pwc-archive/papers-with-abstracts huggingface dataset:
Context-Aware Feature Learning for Noise Robust Person Search
Authors: Cairong Zhao, Zhicheng Chen, Shuguang Dou, Zefan Qu, Jiawei Yao, Jun Wu, Duoqian Miao
Date: 2022-05-30 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2022 5
Tasks: Person Search

Person search aims to localize and identify specific pedestrians from numerous surveillance scene images. In this work, we focus on the noise in person search. We categorize the noise into scene-inherent noise and human-introduced noise. Scene-inherent noise comes from congestion, occlusion, and illumination changes. Human-introduced noise originates from the labeling process. For scene-inherent noise, we propose a novel context contrastive loss to take advantage of the latent contextual information from scene images. Features from context regions are utilized to construct contrastive pairs to constrain the feature discrimination among pedestrians in scene images while maintaining the feature consistency of the same identity. The network can thus learn to distinguish congested and overlapped pedestrians and more robust features can be obtained. For human-introduced noise, we propose a noise-discovery and noise-suppression training process for mislabeling robust person search. After the first training pass, the relation between feature prototypes of different identities is analyzed and the mislabeled pedestrians are discovered. During the second training pass, the label noise is suppressed to reduce the negative influence of mislabeled data. Experiments show that the proposed context-aware noise-robust (CANR) person search can achieve competitive performance. Further ablation studies confirm the effectiveness of CANR.
Assessment of the Impact of Solid Waste on Groundwater Quality in Ikole Town, Ekiti State, Nigeria: A Case Study of Otunja and Asin Dumpsites
Authors: Ojo Benson, Tolulope Adewunmi
Date: 2023-11-30 00:00:00
Proceeding: International Journal of Scientific Research in Multidisciplinary Studies 2023 11
Tasks: Management

This study aimed to assess the impact of solid waste on the physical, chemical, and biological characteristics of underground water in some areas of Ikole town in Ekiti State, Nigeria. The study was conducted by collecting groundwater samples from Otunja and Asin, which were contaminated by domestic wastewater. The samples were analysed using conventional tests, infrared spectrophotometer (IR), and atomic absorption spectrophotometer (AAS) equipment to measure pollutants concentration of groundwater. The study also compared the characteristics of a control well with the underground water at the dumping sites and evaluated the effect of the solid wastes on each well by comparing the quality to WHO standard. The results showed that the presence of solid waste in the dumping sites had a significant impact on the quality of groundwater, which was contaminated with pollutants such as turbidity, pH, alkalinity, biochemical oxygen demand, total solid, suspended solid, and microbial analysis. The study recommends the implementation of proper waste management practices to prevent groundwater contamination and protect human health.
Salience-Guided Iterative Asymmetric Mutual Hashing for Fast Person Re-identification
Authors: Cairong Zhao, Yuanpeng Tu, Zhihui Lai, Fumin Shen, Heng Tao Shen, Duoqian Miao
Date: 2021-09-08 00:00:00
Proceeding: IEEE Transactions on Image Processing 2021 9
Tasks: Code Generation, Person Re-Identification

Person Re-identification (ReID) aims to retrieve the pedestrian with the same identity across different views. Existing studies mainly focus on improving accuracy, while ignoring their efficiency. Recently, several hash based methods have been proposed. Despite their improvement in efficiency, there still exists an unacceptable gap in accuracy between these methods and real-valued ones. Besides, few attempts have been made to simultaneously explicitly reduce redundancy and improve discrimination of hash codes, especially for short ones. Integrating Mutual learning may be a possible solution to reach this goal. However, it fails to utilize the complementary effect of teacher and student models. Additionally, it will degrade the performance of teacher models by treating two models equally. To address these issues, we propose a salience-guided iterative asymmetric mutual hashing (SIAMH) to achieve high-quality hash code generation and fast feature extraction. Specifically, a salience-guided self-distillation branch (SSB) is proposed to enable SIAMH to generate hash codes based on salience regions, thus explicitly reducing the redundancy between codes. Moreover, a novel iterative asymmetric mutual training strategy (IAMT) is proposed to alleviate drawbacks of common mutual learning, which can continuously refine the discriminative regions for SSB and extract regularized dark knowledge for two models as well. Extensive experiment results on five widely used datasets demonstrate the superiority of the proposed method in efficiency and accuracy when compared with existing state-of-the-art hashing and real-valued approaches. The code is released at https://github.com/Vill-Lab/SIAMH
Improved Instance Discrimination and Feature Compactness for End-to-End Person Search
Authors: Shaowei Hou, Cairong Zhao, Zhicheng Chen, Jun Wu, Zhihua Wei, Duoqian Miao
Date: 2021-05-21 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2021 5
Tasks: Pedestrian Detection, Person Re-Identification, Person Search, Triplet

