pwc-archive/papers-with-abstracts huggingface dataset:
Grapevine Disease Prediction Using Climate Variables from Multi-Sensor Remote Sensing Imagery via a Transformer Model
Authors: Weiying Zhao, Natalia Efremova
Date: 2024-06-11 00:00:00
Tasks: Disease Prediction, Management

Early detection and management of grapevine diseases are important in pursuing sustainable viticulture. This paper introduces a novel framework leveraging the TabPFN model to forecast blockwise grapevine diseases using climate variables from multi-sensor remote sensing imagery. By integrating advanced machine learning techniques with detailed environmental data, our approach significantly enhances the accuracy and efficiency of disease prediction in vineyards. The TabPFN model's experimental evaluations showcase comparable performance to traditional gradient-boosted decision trees, such as XGBoost, CatBoost, and LightGBM. The model's capability to process complex data and provide per-pixel disease-affecting probabilities enables precise, targeted interventions, contributing to more sustainable disease management practices. Our findings underscore the transformative potential of combining Transformer models with remote sensing data in precision agriculture, offering a scalable solution for improving crop health and productivity while reducing environmental impact.
Anna Karenina Strikes Again: Pre-Trained LLM Embeddings May Favor High-Performing Learners
Authors: Abigail Gurin Schleifer, Beata Beigman Klebanov, Moriah Ariely, Giora Alexandron
Date: 2024-06-06 00:00:00
Tasks: Clustering

Unsupervised clustering of student responses to open-ended questions into behavioral and cognitive profiles using pre-trained LLM embeddings is an emerging technique, but little is known about how well this captures pedagogically meaningful information. We investigate this in the context of student responses to open-ended questions in biology, which were previously analyzed and clustered by experts into theory-driven Knowledge Profiles (KPs). Comparing these KPs to ones discovered by purely data-driven clustering techniques, we report poor discoverability of most KPs, except for the ones including the correct answers. We trace this "discoverability bias" to the representations of KPs in the pre-trained LLM embeddings space.
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
Authors: Zijian Li, Qingyan Guo, Jiawei Shao, Lei Song, Jiang Bian, Jun Zhang, Rui Wang
Date: 2024-06-03 00:00:00
Tasks: Graph Neural Network, Language Modelling, Large Language Model, Question Answering, Retrieval, Retrieval-augmented Generation

Retrieval augmented generation has revolutionized large language model (LLM) outputs by providing factual supports. Nevertheless, it struggles to capture all the necessary knowledge for complex reasoning questions. Existing retrieval methods typically divide reference documents into passages, treating them in isolation. These passages, however, are often interrelated, such as passages that are contiguous or share the same keywords. Therefore, it is crucial to recognize such relatedness for enhancing the retrieval process. In this paper, we propose a novel retrieval method, called GNN-Ret, which leverages graph neural networks (GNNs) to enhance retrieval by exploiting the relatedness between passages. Specifically, we first construct a graph of passages by connecting passages that are structure-related or keyword-related. A graph neural network (GNN) is then leveraged to exploit the relationships between passages and improve the retrieval of supporting passages. Furthermore, we extend our method to handle multi-hop reasoning questions using a recurrent graph neural network (RGNN), named RGNN-Ret. At each step, RGNN-Ret integrates the graphs of passages from previous steps, thereby enhancing the retrieval of supporting passages. Extensive experiments on benchmark datasets demonstrate that GNN-Ret achieves higher accuracy for question answering with a single query of LLMs than strong baselines that require multiple queries, and RGNN-Ret further improves accuracy and achieves state-of-the-art performance, with up to 10.4% accuracy improvement on the 2WikiMQA dataset.
Connectionist Temporal Fusion for Sign Language Translation
Authors: Wang, Shuo; Guo, Dan; Zhou, Wen gang; Zha, Zheng jun; Wang, Meng
Date: 2018-10-01 00:00:00
Proceeding: ACM MM 2018 10
Tasks: Sentence, Sign Language Recognition, Sign Language Translation, Translation

