pwc-archive/papers-with-abstracts huggingface dataset:
About Odomode Idris Opeyemi
Authors: freelancing
Date: 2025-02-10 00:00:00
Proceeding: 2025 2025 2
Tasks: Electrical Engineering, Marketing

I Odomode Idris Opeyemi was born on 29/05 /2005 into the family of Mr &Mrs Odomode. I was born into Muslim family. My father is a politician and a farmer while My mother is a trader . I was born in Ikirun Osun state.

 I started my nursery school at Great Success Nursery and Primary School when i was two (2) years old, I graduated from Great Success Nursery and Primary School in 2017 while i further my education and went to Holy Trinity Middle School which was later changed to Holy Trinity Secondary School ,this is where i completed my junior secondary school. After my graduated from Holy Trinity Secondary School i traveled to Araromi-Owu in 2020 where i later completed my senior secondary school at Araromi-Owu Grammar school .

After I Completed his Secondary School in July 2023 I came back to Ikirun where I began to learn Electrical Engineering, i learnt this Electrical Engineering for a year, while i was learning this Electrical Engineering, fortunately for me i met one of school mate one faithful day ,it was this his school mate that later introduced a business for him, which i later joined the business i joined is called Network marketing and freelancer which was under Neolife authority, which is also known as(FHG).
Adekunle Olajide Ridwan
Authors: Freelancer
Date: 2008-08-31 00:00:00
Proceeding: 2025 2008 8
Tasks: Navigate

'About Adekunle'

Adekunle Olajide Ridwan was born on August 31, 2008, into the family of Mr. and Mrs. Adekunle in Nigeria. As the second male child in a close-knit family, Adekunle exhibited remarkable intelligence and brilliance from an early age. His family nurtured his talents and encouraged him to strive for academic excellence.

Adekunle's educational journey began at Diamond Group of Schools, where his potential became evident. However, after facing some educational and age-related challenges, he was transferred to Babjo School. There, he encountered a significant hurdle: he was demoted from Basic 5 to Basic 1. Despite this setback, Adekunle remained determined, demonstrating resilience and a commitment to his studies.

In 2022, tragedy struck when his father passed away, a loss that deeply impacted the family. In the wake of this event, Adekunle transferred to Police Secondary School in Akure to continue his education. He embraced this new chapter with the intention of honoring his father’s legacy and pursuing a path aligned with his father’s career.

Career
Adekunle graduated from Police Secondary School in 2024, equipped not only with academic credentials but also with a vision for his future. During his time at the school, he discovered a passion for entrepreneurship and networking, leading him to an organization called Faith Heroic Generation, which partnered with Neolife. He diligently worked towards becoming a 5 Diamond Director, honing his skills as a network marketer and freelancer.



Personal Life and Dreams
In 2024, while in his final year of secondary school (SS3), Adekunle met, and fell in love with, Abdullateef Hamdalah Ololade. Their connection blossomed during their time together at Police Secondary School, and he recognized her as his second dream—finding a life partner with whom he could share his aspirations and build a future. The bond they forged during their formative years added a layer of personal motivation to his already burgeoning entrepreneurial ambitions.

Adekunle Olajide Ridwan's journey is one of determination, resilience, and ambition. Despite the challenges he faced—both academically and personally—he remains focused on achieving his goals. His aspirations to become a successful network marketer and to marry Abdullateef Hamdalah Ololade reflect his desire for a fulfilling personal and professional life. As he continues to navigate the path ahead, Adekunle aims not only for personal success but also to inspire others to pursue their dreams against all odds. His story embodies the belief that with hard work, perseverance, and love, one can create a promising future.
Machine learning enhanced optimisation of crash box design for crashworthiness analysis
Authors: Aditya Borse, Rutwik Gulakala, Marcus Stoffel
Date: 2023-09-22 00:00:00
Proceeding: 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) 2023 9
Tasks: Generative Adversarial Network

