pwc-archive/papers-with-abstracts huggingface dataset:
KisanQRS: A Deep Learning-based Automated Query-Response System for Agricultural Decision-Making
Authors: Mohammad Zia Ur Rehman, Devraj Raghuvanshi, Nagendra Kumar
Date: 2024-10-26 00:00:00
Tasks: Clustering, Decision Making, Retrieval

Delivering prompt information and guidance to farmers is critical in agricultural decision-making. Farmers helpline centres are heavily reliant on the expertise and availability of call centre agents, leading to inconsistent quality and delayed responses. To this end, this article presents Kisan Query Response System (KisanQRS), a Deep Learning-based robust query-response framework for the agriculture sector. KisanQRS integrates semantic and lexical similarities of farmers queries and employs a rapid threshold-based clustering method. The clustering algorithm is based on a linear search technique to iterate through all queries and organize them into clusters according to their similarity. For query mapping, LSTM is found to be the optimal method. Our proposed answer retrieval method clusters candidate answers for a crop, ranks these answer clusters based on the number of answers in a cluster, and selects the leader of each cluster. The dataset used in our analysis consists of a subset of 34 million call logs from the Kisan Call Centre (KCC), operated under the Government of India. We evaluated the performance of the query mapping module on the data of five major states of India with 3,00,000 samples and the quantifiable outcomes demonstrate that KisanQRS significantly outperforms traditional techniques by achieving 96.58% top F1-score for a state. The answer retrieval module is evaluated on 10,000 samples and it achieves a competitive NDCG score of 96.20%. KisanQRS is useful in enabling farmers to make informed decisions about their farming practices by providing quick and pertinent responses to their queries.
Calibrated Decision-Making through LLM-Assisted Retrieval
Authors: Chaeyun Jang, Hyungi Lee, Seanie Lee, Juho Lee
Date: 2024-10-28 00:00:00
Tasks: Decision Making, RAG, Retrieval, Retrieval-augmented Generation

Recently, large language models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confidently provide incorrect information, it can lead humans to make suboptimal decisions. To prevent LLMs from generating incorrect information on topics they are unsure of and to improve the accuracy of generated content, prior works have proposed Retrieval Augmented Generation (RAG), where external documents are referenced to generate responses. However, traditional RAG methods focus only on retrieving documents most relevant to the input query, without specifically aiming to ensure that the human user's decisions are well-calibrated. To address this limitation, we propose a novel retrieval method called Calibrated Retrieval-Augmented Generation (CalibRAG), which ensures that decisions informed by the retrieved documents are well-calibrated. Then we empirically validate that CalibRAG improves calibration performance as well as accuracy, compared to other baselines across various datasets.
Comment on Is Complexity an Illusion?
Authors: Gabriel Simmons
Date: 2024-10-29 00:00:00
Tasks: Multi-class Classification

The paper "Is Complexity an Illusion?" (Bennett, 2024) provides a formalism for complexity, learning, inference, and generalization, and introduces a formal definition for a "policy". This reply shows that correct policies do not exist for a simple task of supervised multi-class classification, via mathematical proof and exhaustive search. Implications of this result are discussed, as well as possible responses and amendments to the theory.
Deep Learning-Based CKM Construction with Image Super-Resolution
Authors: Shiyu Wang, Xiaoli Xu, Yong Zeng
Date: 2024-10-28 00:00:00
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution

Channel knowledge map (CKM) is a novel technique for achieving environment awareness, and thereby improving the communication and sensing performance for wireless systems. A fundamental problem associated with CKM is how to construct a complete CKM that provides channel knowledge for a large number of locations based solely on sparse data measurements. This problem bears similarities to the super-resolution (SR) problem in image processing. In this letter, we propose an effective deep learning-based CKM construction method that leverages the image SR network known as SRResNet. Unlike most existing studies, our approach does not require any additional input beyond the sparsely measured data. In addition to the conventional path loss map construction, our approach can also be applied to construct channel angle maps (CAMs), thanks to the use of a new dataset called CKMImageNet. The numerical results demonstrate that our method outperforms interpolation-based methods such as nearest neighbour and bicubic interpolation, as well as the SRGAN method in CKM construction. Furthermore, only 1/16 of the locations need to be measured in order to achieve a root mean square error (RMSE) of 1.1 dB in path loss.
Advancing Bayesian Optimization via Learning Correlated Latent Space
Authors: Seunghun Lee, Jaewon Chu, Sihyeon Kim, Juyeon Ko, Hyunwoo J. Kim
Date: 2023-10-31 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Bayesian Optimization

Bayesian optimization is a powerful method for optimizing black-box functions with limited function evaluations. Recent works have shown that optimization in a latent space through deep generative models such as variational autoencoders leads to effective and efficient Bayesian optimization for structured or discrete data. However, as the optimization does not take place in the input space, it leads to an inherent gap that results in potentially suboptimal solutions. To alleviate the discrepancy, we propose Correlated latent space Bayesian Optimization (CoBO), which focuses on learning correlated latent spaces characterized by a strong correlation between the distances in the latent space and the distances within the objective function. Specifically, our method introduces Lipschitz regularization, loss weighting, and trust region recoordination to minimize the inherent gap around the promising areas. We demonstrate the effectiveness of our approach on several optimization tasks in discrete data, such as molecule design and arithmetic expression fitting, and achieve high performance within a small budget.
ITER: Iterative Transformer-based Entity Recognition and Relation Extraction
Authors: Moritz Hennen, Florian Babl, Michaela Geierhos
Date: 2024-11-11 00:00:00
Proceeding: EMNLP 2024 11
Tasks: GPU, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Relation, Relation Extraction

When extracting structured information from text, recognizing entities and extracting relationships are essential. Recent advances in both tasks generate a structured representation of the information in an autoregressive manner, a time-consuming and computationally expensive approach. This naturally raises the question of whether autoregressive methods are necessary in order to achieve comparable results. In this work, we propose ITER, an efficient encoder-based relation extraction model, that performs the task in three parallelizable steps, greatly accelerating a recent language modeling approach: ITER achieves an inference throughput of over 600 samples per second for a large model on a single consumer-grade GPU. Furthermore, we achieve state-of-the-art results on the relation extraction datasets ADE and ACE05, and demonstrate competitive performance for both named entity recognition with GENIA and CoNLL03, and for relation extraction with SciERC and CoNLL04.
A Survey of Emerging Approaches and Advances in Video Generation
Authors: Elnaz Soleimani, Ghazaleh Khodabandelou
Date: 2024-11-09 00:00:00
Proceeding: HAL Preprint 2024 11
Tasks: Image to Video Generation, Language Modeling, Language Modelling, Large Language Model, Motion Generation, multimodal generation, Survey, Text-to-Video Generation, Video Generation

The field of AI-driven video generation is evolving rapidly, with remarkable advancements achieved over the past two years. These developments have markedly enhanced the ability to transform human imagination into realistic visual content across various domains. This survey provides a comprehensive review of contemporary research in video generation, delving into foundational principles -including diverse generation strategies and key generative frameworks- as well as state-of-the-art models encompassing video synthesis, editing, and enhancement techniques. We present a comparative analysis of critical components such as core technologies, video quality attributes, hardware prerequisites, openness, and essential evaluation metrics and datasets. The survey concludes by discussing open challenges and emerging directions in the field, such as the role of LLMs (Large Language Model) and VLMs (Vision-Language Model) in advancing the sophistication of video generation frameworks. We intend for this survey to serve as a useful resource for researchers and practitioners, offering a structured overview of recent advancements and a clear depiction of the current landscape in video generation research.
Learning Discrete World Models for Heuristic Search
Authors: Forest Agostinelli, Misagh Soltani
Date: 2024-09-14 00:00:00
Proceeding: Reinforcement Learning Journal 2024 9
Tasks: Deep Reinforcement Learning, Heuristic Search, Model-based Reinforcement Learning, Reinforcement Learning, Rubik's Cube, Sequential Decision Making, Sokoban, Trajectory Planning

