pwc-archive/papers-with-abstracts huggingface dataset:
RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images
Authors: Bahareh Behboodi, Francois-Xavier Carton, Matthieu Chabanas, Sandrine de Ribaupierre, Ole Solheim, Bodil K. R. Munkvold, Hassan Rivaz, Yiming Xiao, Ingerid Reinertsen
Date: 2022-07-13 00:00:00

Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images play an essential role in surgical planning and resection of brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique annotation dataset of tumor tissues and resection cavities from the previously published RESECT dataset (Xiao et al. 2017) to encourage a more rigorous assessments of image processing techniques. Acquisition and validation methods: The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent resection surgeries. The proposed dataset contains tumor tissues and resection cavity annotations of the iUS images. The quality of annotations were validated by two highly experienced neurosurgeons through several assessment criteria. Data format and availability: Annotations of tumor tissues and resection cavities are provided in 3D NIFTI formats. Both sets of annotations are accessible online in the \url{https://osf.io/6y4db}. Discussion and potential applications: The proposed database includes tumor tissue and resection cavity annotations from real-world clinical ultrasound brain images to evaluate segmentation and registration methods. These labels could also be used to train deep learning approaches. Eventually, this dataset should further improve the quality of image guidance in neurosurgery.
Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach
Authors: Shuo Sun, Rundong Wang, Bo An
Date: 2022-06-07 00:00:00
Tasks: Decision Making, Mixture-of-Experts, Multi-Task Learning, Stock Prediction

Quantitative investment is a fundamental financial task that highly relies on accurate stock prediction and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, we observe that the performance of existing DL methods is sensitive to random seeds and network initialization. To design more profitable DL methods, we analyze this phenomenon and find two major limitations of existing works. First, there is a noticeable gap between accurate financial predictions and profitable investment strategies. Second, investment decisions are made based on only one individual predictor without consideration of model uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle these two limitations, we first reformulate quantitative investment as a multi-task learning problem. Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up trading strategy design workflow of successful trading firms. In Stage one, multiple independent trading experts are jointly optimized with an individual uncertainty-aware loss function. In Stage two, we train neural routers (corresponding to the role of a portfolio manager) to dynamically deploy these experts on an as-needed basis. AlphaMix is also a universal framework that is applicable to various backbone network architectures with consistent performance gains. Through extensive experiments on long-term real-world data spanning over five years on two of the most influential financial markets (US and China), we demonstrate that AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review
Authors: Sancho Salcedo-Sanz, Jorge Pérez-Aracil, Guido Ascenso, Javier Del Ser, David Casillas-Pérez, Christopher Kadow, Dusan Fister, David Barriopedro, Ricardo García-Herrera, Marcello Restelli, Mateo Giuliani, Andrea Castelletti
Date: 2022-06-03 00:00:00

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.
Chemical detection and indexing in PubMed full text articles using deep learning and rule-based methods
Authors: Tiago Almeida, Rui Antunes, João Figueira Silva, João Rafael Almeida, Sérgio Matos
Date: 2021-11-08 00:00:00
Proceeding: BioCreative VII Challenge Evaluation Workshop 2021 11
Tasks: Articles, Chemical Indexing, Entity Linking, Named Entity Recognition (NER), Retrieval

Identifying chemicals in biomedical scientific literature is a crucial task for drug development research. The BioCreative NLM-Chem challenge promoted the development of automatic systems that can identify chemicals in full-text articles and decide which chemical concepts are relevant to be indexed. This work describes the participation of the BIT.UA team from the University of Aveiro, where we propose a three-stage automatic pipeline that individually tackles (i) chemical mention detection, (ii) entity normalization and (iii) indexing. We adopted a deep learning solution based on a biomedical BERT variant for chemical identification. For normalization we used a rule-based approach and a hybrid version that explores a dense retrieval mechanism. Similarly, for indexing we also followed two distinct approaches: a rule-based, and a TF-IDF based method. Our best official results are consistently above the official median and benchmark in the three subtasks, with respectively 0.8454, 0.8136, and 0.4664 F1-scores.
"Stuff You Should Know" (SYSK) Podcast Episode Transcripts - Full Dataset with Transcript of All Episodes
Authors: Britt D. Pierson
Date: 2022-07-18 00:00:00
Proceeding: None 2022 7
Tasks: All, BIG-bench Machine Learning