The primary goal of the current study is to optimise crash box designs for automobiles. Machine learning (ML) techniques are used to build an intelligent and reliable ML framework. With the help of this framework, the crash box design can be optimised for crashworthiness analysis. The optimisation of the crash box design is resource-intensive due to its intricate geometric design, use of a variety of materials, and extensive use of dynamic simulations to determine the ideal structural parameters through simulations. A reinforcement learning-based (RL) optimization technique is developed for this reason. The crash box is built with nonlinear first-order shear deformation shell elements to provide the necessary simulation data, and nonlinear elastoplastic material is used to model the dynamic impact process. The RL agents are tuned using finite element (FE) simulations and synthetic data generated by a generative adversarial network (GAN) to optimise and select the optimal model parameters for vehicle crashworthiness analysis. To estimate the correct model parameter required to fulfil specified crashworthiness metrics, the RL agent learns and optimises automatically. This method is effective in terms of resources and could be helpful in the early stages of vehicle development.
About olayemi Abolaji
Authors: Freelancer
Date: 2006-08-15 00:00:00
Proceeding: 2005 2006 8
Tasks: Marketing

My name is Olayemi Abolaji. I was born in 2006. I went to Babatope Memorial High School. I come from Ikoro, Ekiti; my parents are Mr. and Mrs. Abolaji. I am now a seasoned digital marketing professional with over 2 years of experience in creating and executing successful digital marketing campaigns. He has a proven track record of businesses across a variety of industries. I am a results-driven digital marketing professional with two years of experience in the industry. After earning a degree in science study from Babatope Memorial High Ikoro Ekiti in 2023, Olayemi Abolaji quickly established a reputation for leveraging digital strategies to drive brand growth and engagement.A digital marketer with two years of experience has typically built a strong foundation in online marketing strategies, analytics, and campaign execution. Here’s an outline of their
PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time Series
Authors: Patara Trirat, Jae-Gil Lee
Date: 2024-12-09 00:00:00
Proceeding: IEEE Transactions on Emerging Topics in Computational Intelligence 2024 12
Tasks: Anomaly Detection, Neural Architecture Search, Time Series, Time Series Anomaly Detection

Time-series anomaly detection uncovers rare errors or intriguing events of interest that significantly deviate from normal patterns. In order to precisely detect anomalies, a detector needs to capture intricate underlying temporal dynamics of a time series, often in multiple scales. Thus, a fixed-designed neural network may not be optimal for capturing such complex dynamics as different time-series data require different learning processes to reflect their unique characteristics. This paper proposes a Prediction-based neural Architecture Search for Time series Anomaly detection framework, dubbed PASTA. Unlike previous work, besides searching for a connection between operations, we design a novel search space to search for optimal connections in the temporal dimension among recurrent cells within/between each layer, i.e., temporal connectivity, and encode them via multi-level configuration encoding networks. Experimental results from both real-world and synthetic benchmarks show that the discovered architectures by PASTA outperform the second-best state-of-the-art baseline by around 13.6% in the enhanced time-series aware F1 score on average, confirming that the design of temporal connectivity is critical for time-series anomaly detection.
Fixed-Throughput GRAND with FIFO Scheduling
Authors: Filippo Christen, Darja Nonaca, Christoph Studer
Date: 2025-02-07 00:00:00
Tasks: Decoder, Scheduling

Guessing random additive noise decoding (GRAND) is a code-agnostic decoding method that iteratively guesses the noise pattern affecting the received codeword. The number of noise sequences to test depends on the noise realization. Thus, GRAND exhibits random runtime which results in nondeterministic throughput. However, real-time systems must process the incoming data at a fixed rate, necessitating a fixed-throughput decoder in order to avoid losing data. We propose a first-in first-out (FIFO) scheduling architecture that enables a fixed throughput while improving the block error rate (BLER) compared to the common approach of imposing a maximum runtime constraint per received codeword. Moreover, we demonstrate that the average throughput metric of GRAND-based hardware implementations typically provided in the literature can be misleading as one needs to operate at approximately one order of magnitude lower throughput to achieve the BLER of an unconstrained decoder.
WaferLLM: Large Language Model Inference at Wafer Scale
Authors: Congjie He, Yeqi Huang, Pei Mu, Ziming Miao, Jilong Xue, Lingxiao Ma, Fan Yang, Luo Mai
Date: 2025-02-06 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Large Language Model, model