Person search aims to locate and retrieve specific pedestrians in scene images, including two subtasks, pedestrian detection and person re-identification. Recently, triplet loss has been widely used in person re-identification, which effectively improves the pedestrian features embedding and achieves superior performance. However, forming triplet in the person search is not an easy task. Most of the existing end-to-end person search methods are based on Faster R-CNN. The training process of person re-identification part is affected by the detector. It is difficult to form pedestrian triplets within a limited batch size. Also, there are many pedestrian identities in the person search dataset, but each pedestrian identity only has a few samples. It is difficult to learn a robust pedestrian feature representation for person search. To resolve the problem discussed above, a novel Feature Compactness (FC) Loss for the person search is designed, which efficiently improves the inter-class discrimination and intra-class compactness of pedestrian features embedding without the need for positive or negative pairs. Besides, we propose a pedestrian attention module (PAM) to help the network focuses more on pedestrian information and suppresses irrelevant background information. Our method achieves comparable performance on two benchmarks, CUHK-SYSU and PRW, and achieves 91.96% of mAP and 93.34% of rank1 accuracy on CUHK-SYSU.
Deep Fusion Feature Representation Learning with Hard Mining Center-Triplet Loss for Person Re-identification
Authors: Cairong Zhao, Xinbi Lv, Zhang Zhang, WangMeng Zuo, Jun Wu, Duoqian Miao
Date: 2020-02-06 00:00:00
Proceeding: IEEE Transactions on Multimedia 2020 2
Tasks: Person Re-Identification, Representation Learning, Triplet

Person re-identification (Re-ID) is a challenging task in the field of computer vision and focuses on matching people across images from different cameras. The extraction of robust feature representations from pedestrian images through CNNs with a single deterministic pooling operation is problematic as the features in real pedestrian images are complex and diverse. To address this problem, we propose a novel center-triplet (CT) model that combines the learning of robust feature representation and the optimization of metric loss function. Firstly, we design a fusion feature learning network (FFLN) with a novel fusion strategy consisting of max pooling and average pooling. Instead of adopting a single deterministic pooling operation, the FFLN combines two pooling operations that can learn high response values, bright features, and low response values, discriminative features simultaneously. Our model obtains more discriminative fusion features by adaptively learning the weights of the features learned by the corresponding pooling operations. In addition, we design a hard mining center-triplet loss (HCTL), a novel improved triplet loss, which effectively optimizes the intra/inter-class distance and reduces the cost of computing and mining hard training samples simultaneously, thereby enhancing the learning of robust feature representation. Finally, we proved our method can learn robust and discriminative feature representations for complex pedestrian images in real scenes. The experimental results also illustrate that our method achieves an 81.8% mAP and a 93.8% rank-1 accuracy on Market1501, a 68.2% mAP and an 83.3% rank-1 accuracy on DukeMTMC-ReID, and a 43.6% mAP and a 74.3% rank-1 accuracy on MSMT17, outperforming most state-of-the-art methods and achieving better performance for person re-identification.
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
Authors: Tung Nguyen, Sudhanshu Agrawal, Aditya Grover
Date: 2023-10-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Experimental Design, In-Context Learning

Experimental design is a fundamental problem in many science and engineering fields. In this problem, sample efficiency is crucial due to the time, money, and safety costs of real-world design evaluations. Existing approaches either rely on active data collection or access to large, labeled datasets of past experiments, making them impractical in many real-world scenarios. In this work, we address the more challenging yet realistic setting of few-shot experimental design, where only a few labeled data points of input designs and their corresponding values are available. We approach this problem as a conditional generation task, where a model conditions on a few labeled examples and the desired output to generate an optimal input design. To this end, we introduce Experiment Pretrained Transformers (ExPT), a foundation model for few-shot experimental design that employs a novel combination of synthetic pretraining with in-context learning. In ExPT, we only assume knowledge of a finite collection of unlabelled data points from the input domain and pretrain a transformer neural network to optimize diverse synthetic functions defined over this domain. Unsupervised pretraining allows ExPT to adapt to any design task at test time in an in-context fashion by conditioning on a few labeled data points from the target task and generating the candidate optima. We evaluate ExPT on few-shot experimental design in challenging domains and demonstrate its superior generality and performance compared to existing methods. The source code is available at https://github.com/tung-nd/ExPT.git.
Multiple Protein Profiler 1.0 (MPP): A webserver for predicting and visualizing physiochemical properties of proteins at the proteome level
Authors: Gustavo Sganzerla Martinez, Mansi Dutt, Anuj Kumar, David J Kelvin
Date: 2023-11-17 00:00:00

