions. However, there is no unique method to design Takagi–Sugeno–Kang rules in the literature, and some limitations can be found in the proposed models, such as no direct control over the number of rules, many hyper-parameters and increased complexity due to hybridization to form Takagi–Sugeno–Kang rules. To overcome these shortcomings, this paper proposes a new Takagi–Sugeno–Kang. The user can define the number of rules in the introduced model considering the accuracy-interpretability trade-off. Furthermore, the model has a lower number of hyper-parameters. Two filtering approaches are implemented to compute the consequent parameters, the recursive least squares, and the weighted recursive least squares. The model is applied to six relevant financial series, S &P 500, NASDAQ, TAIEX, CSI 300, KOSPI 200, and NYSE. The concept of interval-valued data is implemented to estimate the volatility of the economic series as a complement to classical forecasting. The results support that predictions of interval-valued data can be implemented as a complement to crisp prediction in defining decision-making strategies. The proposed approach’s results are compared with those of classical models and evolving Fuzzy Systems, and the model presented satisfactory results. The code of the proposed models is given at https://github.com/kaikerochaalves/NTSK.git.
A new Takagi–Sugeno–Kang model for time series forecasting
Authors: Kaike Sa Teles Rocha Alves, Caian Dutra de Jesus, Eduardo Pestana de Aguiar
Date: 2024-07-01 00:00:00
Proceeding: Engineering Applications of Artificial Intelligence 2024 7
Tasks: Time Series, Time Series Forecasting

A fuzzy inference system consists of a machine learning concept that combines accuracy and interpretability. They are divided into two main groups: Mamdani and Takagi–Sugeno-Kang. While Mamdani models favor interpretability, Takagi–Sugeno-Kang models provide more accurate results because of their ability to approximate a nonlinear system through a collection of linear subsystems. The evolving Takagi Sugeno model inspired a new class of Takagi–Sugeno-Kang models classified as an evolving fuzzy system, which can update their structure and functionality to adapt themselves to changes in the data. However, these models may provide several rules that reduce the interpretability. Furthermore, as they usually present many hyper-parameters, it can be challenging to obtain results that satisfy specific requirements in terms of the accuracy-interpretability trade-off. To overcome such shortcomings, this paper introduces a new model in which the only hyper-parameter is the maximum number of rules. Consequently, the user can define the number of rules considering the accuracy-interpretability trade-off. The introduced models are evaluated using benchmark time series, three well-known financial series, S&P 500, NASDAQ, and TAIEX, and renewable energy datasets. The results are compared with other state-of-the-art machine learning models, such as classical models and some rule-based evolving Fuzzy Systems. The results are evaluated regarding error metrics and the number of final rules. The proposed model obtained similar or equal performance in the simulations to the compared models with increased interpretability. The code of the proposed model is available at https://github.com/kaikerochaalves/NTSK.git.
Ranked Entropy Minimization for Continual Test-Time Adaptation
Authors: Jisu Han, Jaemin Na, Wonjun Hwang
Date: 2025-05-22 00:00:00
Tasks: Test-time Adaptation

Test-time adaptation aims to adapt to realistic environments in an online manner by learning during test time. Entropy minimization has emerged as a principal strategy for test-time adaptation due to its efficiency and adaptability. Nevertheless, it remains underexplored in continual test-time adaptation, where stability is more important. We observe that the entropy minimization method often suffers from model collapse, where the model converges to predicting a single class for all images due to a trivial solution. We propose ranked entropy minimization to mitigate the stability problem of the entropy minimization method and extend its applicability to continuous scenarios. Our approach explicitly structures the prediction difficulty through a progressive masking strategy. Specifically, it gradually aligns the model's probability distributions across different levels of prediction difficulty while preserving the rank order of entropy. The proposed method is extensively evaluated across various benchmarks, demonstrating its effectiveness through empirical results. Our code is available at https://github.com/pilsHan/rem
YNG TEk
Authors: YNG TEk
Date: 2025-05-23 00:00:00
Proceeding: 5/23 2025 5

Early Life

YNG TEk also known as (TEk Emanuel)is a American Rapper Born and raised in Miami, FL, making his first mainstream appearance on the Hard fought tv series aired on CW 33 and Fox tv networks viewed by millions. He is heard on the intro music to Hard Fought giving the series that deep urban sound. He is also known for being a outstanding music artist and phenomenal writer.