Emerging AI accelerators increasingly adopt wafer-scale manufacturing technologies, integrating hundreds of thousands of AI cores in a mesh architecture with large distributed on-chip memory (tens of GB in total) and ultra-high on-chip memory bandwidth (tens of PB/s). However, current LLM inference systems, optimized for shared memory architectures like GPUs, fail to exploit these accelerators fully. We introduce WaferLLM, the first wafer-scale LLM inference system. WaferLLM is guided by a novel PLMR model (pronounced as "Plummer") that captures the unique hardware characteristics of wafer-scale architectures. Leveraging this model, WaferLLM pioneers wafer-scale LLM parallelism, optimizing the utilization of hundreds of thousands of on-chip cores. It also introduces MeshGEMM and MeshGEMV, the first GEMM and GEMV implementations designed to scale effectively on wafer-scale accelerators. Evaluations show that WaferLLM achieves up to 200$\times$ higher accelerator utilization than state-of-the-art methods. Leveraging a wafer-scale accelerator (Cerebras WSE2), WaferLLM delivers GEMV operations 606$\times$ faster and 16$\times$ more energy-efficient than on an NVIDIA A100 GPU. For full LLM inference, WaferLLM achieves 10-20$\times$ speedups over A100 GPU clusters running SGLang and vLLM. These advantages are expected to grow as wafer-scale AI models, software, and hardware continue to mature. WaferLLM is open-sourced at https://github.com/MeshInfra/WaferLLM.
STEMS: Spatial-Temporal Mapping Tool For Spiking Neural Networks
Authors: Sherif Eissa, Sander Stuijk, Floran de Putter, Andrea Nardi-Dei, Federico Corradi, Henk Corporaal
Date: 2025-02-05 00:00:00
Tasks: Event-based vision

Spiking Neural Networks (SNNs) are promising bio-inspired third-generation neural networks. Recent research has trained deep SNN models with accuracy on par with Artificial Neural Networks (ANNs). Although the event-driven and sparse nature of SNNs show potential for more energy efficient computation than ANNs, SNN neurons have internal states which evolve over time. Keeping track of SNN states can significantly increase data movement and storage requirements, potentially losing its advantages with respect to ANNs. This paper investigates the energy effects of having neuron states, and how it is influenced by the chosen mapping to realistic hardware architectures with advanced memory hierarchies. Therefore, we develop STEMS, a mapping design space exploration tool for SNNs. STEMS models SNN's stateful behavior and explores intra-layer and inter-layer mapping optimizations to minimize data movement, considering both spatial and temporal SNN dimensions. Using STEMS, we show up to 12x reduction in off-chip data movement and 5x reduction in energy (on top of intra-layer optimizations), on two event-based vision SNN benchmarks. Finally, neuron states may not be needed for all SNN layers. By optimizing neuron states for one of our benchmarks, we show 20x reduction in neuron states and 1.4x better performance without accuracy loss.
PICBench: Benchmarking LLMs for Photonic Integrated Circuits Design
Authors: Yuchao Wu, Xiaofei Yu, Hao Chen, Yang Luo, Yeyu Tong, Yuzhe ma
Date: 2025-02-05 00:00:00
Tasks: Benchmarking, Prompt Engineering

While large language models (LLMs) have shown remarkable potential in automating various tasks in digital chip design, the field of Photonic Integrated Circuits (PICs)-a promising solution to advanced chip designs-remains relatively unexplored in this context. The design of PICs is time-consuming and prone to errors due to the extensive and repetitive nature of code involved in photonic chip design. In this paper, we introduce PICBench, the first benchmarking and evaluation framework specifically designed to automate PIC design generation using LLMs, where the generated output takes the form of a netlist. Our benchmark consists of dozens of meticulously crafted PIC design problems, spanning from fundamental device designs to more complex circuit-level designs. It automatically evaluates both the syntax and functionality of generated PIC designs by comparing simulation outputs with expert-written solutions, leveraging an open-source simulator. We evaluate a range of existing LLMs, while also conducting comparative tests on various prompt engineering techniques to enhance LLM performance in automated PIC design. The results reveal the challenges and potential of LLMs in the PIC design domain, offering insights into the key areas that require further research and development to optimize automation in this field. Our benchmark and evaluation code is available at https://github.com/PICDA/PICBench.
LLM-USO: Large Language Model-based Universal Sizing Optimizer
Authors: Karthik Somayaji N. S, Peng Li
Date: 2025-02-04 00:00:00
Tasks: Bayesian Optimization, Language Modeling, Language Modelling, Large Language Model, Transfer Learning