Determining the physicochemical properties of a protein can reveal important insights in their structure, biological functions, stability, and interactions with other molecules. Although tools for computing properties of proteins already existed, we could not find a comprehensive tool that enables the calculations of multiple properties for multiple input proteins on the proteome level at once. Facing this limitation, we have developed Multiple Protein Profiler (MPP) 1.0 as an integrated tool that allows the profiling of 12 individual properties of multiple proteins in a significant manner. MPP provides a tabular and graphic visualization of properties of multiple proteins. The tool is freely accessible at https://mproteinprofiler.microbiologyandimmunology.dal.ca/
Decentralized Finance: Protocols, Risks, and Governance
Authors: Agostino Capponi, Garud Iyengar, Jay Sethuraman
Date: 2023-12-02 00:00:00

Financial markets are undergoing an unprecedented transformation. Technological advances have brought major improvements to the operations of financial services. While these advances promote improved accessibility and convenience, traditional finance shortcomings like lack of transparency and moral hazard frictions continue to plague centralized platforms, imposing societal costs. In this paper, we argue how these shortcomings and frictions are being mitigated by the decentralized finance (DeFi) ecosystem. We delve into the workings of smart contracts, the backbone of DeFi transactions, with an emphasis on those underpinning token exchange and lending services. We highlight the pros and cons of the novel form of decentralized governance introduced via the ownership of governance tokens. Despite its potential, the current DeFi infrastructure introduces operational risks to users, which we segment into five primary categories: consensus mechanisms, protocol, oracle, frontrunning, and systemic risks. We conclude by emphasizing the need for future research to focus on the scalability of existing blockchains, the improved design and interoperability of DeFi protocols, and the rigorous auditing of smart contracts.
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
Authors: L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout
Date: 2023-11-17 00:00:00
Tasks: Time Series, Variational Inference

Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of uncertainty have largely been limited to applications of ad-hoc solutions that do not consistently improve reliability. In this work, we consider stochastic neural networks and design a tailor-made multimodal data-driven (M2D2) prior distribution over network parameters. We use simple and scalable Gaussian mean-field variational inference to train a Bayesian neural network using the M2D2 prior. We train and evaluate the proposed approach using clinical time-series data in MIMIC-IV and corresponding chest X-ray images in MIMIC-CXR for the classification of acute care conditions. Our empirical results show that the proposed method produces a more reliable predictive model compared to deterministic and Bayesian neural network baselines.
Towards Decentralized Task Offloading and Resource Allocation in User-Centric Mobile Edge Computing
Authors: Langtian Qin, Hancheng Lu, Yuang Chen, Baolin Chong, Feng Wu
Date: 2023-12-03 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing

In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interruptions. Such edge effect severely obstructs offloading of tasks to MEC servers. To address this issue, we propose user-centric mobile edge computing (UCMEC), a novel MEC architecture integrating user-centric transmission, which can ensure high throughput and reliable communication for task offloading. Then, we formulate an optimization problem with joint consideration of task offloading, power control, and computing resource allocation in UCMEC, aiming at obtaining the optimal performance in terms of long-term average total delay. To solve the intractable problem, we propose two decentralized joint optimization schemes based on multi-agent deep reinforcement learning (MADRL) and convex optimization, which consider both cooperation and non-cooperation among network nodes. Simulation results demonstrate that the proposed schemes in UCMEC can significantly improve the uplink transmission rate by at most 343.56% and reduce the long-term average total delay by at most 45.57% compared to traditional cellular-based MEC.
Do androids dream of fictional references? A bibliographic dialogue with ChatGPT3.5
Authors: Olivier Las Vergnas
Date: 2023-09-04 00:00:00

This article focuses on bibliographic references generated by the ChatGPT3.5 tool. Using this tool based on the trained GPT generation model ChatGPT3.5, developed by the company OpenAI, we explored six different themes and analyzed a sample of references generated by the model, in French and English. The results revealed high percentages of fictitious references in several fields, underlining the importance of carefully checking these references before using them in research work. An improvement in results was nevertheless noted between May and July with regard to English references for themes on which ChatGPR3.5 has been particularly trained, but the situation remains unsatisfactory in French, for example. It should also be pointed out that much of the text in this article was generated by ChatGPT in a joint effort with the human author.
TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model
Authors: Stephan Wenninger, Fabian Kemper, Ulrich Schwanecke, Mario Botsch
Date: 2023-11-03 00:00:00
Tasks: Dimensionality Reduction, Self-Supervised Learning

