pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing the spectral efficiency and energy efficiency of underwater channel communication by optimal cooperative spectrum sensing using hybrid optimization
Authors: Sanapala Umamaheshwararao, Muvvala Naga Venkata Siva Santosh Kumar, Madhu Ramarakula
Date: 2020-03-22 00:00:00
Proceeding: Int J Commun Syst. 2020 3

Underwater wireless sensor networks (UWSNs) contain quite a lot of
components such as vehicles and sensors that are deployed in a specific
acoustic area to perform collaborative monitoring and data collection errands.
These networks are adopted interactively between diverse nodes and groundbased
stations. Currently, UWSNs face problems and challenges that pertain to
limited bandwidth, media access control, high propagation delay, 3D topology,
spectrum sensing, resource utilization, routing, and power constraints. This
proposal deals with the intelligent spectrum sensing in underwater cognitive
sonar communication networks (CSCN). Here, the improved performance of
spectrum sensing in underwater communication is attained by optimizing the
cooperative spectrum sensing and data transmission. The parameters of system
like subchannel allocation and transmission power is optimized by a new
hybrid meta-heuristic algorithm by integrating the concepts of deer hunting
optimization algorithm (DHOA) and lion algorithm (LA) termed as lionenabled
DHOA (L-DHOA). The main intention of optimizing these
parameters is to maximize the spectrum efficiency (SE) and energy efficiency
(EE) of the underwater channel communication system. From the analysis,
with respect to convergence rate, minimum detection probability, and local
sensing time, it is proved that the novel hybrid optimization algorithm keeps a
great role in making the trade-off between the SE and EE in underwater
channel modeling
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
Authors: Gyudong Kim, Mehdi Ghasemi, Soroush Heidari, Seungryong Kim, Young Geun Kim, Sarma Vrudhula, Carole-Jean Wu
Date: 2024-03-07 00:00:00
Tasks: Domain Generalization, Fairness, Federated Learning

Federated Learning (FL) is a practical approach to train deep learning models collaboratively across user-end devices, protecting user privacy by retaining raw data on-device. In FL, participating user-end devices are highly fragmented in terms of hardware and software configurations. Such fragmentation introduces a new type of data heterogeneity in FL, namely \textit{system-induced data heterogeneity}, as each device generates distinct data depending on its hardware and software configurations. In this paper, we first characterize the impact of system-induced data heterogeneity on FL model performance. We collect a dataset using heterogeneous devices with variations across vendors and performance tiers. By using this dataset, we demonstrate that \textit{system-induced data heterogeneity} negatively impacts accuracy, and deteriorates fairness and domain generalization problems in FL. To address these challenges, we propose HeteroSwitch, which adaptively adopts generalization techniques (i.e., ISP transformation and SWAD) depending on the level of bias caused by varying HW and SW configurations. In our evaluation with a realistic FL dataset (FLAIR), HeteroSwitch reduces the variance of averaged precision by 6.3\% across device types.
Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy
Authors: SeongKu Kang, Shivam Agarwal, Bowen Jin, Dongha Lee, Hwanjo Yu, Jiawei Han
Date: 2024-03-07 00:00:00
Tasks: Retrieval

Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific applications for specialized areas or industries, due to unique terminologies, incomplete contexts of user queries, and specialized search intents. To capture the theme-specific information and improve retrieval, we propose to use a corpus topical taxonomy, which outlines the latent topic structure of the corpus while reflecting user-interested aspects. We introduce ToTER (Topical Taxonomy Enhanced Retrieval) framework, which identifies the central topics of queries and documents with the guidance of the taxonomy, and exploits their topical relatedness to supplement missing contexts. As a plug-and-play framework, ToTER can be flexibly employed to enhance various PLM-based retrievers. Through extensive quantitative, ablative, and exploratory experiments on two real-world datasets, we ascertain the benefits of using topical taxonomy for retrieval in theme-specific applications and demonstrate the effectiveness of ToTER.
Table grape inflorescence detection and clamping point localisation based on channel pruned YOLO V7-TP
Authors: Wensheng Du，Zihang Jia，Shunshun Sui，Ping Liu
Date: 2023-10-05 00:00:00
Proceeding: ScienceDirect 2023 10