Continuous sign language translation (CSLT) is a weakly supervised problem aiming at translating vision-based videos into natural languages under complicated sign linguistics, where the ordered words in a sentence label have no exact boundary of each sign action in the video. This paper proposes a hybrid deep architecture which consists of a temporal convolution module (TCOV), a bidirectional gated recurrent unit module (BGRU), and a fusion layer module (FL) to address the CSLT problem. TCOV captures short-term temporal transition on adjacent clip features (local pattern), while BGRU keeps the long-term context transition across temporal dimension (global pattern). FL concatenates the feature embedding of TCOV and BGRU to learn their complementary relationship (mutual pattern). Thus we propose a joint connectionist temporal fusion (CTF) mechanism to utilize the merit of each module. The proposed joint CTC loss optimization and deep classification score-based decoding fusion strategy are designed to boost performance. With only once training, our model under the CTC constraints achieves comparable performance to other existing methods with multiple EM iterations. Experiments are tested and verified on a benchmark, \emph{i.e.} the RWTH-PHOENIX-Weather dataset, which demonstrate the effectiveness of our proposed method.
Dense Temporal Convolution Network for Sign Language Translation
Authors: Dan Guo; Shuo Wang; Qi Tian;Meng Wang
Date: 2019-05-01 00:00:00
Proceeding: IJCAI 2019 5
Tasks: Sentence, Sign Language Recognition, Translation

The sign language translation (SLT) which aims at translating a sign language video into natural language is weakly supervised given that there is no exact mapping relationship between visual actions and textual words in a sentence label.
To align the sign language actions and translate them into the respective words automatically, this paper proposes a dense temporal convolution network, termed \emph{DenseTCN} which captures the actions in hierarchical views. 
Within this network, a temporal convolution (TC) is designed to learn the short-term correlation among adjacent features and further extended to a dense hierarchical structure. In the $k^\mathrm{th}$ TC layer, we integrate the outputs of all preceding layers together: (1) The TC in a deeper layer essentially has larger receptive fields, which captures long-term temporal context by the hierarchical content transition. (2) The integration addresses the SLT problem by different views, including embedded short-term and extended long-term sequential learning. Finally, we adopt the CTC loss and a fusion strategy to learn the feature-wise classification and generate the translated sentence. The experimental results on two popular sign language benchmarks, \emph{i.e.} PHOENIX and USTC-ConSents, demonstrate the effectiveness of our proposed method in terms of various measurements.
PuzzleFusion++: Auto-agglomerative 3D Fracture Assembly by Denoise and Verify
Authors: Zhengqing Wang, Jiacheng Chen, Yasutaka Furukawa
Date: 2024-06-01 00:00:00

This paper proposes a novel "auto-agglomerative" 3D fracture assembly method, PuzzleFusion++, resembling how humans solve challenging spatial puzzles. Starting from individual fragments, the approach 1) aligns and merges fragments into larger groups akin to agglomerative clustering and 2) repeats the process iteratively in completing the assembly akin to auto-regressive methods. Concretely, a diffusion model denoises the 6-DoF alignment parameters of the fragments simultaneously, and a transformer model verifies and merges pairwise alignments into larger ones, whose process repeats iteratively. Extensive experiments on the Breaking Bad dataset show that PuzzleFusion++ outperforms all other state-of-the-art techniques by significant margins across all metrics, in particular by over 10% in part accuracy and 50% in Chamfer distance. The code will be available on our project page: https://puzzlefusion-plusplus.github.io.
HORAE: A Domain-Agnostic Language for Automated Service Regulation
Authors: Yutao Sun, Mingshuai Chen, Tiancheng Zhao, Kangjia Zhao, He Li, Jintao Chen, Zhongyi Wang, Liqiang Lu, Xinkui Zhao, Shuiguang Deng, Jianwei Yin
Date: 2024-06-06 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domains and thus are difficult to generalize in an automated manner. This paper presents Horae, a unified specification language for modeling (multimodal) regulation rules across a diverse set of domains. We showcase how Horae facilitates an intelligent service regulation pipeline by further exploiting a fine-tuned large language model named RuleGPT that automates the Horae modeling process, thereby yielding an end-to-end framework for fully automated intelligent service regulation. The feasibility and effectiveness of our framework are demonstrated over a benchmark of various real-world regulation domains. In particular, we show that our open-sourced, fine-tuned RuleGPT with 7B parameters suffices to outperform GPT-3.5 and perform on par with GPT-4o.
SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound
Authors: Rishit Dagli, Shivesh Prakash, Robert Wu, Houman Khosravani
Date: 2024-06-06 00:00:00
Tasks: Audio Generation