Career

YNG TEk, also known as TEk Emmanuel, is a rapper and songwriter from Miami, Florida. He grew up in Miami, surrounded by music, culture, and the energy of the city, which helped shape his style and love for hip-hop. From a young age, he was drawn to music and knew it was something he wanted to do for the rest of his life.

His first big moment came when he was featured on the TV show “Hard Fought,” which aired on popular networks like CW33 and FOX. The show was seen by millions of viewers across the country. YNG TEk’s music plays during the intro of the show, giving it a deep, urban sound that helped set the tone for the series. His appearance and music on the show introduced him to a wider audience and showed off his unique style

YNG TEk is more than just a rapper on TV—he’s also a talented songwriter known for his strong lyrics and real storytelling. He writes music that reflects real life, with topics like struggle, growth, and staying true to yourself. His songs are honest, emotional, and full of energy, which is why so many people connect with them.

As he continues his journey in music, YNG Tek is building a solid fanbase and working on more projects. Whether he’s performing, recording, or writing new tracks, he brings passion and skill to everything he does. With his talent and drive, YNG TEk (also known as TEk Emmanuel) is on his way to becoming a well-known name in the music world
GiGL: Large-Scale Graph Neural Networks at Snapchat
Authors: Tong Zhao, Yozen Liu, Matthew Kolodner, Kyle Montemayor, Elham Ghazizadeh, Ankit Batra, Zihao Fan, Xiaobin Gao, Xuan Guo, Jiwen Ren, Serim Park, Peicheng Yu, Jun Yu, Shubham Vij, Neil Shah
Date: 2025-02-20 00:00:00
Tasks: Graph Learning

Recent advances in graph machine learning (ML) with the introduction of Graph Neural Networks (GNNs) have led to a widespread interest in applying these approaches to business applications at scale. GNNs enable differentiable end-to-end (E2E) learning of model parameters given graph structure which enables optimization towards popular node, edge (link) and graph-level tasks. While the research innovation in new GNN layers and training strategies has been rapid, industrial adoption and utility of GNNs has lagged considerably due to the unique scale challenges that large-scale graph ML problems create. In this work, we share our approach to training, inference, and utilization of GNNs at Snapchat. To this end, we present GiGL (Gigantic Graph Learning), an open-source library to enable large-scale distributed graph ML to the benefit of researchers, ML engineers, and practitioners. We use GiGL internally at Snapchat to manage the heavy lifting of GNN workflows, including graph data preprocessing from relational DBs, subgraph sampling, distributed training, inference, and orchestration. GiGL is designed to interface cleanly with open-source GNN modeling libraries prominent in academia like PyTorch Geometric (PyG), while handling scaling and productionization challenges that make it easier for internal practitioners to focus on modeling. GiGL is used in multiple production settings, and has powered over 35 launches across multiple business domains in the last 2 years in the contexts of friend recommendation, content recommendation and advertising. This work details high-level design and tools the library provides, scaling properties, case studies in diverse business settings with industry-scale graphs, and several key lessons learned in employing graph ML at scale on large social data. GiGL is open-sourced at https://github.com/snap-research/GiGL.
VERDI: VLM-Embedded Reasoning for Autonomous Driving
Authors: Bowen Feng, Zhiting Mei, Baiang Li, Julian Ost, Roger Girgis, Anirudha Majumdar, Felix Heide
Date: 2025-05-21 00:00:00
Tasks: Autonomous Driving, Trajectory Planning

While autonomous driving (AD) stacks struggle with decision making under partial observability and real-world complexity, human drivers are capable of commonsense reasoning to make near-optimal decisions with limited information. Recent work has attempted to leverage finetuned Vision-Language Models (VLMs) for trajectory planning at inference time to emulate human behavior. Despite their success in benchmark evaluations, these methods are often impractical to deploy (a 70B parameter VLM inference at merely 8 tokens per second requires more than 160G of memory), and their monolithic network structure prohibits safety decomposition. To bridge this gap, we propose VLM-Embedded Reasoning for autonomous Driving (VERDI), a training-time framework that distills the reasoning process and commonsense knowledge of VLMs into the AD stack. VERDI augments modular differentiable end-to-end (e2e) AD models by aligning intermediate module outputs at the perception, prediction, and planning stages with text features explaining the driving reasoning process produced by VLMs. By encouraging alignment in latent space, VERDI enables the modular AD stack to internalize structured reasoning, without incurring the inference-time costs of large VLMs. We demonstrate the effectiveness of our method on the NuScenes dataset and find that VERDI outperforms existing e2e methods that do not embed reasoning by 10% in $\ell_{2}$ distance, while maintaining high inference speed.
LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols
Authors: Ziming Liu, Bryan Liu, Alvaro Valcarce, Xiaoli Chu
Date: 2025-05-22 00:00:00
Tasks: GPU