Inflorescence thinning is one of the most critical techniques for producing high-quality table grapes. Currently, manual thinning of grape inflorescences is still adopted in most vineyards, and it is hard to reach the demands of large-scale and mechanical planting, which easily misses the best season for thinning inﬂorescences. It is urgent to design a machine for inﬂorescence thinning to avoid repetitive labour. The thinning machine requires the detection of table grape inﬂorescences and the location of stem clamping point. A lightweight channel pruned YOLOV7-TP that can be deployed in the consumer-grade vision system of the thinning machine was proposed. First, grape inflorescences and stems with different numbers of clamping points were stacked as labels and fed into the YOLOV7-TP for training. Then an optimal model was obtained after studying the factors impacting the detection and localisation performance. Finally, channel pruning was applied to YOLOV7-TP to compress the model, and some factors affecting the results were analysed for balancing detection accuracy and speed. Experiments were conducted to evaluate the effectiveness of YOLOV7-TP and the optimal combination of hyperparameters was determined. Furthermore, when the sparsity rate was 0.00025 and the pruning rate was 0.4, the channel pruned YOLOV7-TP performed optimally. The model achieves 91.5% mAP0.5, 2.3M parameters, 8.7GFLOPS, and 29.4fps detection speed. The channel pruned YOLOV7-TP has demonstrated excellent detection accuracy and speed in detecting grape inflorescence and location stem clamping points simultaneously, which supports a powerful technology for the vision system of grape inﬂorescence thinning machine.
Controllable Generation with Text-to-Image Diffusion Models: A Survey
Authors: Pu Cao, Feng Zhou, Qing Song, Lu Yang
Date: 2024-03-07 00:00:00
Tasks: Denoising

In the rapidly advancing realm of visual generation, diffusion models have revolutionized the landscape, marking a significant shift in capabilities with their impressive text-guided generative functions. However, relying solely on text for conditioning these models does not fully cater to the varied and complex requirements of different applications and scenarios. Acknowledging this shortfall, a variety of studies aim to control pre-trained text-to-image (T2I) models to support novel conditions. In this survey, we undertake a thorough review of the literature on controllable generation with T2I diffusion models, covering both the theoretical foundations and practical advancements in this domain. Our review begins with a brief introduction to the basics of denoising diffusion probabilistic models (DDPMs) and widely used T2I diffusion models. We then reveal the controlling mechanisms of diffusion models, theoretically analyzing how novel conditions are introduced into the denoising process for conditional generation. Additionally, we offer a detailed overview of research in this area, organizing it into distinct categories from the condition perspective: generation with specific conditions, generation with multiple conditions, and universal controllable generation. For an exhaustive list of the controllable generation literature surveyed, please refer to our curated repository at \url{https://github.com/PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models}.
Unbiased Estimator for Distorted Conics in Camera Calibration
Authors: Chaehyeon Song, Jaeho Shin, Myung-Hwan Jeon, Jongwoo Lim, Ayoung Kim
Date: 2024-03-07 00:00:00
Proceeding: CVPR 2024 1
Tasks: Camera Calibration

In the literature, points and conics have been major features for camera geometric calibration. Although conics are more informative features than points, the loss of the conic property under distortion has critically limited the utility of conic features in camera calibration. Many existing approaches addressed conic-based calibration by ignoring distortion or introducing 3D spherical targets to circumvent this limitation. In this paper, we present a novel formulation for conic-based calibration using moments. Our derivation is based on the mathematical finding that the first moment can be estimated without bias even under distortion. This allows us to track moment changes during projection and distortion, ensuring the preservation of the first moment of the distorted conic. With an unbiased estimator, the circular patterns can be accurately detected at the sub-pixel level and can now be fully exploited for an entire calibration pipeline, resulting in significantly improved calibration. The entire code is readily available from https://github.com/ChaehyeonSong/discocal.
Delving into the Trajectory Long-tail Distribution for Muti-object Tracking
Authors: Sijia Chen, En Yu, Jinyang Li, Wenbing Tao
Date: 2024-03-07 00:00:00
Proceeding: CVPR 2024 1
Tasks: Data Augmentation, Multi-Object Tracking, Multiple Object Tracking, Object Tracking, Trajectory Long-tail Distribution for Muti-object Tracking

Multiple Object Tracking (MOT) is a critical area within computer vision, with a broad spectrum of practical implementations. Current research has primarily focused on the development of tracking algorithms and enhancement of post-processing techniques. Yet, there has been a lack of thorough examination concerning the nature of tracking data it self. In this study, we pioneer an exploration into the distribution patterns of tracking data and identify a pronounced long-tail distribution issue within existing MOT datasets. We note a significant imbalance in the distribution of trajectory lengths across different pedestrians, a phenomenon we refer to as ``pedestrians trajectory long-tail distribution''. Addressing this challenge, we introduce a bespoke strategy designed to mitigate the effects of this skewed distribution. Specifically, we propose two data augmentation strategies, including Stationary Camera View Data Augmentation (SVA) and Dynamic Camera View Data Augmentation (DVA) , designed for viewpoint states and the Group Softmax (GS) module for Re-ID. SVA is to backtrack and predict the pedestrian trajectory of tail classes, and DVA is to use diffusion model to change the background of the scene. GS divides the pedestrians into unrelated groups and performs softmax operation on each group individually. Our proposed strategies can be integrated into numerous existing tracking systems, and extensive experimentation validates the efficacy of our method in reducing the influence of long-tail distribution on multi-object tracking performance. The code is available at https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT.
RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for Efficient Optical Flow Estimation
Authors: Henrique Morimitsu, Xiaobin Zhu, Roberto M. Cesar-Jr., Xiangyang Ji, Xu-Cheng Yin
Date: 2024-05-01 00:00:00
Proceeding: IEEE International Conference on Robotics and Automation (ICRA) 2024 5
Tasks: Optical Flow Estimation