The design of analog circuits is a cornerstone of integrated circuit (IC) development, requiring the optimization of complex, interconnected sub-structures such as amplifiers, comparators, and buffers. Traditionally, this process relies heavily on expert human knowledge to refine design objectives by carefully tuning sub-components while accounting for their interdependencies. Existing methods, such as Bayesian Optimization (BO), offer a mathematically driven approach for efficiently navigating large design spaces. However, these methods fall short in two critical areas compared to human expertise: (i) they lack the semantic understanding of the sizing solution space and its direct correlation with design objectives before optimization, and (ii) they fail to reuse knowledge gained from optimizing similar sub-structures across different circuits. To overcome these limitations, we propose the Large Language Model-based Universal Sizing Optimizer (LLM-USO), which introduces a novel method for knowledge representation to encode circuit design knowledge in a structured text format. This representation enables the systematic reuse of optimization insights for circuits with similar sub-structures. LLM-USO employs a hybrid framework that integrates BO with large language models (LLMs) and a learning summary module. This approach serves to: (i) infuse domain-specific knowledge into the BO process and (ii) facilitate knowledge transfer across circuits, mirroring the cognitive strategies of expert designers. Specifically, LLM-USO constructs a knowledge summary mechanism to distill and apply design insights from one circuit to related ones. It also incorporates a knowledge summary critiquing mechanism to ensure the accuracy and quality of the summaries and employs BO-guided suggestion filtering to identify optimal design points efficiently.
Noise Sensitivity and Learning Lower Bounds for Hierarchical Functions
Authors: Rupert Li, Elchanan Mossel
Date: 2025-02-07 00:00:00
Tasks: Sensitivity

Recent works explore deep learning's success by examining functions or data with hierarchical structure. To study the learning complexity of functions with hierarchical structure, we study the noise stability of functions with tree hierarchical structure on independent inputs. We show that if each function in the hierarchy is $\varepsilon$-far from linear, the noise stability is exponentially small in the depth of the hierarchy. Our results have immediate applications for learning. In the Boolean setting using the results of Dachman-Soled, Feldman, Tan, Wan and Wimmer (2014) our results provide Statistical Query super-polynomial lower bounds for learning classes that are based on hierarchical functions. Similarly, using the results of Diakonikolas, Kane, Pittas and Zarifis (2021) our results provide super-polynomial lower bounds for SQ learning under the Gaussian measure. Using the results of Abbe, Bengio, Cornacchiam, Kleinberg, Lotfi, Raghu and Zhang (2022) our results imply sample complexity lower bounds for learning hierarchical functions with gradient descent on fully connected neural networks.
Algorithmic causal structure emerging through compression
Authors: Liang Wendong, Simon Buchholz, Bernhard Schölkopf
Date: 2025-02-06 00:00:00

We explore the relationship between causality, symmetry, and compression. We build on and generalize the known connection between learning and compression to a setting where causal models are not identifiable. We propose a framework where causality emerges as a consequence of compressing data across multiple environments. We define algorithmic causality as an alternative definition of causality when traditional assumptions for causal identifiability do not hold. We demonstrate how algorithmic causal and symmetric structures can emerge from minimizing upper bounds on Kolmogorov complexity, without knowledge of intervention targets. We hypothesize that these insights may also provide a novel perspective on the emergence of causality in machine learning models, such as large language models, where causal relationships may not be explicitly identifiable.
Cascaded Learned Bloom Filter for Optimal Model-Filter Size Balance and Fast Rejection
Authors: Atsuki Sato, Yusuke Matsui
Date: 2025-02-06 00:00:00