For many sequential decision making problems, planning is often necessary to find solutions. However, for domains such as those encountered in robotics, the transition function, also known as the world model, is often unknown. While model-based reinforcement learning methods learn world models that can then be used for planning, such approaches are limited by errors that accumulate when the model is applied across many timesteps as well as the inability to re-identify states during planning. To solve these problems, we introduce DeepCubeAI, an algorithm that learns a world model that represents states in a discrete latent space, uses reinforcement learning to learn a heuristic function that generalizes over start and goal states using this learned model, and combines the learned model and learned heuristic function with heuristic search to solve problems. Since the latent space is discrete, we can prevent the accumulation of small errors by rounding and we can re-identify states by simply comparing two binary vectors. In our experiments on a pixel representation of the Rubik’s cube, Sokoban, IceSlider, and DigitJump, we find that DeepCubeAI is able to apply the model for thousands of steps without accumulating any error. Furthermore, DeepCubeAI solves over 99% of test instances in all domains, generalizes across goal states, and significantly outperforms a greedy policy that does not plan with the learned world model.
Content-Aware Preserving Image Generation
Authors: Giang H. Le, Anh Q. Nguyen, Byeongkeun Kang, Yeejin Lee
Date: 2024-11-15 00:00:00
Tasks: Image Generation, Scene Understanding

Remarkable progress has been achieved in image generation with the introduction of generative models. However, precisely controlling the content in generated images remains a challenging task due to their fundamental training objective. This paper addresses this challenge by proposing a novel image generation framework explicitly designed to incorporate desired content in output images. The framework utilizes advanced encoding techniques, integrating subnetworks called content fusion and frequency encoding modules. The frequency encoding module first captures features and structures of reference images by exclusively focusing on selected frequency components. Subsequently, the content fusion module generates a content-guiding vector that encapsulates desired content features. During the image generation process, content-guiding vectors from real images are fused with projected noise vectors. This ensures the production of generated images that not only maintain consistent content from guiding images but also exhibit diverse stylistic variations. To validate the effectiveness of the proposed framework in preserving content attributes, extensive experiments are conducted on widely used benchmark datasets, including Flickr-Faces-High Quality, Animal Faces High Quality, and Large-scale Scene Understanding datasets.
Asymmetric Mutualism in Two- and Three-Dimensional Range Expansions
Authors: Maxim O. Lavrentovich, David R. Nelson
Date: 2014-04-01 00:00:00
Proceeding: APS 2014 4

Genetic drift at the frontiers of two-dimensional range expansions of microorganisms can frustrate
local cooperation between different genetic variants, demixing the population into distinct sectors.
In a biological context, mutualistic or antagonistic interactions will typically be asymmetric between
variants. By taking into account both the asymmetry and the interaction strength, we show that
the much weaker demixing in three dimensions allows for a mutualistic phase over a much wider
range of asymmetric cooperative benefits, with mutualism prevailing for any positive, symmetric
benefit. We also demonstrate that expansions with undulating fronts roughen dramatically at the
boundaries of the mutualistic phase, with severe consequences for the population genetics along the
transition lines.
EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation
Authors: Rang Meng, Xingyu Zhang, Yuming Li, Chenguang Ma
Date: 2024-11-15 00:00:00
Proceeding: CVPR 2025 1
Tasks: Audio-Driven Body Animation, Human Animation, Image to Video Generation, Subject-driven Video Generation, Video Generation