Extracting motion information from videos with optical flow estimation is vital in multiple practical robot applications. Current optical flow approaches show remarkable accuracy, but top-performing methods have high computational costs and are unsuitable for embedded devices. Although some previous works have focused on developing low-cost optical flow strategies, their estimation quality has a noticeable gap with more robust methods. In this paper, we develop a novel method to efficiently estimate high-quality optical flow in embedded devices. Our proposed RAPIDFlow model combines efficient NeXt1D convolution blocks with a fully recurrent structure based on feature pyramids to decrease computational costs without significantly impacting estimation accuracy. The adaptable recurrent encoder produces multi-scale features with a single shared block, which allows us to adjust the pyramid length at inference time and make it more robust to changes in input size. Also, it enables our model to offer multiple tradeoffs between accuracy and speed to suit different applications. Experiments using a Jetson Orin NX embedded system on the MPI-Sintel and KITTI public benchmarks show that RAPIDFlow outperforms previous approaches by significant margins at faster speeds.
Do Large Language Model Understand Multi-Intent Spoken Language ?
Authors: Shangjian Yin, Peijie Huang, Yuhong Xu, Haojing Huang, Jiatian Chen
Date: 2024-03-07 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Spoken Language Understanding

This research signifies a considerable breakthrough in leveraging Large Language Models (LLMs) for multi-intent spoken language understanding (SLU). Our approach re-imagines the use of entity slots in multi-intent SLU applications, making the most of the generative potential of LLMs within the SLU landscape, leading to the development of the EN-LLM series. Furthermore, we introduce the concept of Sub-Intent Instruction (SII) to amplify the analysis and interpretation of complex, multi-intent communications, which further supports the creation of the ENSI-LLM models series. Our novel datasets, identified as LM-MixATIS and LM-MixSNIPS, are synthesized from existing benchmarks. The study evidences that LLMs may match or even surpass the performance of the current best multi-intent SLU models. We also scrutinize the performance of LLMs across a spectrum of intent configurations and dataset distributions. On top of this, we present two revolutionary metrics - Entity Slot Accuracy (ESA) and Combined Semantic Accuracy (CSA) - to facilitate a detailed assessment of LLM competence in this multifaceted field." Our code and datasets are available at \url{https://github.com/SJY8460/SLM}.
iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries
Authors: Adam Coscia, Langdon Holmes, Wesley Morris, Joon Suh Choi, Scott Crossley, Alex Endert
Date: 2024-03-07 00:00:00

The recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deploying them in critical learning environments, yet their unprecedented size and expanding number of parameters inhibits transparency and impedes trust when they underperform. Through a collaborative user-centered design process with several learning engineers building and deploying summary scoring LLMs, we characterized fundamental design challenges and goals around interpreting their models, including aggregating large text inputs, tracking score provenance, and scaling LLM interpretability methods. To address their concerns, we developed iScore, an interactive visual analytics tool for learning engineers to upload, score, and compare multiple summaries simultaneously. Tightly integrated views allow users to iteratively revise the language in summaries, track changes in the resulting LLM scores, and visualize model weights at multiple levels of abstraction. To validate our approach, we deployed iScore with three learning engineers over the course of a month. We present a case study where interacting with iScore led a learning engineer to improve their LLM's score accuracy by three percentage points. Finally, we conducted qualitative interviews with the learning engineers that revealed how iScore enabled them to understand, evaluate, and build trust in their LLMs during deployment.
KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts
Authors: Adam Coscia, Alex Endert
Date: 2024-03-07 00:00:00

Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVis, a human-in-the-loop visual analytics system for interpreting language models using fill-in-the-blank sentences as prompts. By comparing predictions between sentences, KnowledgeVis reveals learned associations that intuitively connect what language models learn during training to natural language tasks downstream, helping users create and test multiple prompt variations, analyze predicted words using a novel semantic clustering technique, and discover insights using interactive visualizations. Collectively, these visualizations help users identify the likelihood and uniqueness of individual predictions, compare sets of predictions between prompts, and summarize patterns and relationships between predictions across all prompts. We demonstrate the capabilities of KnowledgeVis with feedback from six NLP experts as well as three different use cases: (1) probing biomedical knowledge in two domain-adapted models; and (2) evaluating harmful identity stereotypes and (3) discovering facts and relationships between three general-purpose models.
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Authors: Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak, Johannes Brandstetter, Günter Klambauer, Andreas Mayr
Date: 2024-03-07 00:00:00
Tasks: Drug Discovery

Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling. The expressivity of GNNs with respect to their ability to discriminate non-isomorphic graphs critically depends on the functions employed for message aggregation and graph-level readout. By applying signal propagation theory, we propose a variance-preserving aggregation function (VPA) that maintains expressivity, but yields improved forward and backward dynamics. Experiments demonstrate that VPA leads to increased predictive performance for popular GNN architectures as well as improved learning dynamics. Our results could pave the way towards normalizer-free or self-normalizing GNNs.
LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
Authors: Boshi Wang, Hao Fang, Jason Eisner, Benjamin Van Durme, Yu Su
Date: 2024-03-07 00:00:00
Tasks: Continual Learning, In-Context Learning

Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments. Existing work on tool-augmented LLMs primarily focuses on the broad coverage of tools and the flexibility of adding new tools. However, a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses tools for which it has been trained. We find that existing LLMs, including GPT-4 and open-source LLMs specifically fine-tuned for tool use, only reach a correctness rate in the range of 30% to 60%, far from reliable use in practice. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE), that orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory. Specifically, STE leverages an LLM's 'imagination' to simulate plausible scenarios for using a tool, after which the LLM interacts with the tool to learn from its execution feedback. Both short-term and long-term memory are employed to improve the depth and breadth of the exploration, respectively. Comprehensive experiments on ToolBench show that STE substantially improves tool learning for LLMs under both in-context learning and fine-tuning settings, bringing a boost of 46.7% to Mistral-Instruct-7B and enabling it to outperform GPT-4. We also show effective continual learning of tools via a simple experience replay strategy.
How Far Are We from Intelligent Visual Deductive Reasoning?
Authors: Yizhe Zhang, He Bai, Ruixiang Zhang, Jiatao Gu, Shuangfei Zhai, Josh Susskind, Navdeep Jaitly
Date: 2024-03-07 00:00:00
Tasks: In-Context Learning, Visual Reasoning

Vision-Language Models (VLMs) have recently demonstrated incredible strides on diverse vision language tasks. We dig into vision-based deductive reasoning, a more sophisticated but less explored realm, and find previously unexposed blindspots in the current SOTA VLMs. Specifically, we leverage Raven's Progressive Matrices (RPMs), to assess VLMs' abilities to perform multi-hop relational and deductive reasoning relying solely on visual clues. We perform comprehensive evaluations of several popular VLMs employing standard strategies such as in-context learning, self-consistency, and Chain-of-thoughts (CoT) on three diverse datasets, including the Mensa IQ test, IntelligenceTest, and RAVEN. The results reveal that despite the impressive capabilities of LLMs in text-based reasoning, we are still far from achieving comparable proficiency in visual deductive reasoning. We found that certain standard strategies that are effective when applied to LLMs do not seamlessly translate to the challenges presented by visual reasoning tasks. A detailed analysis reveals that VLMs struggle to solve these tasks mainly because they are unable to perceive and comprehend multiple, confounding abstract patterns in RPM examples.
Common 7B Language Models Already Possess Strong Math Capabilities
Authors: Chen Li, Weiqi Wang, Jingcheng Hu, Yixuan Wei, Nanning Zheng, Han Hu, Zheng Zhang, Houwen Peng
Date: 2024-03-07 00:00:00
Tasks: GSM8K, Math

Mathematical capabilities were previously believed to emerge in common language models only at a very large scale or require extensive math-related pre-training. This paper shows that the LLaMA-2 7B model with common pre-training already exhibits strong mathematical abilities, as evidenced by its impressive accuracy of 97.7% and 72.0% on the GSM8K and MATH benchmarks, respectively, when selecting the best response from 256 random generations. The primary issue with the current base model is the difficulty in consistently eliciting its inherent mathematical capabilities. Notably, the accuracy for the first answer drops to 49.5% and 7.9% on the GSM8K and MATH benchmarks, respectively. We find that simply scaling up the SFT data can significantly enhance the reliability of generating correct answers. However, the potential for extensive scaling is constrained by the scarcity of publicly available math questions. To overcome this limitation, we employ synthetic data, which proves to be nearly as effective as real data and shows no clear saturation when scaled up to approximately one million samples. This straightforward approach achieves an accuracy of 82.6% on GSM8K and 40.6% on MATH using LLaMA-2 7B models, surpassing previous models by 14.2% and 20.8%, respectively. We also provide insights into scaling behaviors across different reasoning complexities and error types.