Recent studies have demonstrated that learned Bloom filters, which combine machine learning with the classical Bloom filter, can achieve superior memory efficiency. However, existing learned Bloom filters face two critical unresolved challenges: the balance between the machine learning model size and the Bloom filter size is not optimal, and the reject time cannot be minimized effectively. We propose the Cascaded Learned Bloom Filter (CLBF) to address these issues. Our dynamic programming-based optimization automatically selects configurations that achieve an optimal balance between the model and filter sizes while minimizing reject time. Experiments on real-world datasets show that CLBF reduces memory usage by up to 24% and decreases reject time by up to 14 times compared to state-of-the-art learned Bloom filters.
Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives
Authors: Elliot Meyerson, Xin Qiu
Date: 2025-02-04 00:00:00
Tasks: Position

Decomposing hard problems into subproblems often makes them easier and more efficient to solve. With large language models (LLMs) crossing critical reliability thresholds for a growing slate of capabilities, there is an increasing effort to decompose systems into sets of LLM-based agents, each of whom can be delegated sub-tasks. However, this decomposition (even when automated) is often intuitive, e.g., based on how a human might assign roles to members of a human team. How close are these role decompositions to optimal? This position paper argues that asymptotic analysis with LLM primitives is needed to reason about the efficiency of such decomposed systems, and that insights from such analysis will unlock opportunities for scaling them. By treating the LLM forward pass as the atomic unit of computational cost, one can separate out the (often opaque) inner workings of a particular LLM from the inherent efficiency of how a set of LLMs are orchestrated to solve hard problems. In other words, if we want to scale the deployment of LLMs to the limit, instead of anthropomorphizing LLMs, asymptotic analysis with LLM primitives should be used to reason about and develop more powerful decompositions of large problems into LLM agents.
3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery
Authors: Xiuyuan Hu, Guoqing Liu, Can Chen, Yang Zhao, Hao Zhang, Xue Liu
Date: 2025-02-07 00:00:00
Tasks: Drug Design, Drug Discovery

Structure-based drug discovery, encompassing the tasks of protein-ligand docking and pocket-aware 3D drug design, represents a core challenge in drug discovery. However, no existing work can deal with both tasks to effectively leverage the duality between them, and current methods for each task are hindered by challenges in modeling 3D information and the limitations of available data. To address these issues, we propose 3DMolFormer, a unified dual-channel transformer-based framework applicable to both docking and 3D drug design tasks, which exploits their duality by utilizing docking functionalities within the drug design process. Specifically, we represent 3D pocket-ligand complexes using parallel sequences of discrete tokens and continuous numbers, and we design a corresponding dual-channel transformer model to handle this format, thereby overcoming the challenges of 3D information modeling. Additionally, we alleviate data limitations through large-scale pre-training on a mixed dataset, followed by supervised and reinforcement learning fine-tuning techniques respectively tailored for the two tasks. Experimental results demonstrate that 3DMolFormer outperforms previous approaches in both protein-ligand docking and pocket-aware 3D drug design, highlighting its promising application in structure-based drug discovery. The code is available at: https://github.com/HXYfighter/3DMolFormer .
Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliance
Authors: Pratinav Seth, Vinay Kumar Sankarapu
Date: 2025-02-07 00:00:00

This position paper emphasizes the critical gap in the evaluation of Explainable AI (XAI) due to the lack of standardized and reliable metrics, which diminishes its practical value, trustworthiness, and ability to meet regulatory requirements. Current evaluation methods are often fragmented, subjective, and biased, making them prone to manipulation and complicating the assessment of complex models. A central issue is the absence of a ground truth for explanations, complicating comparisons across various XAI approaches. To address these challenges, we advocate for widespread research into developing robust, context-sensitive evaluation metrics. These metrics should be resistant to manipulation, relevant to each use case, and based on human judgment and real-world applicability. We also recommend creating domain-specific evaluation benchmarks that align with the user and regulatory needs of sectors such as healthcare and finance. By encouraging collaboration among academia, industry, and regulators, we can create standards that balance flexibility and consistency, ensuring XAI explanations are meaningful, trustworthy, and compliant with evolving regulations.