Human shape spaces have been extensively studied, as they are a core element of human shape and pose inference tasks. Classic methods for creating a human shape model register a surface template mesh to a database of 3D scans and use dimensionality reduction techniques, such as Principal Component Analysis, to learn a compact representation. While these shape models enable global shape modifications by correlating anthropometric measurements with the learned subspace, they only provide limited localized shape control. We instead register a volumetric anatomical template, consisting of skeleton bones and soft tissue, to the surface scans of the CAESAR database. We further enlarge our training data to the full Cartesian product of all skeletons and all soft tissues using physically plausible volumetric deformation transfer. This data is then used to learn an anatomically constrained volumetric human shape model in a self-supervised fashion. The resulting TailorMe model enables shape sampling, localized shape manipulation, and fast inference from given surface scans.
States as goal-directed concepts: an epistemic approach to state-representation learning
Authors: Nadav Amir, Yael Niv, Angela Langdon
Date: 2023-12-04 00:00:00
Tasks: Representation Learning

Our goals fundamentally shape how we experience the world. For example, when we are hungry, we tend to view objects in our environment according to whether or not they are edible (or tasty). Alternatively, when we are cold, we may view the very same objects according to their ability to produce heat. Computational theories of learning in cognitive systems, such as reinforcement learning, use the notion of "state-representation" to describe how agents decide which features of their environment are behaviorally-relevant and which can be ignored. However, these approaches typically assume "ground-truth" state representations that are known by the agent, and reward functions that need to be learned. Here we suggest an alternative approach in which state-representations are not assumed veridical, or even pre-defined, but rather emerge from the agent's goals through interaction with its environment. We illustrate this novel perspective by inferring the goals driving rat behavior in an odor-guided choice task and discuss its implications for developing, from first principles, an information-theoretic account of goal-directed state representation learning and behavior.
A Mapping of Triangular Block Interleavers to DRAM for Optical Satellite Communication
Authors: Lukas Steiner, Timo Lehnigk-Emden, Markus Fehrenz, Norbert Wehn
Date: 2023-12-04 00:00:00

Communication in optical downlinks of low earth orbit (LEO) satellites requires interleaving to enable reliable data transmission. These interleavers are orders of magnitude larger than conventional interleavers utilized for example in wireless communication. Hence, the capacity of on-chip memories (SRAMs) is insufficient to store all symbols and external memories (DRAMs) must be used. Due to the overall requirement for very high data rates beyond 100 Gbit/s, DRAM bandwidth then quickly becomes a critical bottleneck of the communication system. In this paper, we investigate triangular block interleavers for the aforementioned application and show that the standard mapping of symbols used for SRAMs results in low bandwidth utilization for DRAMs, in some cases below 50 %. As a solution, we present a novel mapping approach that combines different optimizations and achieves over 90 % bandwidth utilization in all tested configurations. Further, the mapping can be applied to any JEDEC-compliant DRAM device.
Doubly 1-Bit Quantized Massive MIMO
Authors: Italo Atzeni, Antti Tölli, Duy H. N. Nguyen, A. Lee Swindlehurst
Date: 2023-12-04 00:00:00

Enabling communications in the (sub-)THz band will call for massive multiple-input multiple-output (MIMO) arrays at either the transmit- or receive-side, or at both. To scale down the complexity and power consumption when operating across massive frequency and antenna dimensions, a sacrifice in the resolution of the digital-to-analog/analog-to-digital converters (DACs/ADCs) will be inevitable. In this paper, we analyze the extreme scenario where both the transmit- and receive-side are equipped with fully digital massive MIMO arrays and 1-bit DACs/ADCs, which leads to a system with minimum radio-frequency complexity, cost, and power consumption. Building upon the Bussgang decomposition, we derive a tractable approximation of the mean squared error (MSE) between the transmitted data symbols and their soft estimates. Numerical results show that, despite its simplicity, a doubly 1-bit quantized massive MIMO system with very large antenna arrays can deliver an impressive performance in terms of MSE and symbol error rate.
Bitstream Organization for Parallel Entropy Coding on Neural Network-based Video Codecs
Authors: Amir Said, Hoang Le, Farzad Farhadzadeh
Date: 2023-12-01 00:00:00
Tasks: Video Compression

Video compression systems must support increasing bandwidth and data throughput at low cost and power, and can be limited by entropy coding bottlenecks. Efficiency can be greatly improved by parallelizing coding, which can be done at much larger scales with new neural-based codecs, but with some compression loss related to data organization. We analyze the bit rate overhead needed to support multiple bitstreams for concurrent decoding, and for its minimization propose a method for compressing parallel-decoding entry points, using bidirectional bitstream packing, and a new form of jointly optimizing arithmetic coding termination. It is shown that those techniques significantly lower the overhead, making it easier to reduce it to a small fraction of the average bitstream size, like, for example, less than 1% and 0.1% when the average number of bitstream bytes is respectively larger than 95 and 1,200 bytes.