Generating combined visual and auditory sensory experiences is critical for the consumption of immersive content. Recent advances in neural generative models have enabled the creation of high-resolution content across multiple modalities such as images, text, speech, and videos. Despite these successes, there remains a significant gap in the generation of high-quality spatial audio that complements generated visual content. Furthermore, current audio generation models excel in either generating natural audio or speech or music but fall short in integrating spatial audio cues necessary for immersive experiences. In this work, we introduce SEE-2-SOUND, a zero-shot approach that decomposes the task into (1) identifying visual regions of interest; (2) locating these elements in 3D space; (3) generating mono-audio for each; and (4) integrating them into spatial audio. Using our framework, we demonstrate compelling results for generating spatial audio for high-quality videos, images, and dynamic images from the internet, as well as media generated by learned approaches.
U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery
Authors: Ivica Dimitrovski, Vlatko Spasev, Suzana Loshkovska, Ivan Kitanovski
Date: 2024-06-08 00:00:00
Proceeding: Remote Sensing 2024 6
Tasks: Segmentation, Segmentation Of Remote Sensing Imagery, Semantic Segmentation

Semantic segmentation of remote sensing imagery stands as a fundamental task within the domains of both remote sensing and computer vision. Its objective is to generate a comprehensive pixel-wise segmentation map of an image, assigning a specific label to each pixel. This facilitates in-depth analysis and comprehension of the Earth’s surface. In this paper, we propose an approach for enhancing semantic segmentation performance by employing an ensemble of U-Net models with three different backbone networks: Multi-Axis Vision Transformer, ConvFormer, and EfficientNet. The final segmentation maps are generated through a geometric mean ensemble method, leveraging the diverse representations learned by each backbone network. The effectiveness of the base U-Net models and the proposed ensemble is evaluated on multiple datasets commonly used for semantic segmentation tasks in remote sensing imagery, including LandCover.ai, LoveDA, INRIA, UAVid, and ISPRS Potsdam datasets. Our experimental results demonstrate that the proposed approach achieves state-of-the-art performance, showcasing its effectiveness and robustness in accurately capturing the semantic information embedded within remote sensing images.
Inverse Constitutional AI: Compressing Preferences into Principles
Authors: Arduin Findeis, Timo Kaufmann, Eyke Hüllermeier, Samuel Albanie, Robert Mullins
Date: 2024-06-02 00:00:00
Tasks: Chatbot, Language Modelling, Large Language Model

Feedback data is widely used for fine-tuning and evaluating state-of-the-art AI models. Pairwise text preferences, where human or AI annotators select the "better" of two options, are particularly common. Such preferences are used to train (reward) models or to rank models with aggregate statistics. For many applications it is desirable to understand annotator preferences in addition to modelling them - not least because extensive prior work has shown various unintended biases in preference datasets. Yet, preference datasets remain challenging to interpret. Neither black-box reward models nor statistics can answer why one text is preferred over another. Manual interpretation of the numerous (long) response pairs is usually equally infeasible. In this paper, we introduce the Inverse Constitutional AI (ICAI) problem, formulating the interpretation of pairwise text preference data as a compression task. In constitutional AI, a set of principles (a constitution) is used to provide feedback and fine-tune AI models. ICAI inverts this process: given a feedback dataset, we aim to extract a constitution that best enables a large language model (LLM) to reconstruct the original annotations. We propose a corresponding ICAI algorithm and validate its generated constitutions quantitatively based on annotation reconstruction accuracy on several datasets: (a) synthetic feedback data with known principles; (b) AlpacaEval cross-annotated human feedback data; (c) crowdsourced Chatbot Arena data; and (d) PRISM data from diverse demographic groups. As a short and interpretable representation of the original dataset, generated constitutions have many potential use cases: help identify undesirable annotator biases, understand model performance better, scale feedback to unseen data, or adapt models to individual user or group preferences. We release the source code at https://github.com/rdnfn/icai.
How well it works: Benchmarking performance of GPT models on medical natural language processing tasks
Authors: Hui Feng, Kathryn Rough, Paul B Milligan, Francesco Tombini, Tom Kwon, Khaldoun Zine El Abidine, Christina D Mack, Benjamin Hughes
Date: 2024-06-12 00:00:00
Proceeding: medRxiv 2024 6
Tasks: Benchmarking