Recent work on human animation usually involves audio, pose, or movement maps conditions, thereby achieves vivid animation quality. However, these methods often face practical challenges due to extra control conditions, cumbersome condition injection modules, or limitation to head region driving. Hence, we ask if it is possible to achieve striking half-body human animation while simplifying unnecessary conditions. To this end, we propose a half-body human animation method, dubbed EchoMimicV2, that leverages a novel Audio-Pose Dynamic Harmonization strategy, including Pose Sampling and Audio Diffusion, to enhance half-body details, facial and gestural expressiveness, and meanwhile reduce conditions redundancy. To compensate for the scarcity of half-body data, we utilize Head Partial Attention to seamlessly accommodate headshot data into our training framework, which can be omitted during inference, providing a free lunch for animation. Furthermore, we design the Phase-specific Denoising Loss to guide motion, detail, and low-level quality for animation in specific phases, respectively. Besides, we also present a novel benchmark for evaluating the effectiveness of half-body human animation. Extensive experiments and analyses demonstrate that EchoMimicV2 surpasses existing methods in both quantitative and qualitative evaluations.
Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions
Authors: Tianhao Ma, Han Chen, Juncheng Hu, Yungang Zhu, Ximing Li
Date: 2024-11-15 00:00:00
Proceeding: CVPR 2025 1
Tasks: Weakly-supervised Learning

Learning from label proportions (LLP), i.e., a challenging weakly-supervised learning task, aims to train a classifier by using bags of instances and the proportions of classes within bags, rather than annotated labels for each instance. Beyond the traditional bag-level loss, the mainstream methodology of LLP is to incorporate an auxiliary instance-level loss with pseudo-labels formed by predictions. Unfortunately, we empirically observed that the pseudo-labels are are often inaccurate due to over-smoothing, especially for the scenarios with large bag sizes, hurting the classifier induction. To alleviate this problem, we suggest a novel LLP method, namely Learning from Label Proportions with Auxiliary High-confident Instance-level Loss (L^2P-AHIL). Specifically, we propose a dual entropy-based weight (DEW) method to adaptively measure the confidences of pseudo-labels. It simultaneously emphasizes accurate predictions at the bag level and avoids overly smoothed predictions. We then form high-confident instance-level loss with DEW, and jointly optimize it with the bag-level loss in a self-training manner. The experimental results on benchmark datasets show that L^2P-AHIL can surpass the existing baseline methods, and the performance gain can be more significant as the bag size increases.
Federated Domain Generalization via Prompt Learning and Aggregation
Authors: Shuai Gong, Chaoran Cui, Chunyun Zhang, Wenna Wang, Xiushan Nie, Lei Zhu
Date: 2024-11-15 00:00:00
Tasks: Domain Generalization, Privacy Preserving, Prompt Learning, Transfer Learning

Federated domain generalization (FedDG) aims to improve the global model generalization in unseen domains by addressing data heterogeneity under privacy-preserving constraints. A common strategy in existing FedDG studies involves sharing domain-specific knowledge among clients, such as spectrum information, class prototypes, and data styles. However, this knowledge is extracted directly from local client samples, and sharing such sensitive information poses a potential risk of data leakage, which might not fully meet the requirements of FedDG. In this paper, we introduce prompt learning to adapt pre-trained vision-language models (VLMs) in the FedDG scenario, and leverage locally learned prompts as a more secure bridge to facilitate knowledge transfer among clients. Specifically, we propose a novel FedDG framework through Prompt Learning and AggregatioN (PLAN), which comprises two training stages to collaboratively generate local prompts and global prompts at each federated round. First, each client performs both text and visual prompt learning using their own data, with local prompts indirectly synchronized by regarding the global prompts as a common reference. Second, all domain-specific local prompts are exchanged among clients and selectively aggregated into the global prompts using lightweight attention-based aggregators. The global prompts are finally applied to adapt VLMs to unseen target domains. As our PLAN framework requires training only a limited number of prompts and lightweight aggregators, it offers notable advantages in computational and communication efficiency for FedDG. Extensive experiments demonstrate the superior generalization ability of PLAN across four benchmark datasets.
PoLYTC: a novel BERT-based classifier to detect political leaning of YouTube videos based on their titles
Authors: Nouar AlDahoul, Talal Rahwan, Yasir Zaki
Date: 2024-06-05 00:00:00
Proceeding: Journal of Big Data 2024 6
Tasks: Large Language Model