Integrating large AI models (LAMs) into 6G mobile networks promises to redefine protocol design and control-plane intelligence by enabling autonomous, cognitive network operations. While industry concepts, such as ETSI's Experiential Networked Intelligence (ENI), envision LAM-driven agents for adaptive network slicing and intent-based management, practical implementations still face challenges in protocol literacy and real-world deployment. This paper presents an end-to-end demonstration of a LAM that generates standards-compliant, ASN.1-encoded Radio Resource Control (RRC) messages as part of control-plane procedures inside a gNB. We treat RRC messaging as a domain-specific language and fine-tune a decoder-only transformer model (LLaMA class) using parameter-efficient Low-Rank Adaptation (LoRA) on RRC messages linearized to retain their ASN.1 syntactic structure before standard byte-pair encoding tokenization. This enables combinatorial generalization over RRC protocol states while minimizing training overhead. On 30k field-test request-response pairs, our 8 B model achieves a median cosine similarity of 0.97 with ground-truth messages on an edge GPU -- a 61 % relative gain over a zero-shot LLaMA-3 8B baseline -- indicating substantially improved structural and semantic RRC fidelity. Overall, our results show that LAMs, when augmented with Radio Access Network (RAN)-specific reasoning, can directly orchestrate control-plane procedures, representing a stepping stone toward the AI-native air-interface paradigm. Beyond RRC emulation, this work lays the groundwork for future AI-native wireless standards.
Leveraging Online Data to Enhance Medical Knowledge in a Small Persian Language Model
Authors: Mehrdad ghassabi, Pedram Rostami, Hamidreza Baradaran Kashani, Amirhossein Poursina, Zahra Kazemi, Milad Tavakoli
Date: 2025-05-21 00:00:00
Tasks: Language Modeling, Language Modelling, Medical Question Answering, Patient QA, Question Answering, Small Language Model

The rapid advancement of language models has demonstrated the potential of artificial intelligence in the healthcare industry. However, small language models struggle with specialized domains in low-resource languages like Persian. While numerous medical-domain websites exist in Persian, no curated dataset or corpus has been available making ours the first of its kind. This study explores the enhancement of medical knowledge in a small language model by leveraging accessible online data, including a crawled corpus from medical magazines and a dataset of real doctor-patient QA pairs. We fine-tuned a baseline model using our curated data to improve its medical knowledge. Benchmark evaluations demonstrate that the fine-tuned model achieves improved accuracy in medical question answering and provides better responses compared to its baseline. This work highlights the potential of leveraging open-access online data to enrich small language models in medical fields, providing a novel solution for Persian medical AI applications suitable for resource-constrained environments.
IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery
Authors: Aniketh Garikaparthi, Manasi Patwardhan, Lovekesh Vig, Arman Cohan
Date: 2025-04-23 00:00:00
Tasks: scientific discovery

The rapid advancement in capabilities of large language models (LLMs) raises a pivotal question: How can LLMs accelerate scientific discovery? This work tackles the crucial first stage of research, generating novel hypotheses. While recent work on automated hypothesis generation focuses on multi-agent frameworks and extending test-time compute, none of the approaches effectively incorporate transparency and steerability through a synergistic Human-in-the-loop (HITL) approach. To address this gap, we introduce IRIS: Interactive Research Ideation System, an open-source platform designed for researchers to leverage LLM-assisted scientific ideation. IRIS incorporates innovative features to enhance ideation, including adaptive test-time compute expansion via Monte Carlo Tree Search (MCTS), fine-grained feedback mechanism, and query-based literature synthesis. Designed to empower researchers with greater control and insight throughout the ideation process. We additionally conduct a user study with researchers across diverse disciplines, validating the effectiveness of our system in enhancing ideation. We open-source our code at https://github.com/Anikethh/IRIS-Interactive-Research-Ideation-System