Importance: The ability of large language models (LLMs) to generate high-quality, human-like text has been accompanied with speculation about their application in healthcare, alongside ethical and safety concerns. Objective: Evaluate LLM performance on medical natural language processing (NLP) tasks, benchmarked against other commercially available tools. Design: Observational study to evaluate and compare model performance. All models were commercially available and were evaluated without modification. Setting: The Text Analysis Coding (TAC) 2017 challenge was used to assess ability to perform medical coding using standard MedDRA preferred terms. Text from 55 publicly available de-identified medical transcription reports were annotated to identify pre-defined medical concepts (age, disease/symptom, body structure, medication name, and medication dosage). Participants: Publicly available, de-identified adverse event and medical transcription reports were used for evaluation. Exposures: For each task, general LLMs (GPT-3.5-turbo, GPT-4) were compared to commercially available healthcare NLP tools (Microsoft Text Analytics for Health, Amazon Comprehend Medical, IQVIA API Marketplace). Main Outcomes and Measures: For each NLP task, sensitivity, positive predictive value (PPV) and F1 score were calculated. Because GPT models had variable outputs, the range of metrics over 5 trials is reported. Results: For MedDRA coding, GPT-4 had similar F1 score performance to healthcare NLP algorithms (GPT-4: 0.67 to 0.73; Microsoft Text Analytics for Health: 0.66, IQVIA API Marketplace: 0.72), while GPT- 3.5-turbo had considerably lower performance (0.50 to 0.51). For medical information extraction, LLM performance varied widely across differing medical concepts; the highest F1 scores were for age (GPT- 3.5-turbo: 0.82 to 0.83, GPT-4: 0.84 to 0.87) and medication name (GPT-3.5-turbo: 0.55 to 0.59, GPT-4: 0.70 to 0.76), while F1 scores for disease/symptom, body structure, and medication dosage were lower than those observed for the healthcare NLP tools. GPT-3.5-turbo and GPT-4 generally had lower sensitivity than comparators. Conclusions and Relevance: In the absence of domain-specific fine tuning, GPT-4 performed similarly to healthcare-specific NLP tools on some tasks and less accurately on others; GPT-3.5-turbo was consistently less accurate than comparators. To maximize benefit and reduce risk of harm, robust quantitative evaluation for specific tasks should be performed prior to implementing LLMs in medical contexts.
Differentiable Combinatorial Scheduling at Scale
Authors: Mingju Liu, Yingjie Li, Jiaqi Yin, Zhiru Zhang, Cunxi Yu
Date: 2024-06-06 00:00:00
Tasks: Scheduling

This paper addresses the complex issue of resource-constrained scheduling, an NP-hard problem that spans critical areas including chip design and high-performance computing. Traditional scheduling methods often stumble over scalability and applicability challenges. We propose a novel approach using a differentiable combinatorial scheduling framework, utilizing Gumbel-Softmax differentiable sampling technique. This new technical allows for a fully differentiable formulation of linear programming (LP) based scheduling, extending its application to a broader range of LP formulations. To encode inequality constraints for scheduling tasks, we introduce \textit{constrained Gumbel Trick}, which adeptly encodes arbitrary inequality constraints. Consequently, our method facilitates an efficient and scalable scheduling via gradient descent without the need for training data. Comparative evaluations on both synthetic and real-world benchmarks highlight our capability to significantly improve the optimization efficiency of scheduling, surpassing state-of-the-art solutions offered by commercial and open-source solvers such as CPLEX, Gurobi, and CP-SAT in the majority of the designs.
Large Language Model (LLM) for Standard Cell Layout Design Optimization
Authors: Chia-Tung Ho, Haoxing Ren
Date: 2024-05-24 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Layout Design