Over two-thirds of the U.S. population uses YouTube, and a quarter of U.S. adults regularly receive their news from it. Despite the massive political content available on the platform, to date, no classifier has been proposed to classify the political leaning of YouTube videos. The only exception is a classifier that requires extensive information about each video (rather than just the title) and classifies the videos into just three classes (rather than the widely-used categorization into six classes). To fill this gap, “PoLYTC” (Political Leaning YouTube Classifier) is proposed to classify YouTube videos based on their titles into six political classes. PoLYTC utilizes a large language model, namely BERT, and is fine-tuned on a public dataset of 11.5 million YouTube videos. Experiments reveal that the proposed solution achieves high accuracy (75%) and high F1-score (77%), thereby outperforming the state of the art. To further validate the solution’s classification performance, several videos were collected from numerous prominent news agencies’ YouTube channels, such as Fox News and The New York Times, which have widely known political leanings. These videos were classified based on their titles, and the results have shown that, in the vast majority of cases, the predicted political leaning matches that of the news agency. PoLYTC can help YouTube users make informed decisions about which videos to watch and can help researchers analyze the political content on YouTube.
A Polarization Image Dehazing Method Based on the Principle of Physical Diffusion
Authors: Zhenjun Zhang, Lijun Tang, Hongjin Wang, Lilian Zhang, Yunze He, Yaonan Wang
Date: 2024-11-15 00:00:00
Tasks: Image Dehazing

Computer vision is increasingly used in areas such as unmanned vehicles, surveillance systems and remote sensing. However, in foggy scenarios, image degradation leads to loss of target details, which seriously affects the accuracy and effectiveness of these vision tasks. Polarized light, due to the fact that its electromagnetic waves vibrate in a specific direction, is able to resist scattering and refraction effects in complex media more effectively compared to unpolarized light. As a result, polarized light has a greater ability to maintain its polarization characteristics in complex transmission media and under long-distance imaging conditions. This property makes polarized imaging especially suitable for complex scenes such as outdoor and underwater, especially in foggy environments, where higher quality images can be obtained. Based on this advantage, we propose an innovative semi-physical polarization dehazing method that does not rely on an external light source. The method simulates the diffusion process of fog and designs a diffusion kernel that corresponds to the image blurriness caused by this diffusion. By employing spatiotemporal Fourier transforms and deconvolution operations, the method recovers the state of fog droplets prior to diffusion and the light inversion distribution of objects. This approach effectively achieves dehazing and detail enhancement of the scene.
VeriGraph: Scene Graphs for Execution Verifiable Robot Planning
Authors: Daniel Ekpo, Mara Levy, Saksham Suri, Chuong Huynh, Abhinav Shrivastava
Date: 2024-11-15 00:00:00
Tasks: Robot Task Planning, Task Planning

Recent advancements in vision-language models (VLMs) offer potential for robot task planning, but challenges remain due to VLMs' tendency to generate incorrect action sequences. To address these limitations, we propose VeriGraph, a novel framework that integrates VLMs for robotic planning while verifying action feasibility. VeriGraph employs scene graphs as an intermediate representation, capturing key objects and spatial relationships to improve plan verification and refinement. The system generates a scene graph from input images and uses it to iteratively check and correct action sequences generated by an LLM-based task planner, ensuring constraints are respected and actions are executable. Our approach significantly enhances task completion rates across diverse manipulation scenarios, outperforming baseline methods by 58% for language-based tasks and 30% for image-based tasks.