Standard cells are essential components of modern digital circuit designs. With process technologies advancing toward 2nm, more routability issues have arisen due to the decreasing number of routing tracks, increasing number and complexity of design rules, and strict patterning rules. The state-of-the-art standard cell design automation framework is able to automatically design standard cell layouts in advanced nodes, but it is still struggling to generate highly competitive Performance-Power-Area (PPA) and routable cell layouts for complex sequential cell designs. Consequently, a novel and efficient methodology incorporating the expertise of experienced human designers to incrementally optimize the PPA of cell layouts is highly necessary and essential. High-quality device clustering, with consideration of netlist topology, diffusion sharing/break and routability in the layouts, can reduce complexity and assist in finding highly competitive PPA, and routable layouts faster. In this paper, we leverage the natural language and reasoning ability of Large Language Model (LLM) to generate high-quality cluster constraints incrementally to optimize the cell layout PPA and debug the routability with ReAct prompting. On a benchmark of sequential standard cells in 2nm, we demonstrate that the proposed method not only achieves up to 19.4% smaller cell area, but also generates 23.5% more LVS/DRC clean cell layouts than previous work. In summary, the proposed method not only successfully reduces cell area by 4.65% on average, but also is able to fix routability in the cell layout designs.
TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Accelerators
Authors: Yifan Qin, Zheyu Yan, Zixuan Pan, Wujie Wen, Xiaobo Sharon Hu, Yiyu Shi
Date: 2024-05-08 00:00:00

Compute-in-memory (CIM) accelerators using non-volatile memory (NVM) devices offer promising solutions for energy-efficient and low-latency Deep Neural Network (DNN) inference execution. However, practical deployment is often hindered by the challenge of dealing with the massive amount of model weight parameters impacted by the inherent device variations within non-volatile computing-in-memory (NVCIM) accelerators. This issue significantly offsets their advantages by increasing training overhead, the time and energy needed for mapping weights to device states, and diminishing inference accuracy. To mitigate these challenges, we propose the "Tiny Shared Block (TSB)" method, which integrates a small shared 1x1 convolution block into the DNN architecture. This block is designed to stabilize feature processing across the network, effectively reducing the impact of device variation. Extensive experimental results show that TSB achieves over 20x inference accuracy gap improvement, over 5x training speedup, and weights-to-device mapping cost reduction while requiring less than 0.4% of the original weights to be write-verified during programming, when compared with state-of-the-art baseline solutions. Our approach provides a practical and efficient solution for deploying robust DNN models on NVCIM accelerators, making it a valuable contribution to the field of energy-efficient AI hardware.
SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
Authors: Zhanglu Yan, Zhenyu Bai, Tulika Mitra, Weng-Fai Wong
Date: 2024-05-06 00:00:00
Tasks: ECG Classification, Edge-computing, Heartbeat Classification

Heart disease is one of the leading causes of death worldwide. Given its high risk and often asymptomatic nature, real-time continuous monitoring is essential. Unlike traditional artificial neural networks (ANNs), spiking neural networks (SNNs) are well-known for their energy efficiency, making them ideal for wearable devices and energy-constrained edge computing platforms. However, current energy measurement of SNN implementations for detecting heart diseases typically rely on empirical values, often overlooking hardware overhead. Additionally, the integer and fire activations in SNNs require multiple memory accesses and repeated computations, which can further compromise energy efficiency. In this paper, we propose sparrowSNN, a redesign of the standard SNN workflow from a hardware perspective, and present a dedicated ASIC design for SNNs, optimized for ultra-low power wearable devices used in heartbeat classification. Using the MIT-BIH dataset, our SNN achieves a state-of-the-art accuracy of 98.29% for SNNs, with energy consumption of 31.39nJ per inference and power usage of 6.1uW, making sparrowSNN the highest accuracy with the lowest energy use among comparable systems. We also compare the energy-to-accuracy trade-offs between SNNs and quantized ANNs, offering recommendations on insights on how best to use SNNs.