SYSK is an iHeartRadio podcast production. Here we provide transcripts, sentiment and timestamps in JSON format of the entire Stuff You Should Know podcast episode corpus, generated automatically using machine learning (ML). Each episode's full transcript is connected to the episode title and a link to the audio.
MAC-DO: An Efficient Output-Stationary GEMM Accelerator for CNNs Using DRAM Technology
Authors: Minki Jeong, Wanyeong Jung
Date: 2022-07-16 00:00:00
Tasks: speech-recognition, Speech Recognition, Video Segmentation, Video Semantic Segmentation

DRAM-based in-situ accelerators have shown their potential in addressing the memory wall challenge of the traditional von Neumann architecture. Such accelerators exploit charge sharing or logic circuits for simple logic operations at the DRAM subarray level. However, their throughput is limited due to low array utilization, as only a few row cells in a DRAM array participate in operations while most rows remain deactivated. Moreover, they require many cycles for more complex operations such as a multi-bit multiply-accumulate (MAC) operation, resulting in significant data access and movement and potentially worsening power efficiency. To overcome these limitations, this paper presents MAC-DO, an efficient and low-power DRAM-based in-situ accelerator. Compared to previous DRAM-based in-situ accelerators, a MAC-DO cell, consisting of two 1T1C DRAM cells (two transistors and two capacitors), innately supports a multi-bit MAC operation within a single cycle, ensuring good linearity and compatibility with existing 1T1C DRAM cells and array structures. This achievement is facilitated by a novel analog computation method utilizing charge steering. Additionally, MAC-DO enables concurrent individual MAC operations in each MAC-DO cell without idle cells, significantly improving throughput and energy efficiency. As a result, a MAC-DO array efficiently can accelerate matrix multiplications based on output stationary mapping, supporting the majority of computations performed in deep neural networks (DNNs). Furthermore, a MAC-DO array efficiently reuses three types of data (input, weight and output), minimizing data movement.
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement
Authors: Canqian Yang, Meiguang Jin, Yi Xu, Rui Zhang, Ying Chen, Huaida Liu
Date: 2022-07-18 00:00:00
Tasks: Computational Efficiency, Image Enhancement, Photo Retouching

Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms. However, they embed the complete transform, including the color component-independent and the component-correlated parts, into only a single type of LUTs, either 1D or 3D, in a coupled manner. This scheme raises a dilemma of improving model expressiveness or efficiency due to two factors. On the one hand, the 1D LUTs provide high computational efficiency but lack the critical capability of color components interaction. On the other, the 3D LUTs present enhanced component-correlated transform capability but suffer from heavy memory footprint, high training difficulty, and limited cell utilization. Inspired by the conventional divide-and-conquer practice in the image signal processor, we present SepLUT (separable image-adaptive lookup table) to tackle the above limitations. Specifically, we separate a single color transform into a cascade of component-independent and component-correlated sub-transforms instantiated as 1D and 3D LUTs, respectively. In this way, the capabilities of two sub-transforms can facilitate each other, where the 3D LUT complements the ability to mix up color components, and the 1D LUT redistributes the input colors to increase the cell utilization of the 3D LUT and thus enable the use of a more lightweight 3D LUT. Experiments demonstrate that the proposed method presents enhanced performance on photo retouching benchmark datasets than the current state-of-the-art and achieves real-time processing on both GPUs and CPUs.
Temporal Lift Pooling for Continuous Sign Language Recognition
Authors: Lianyu Hu, Liqing Gao, Zekang Liu, Wei Feng
Date: 2022-07-18 00:00:00
Tasks: Sign Language Recognition

Pooling methods are necessities for modern neural networks for increasing receptive fields and lowering down computational costs. However, commonly used hand-crafted pooling approaches, e.g., max pooling and average pooling, may not well preserve discriminative features. While many researchers have elaborately designed various pooling variants in spatial domain to handle these limitations with much progress, the temporal aspect is rarely visited where directly applying hand-crafted methods or these specialized spatial variants may not be optimal. In this paper, we derive temporal lift pooling (TLP) from the Lifting Scheme in signal processing to intelligently downsample features of different temporal hierarchies. The Lifting Scheme factorizes input signals into various sub-bands with different frequency, which can be viewed as different temporal movement patterns. Our TLP is a three-stage procedure, which performs signal decomposition, component weighting and information fusion to generate a refined downsized feature map. We select a typical temporal task with long sequences, i.e. continuous sign language recognition (CSLR), as our testbed to verify the effectiveness of TLP. Experiments on two large-scale datasets show TLP outperforms hand-crafted methods and specialized spatial variants by a large margin (1.5%) with similar computational overhead. As a robust feature extractor, TLP exhibits great generalizability upon multiple backbones on various datasets and achieves new state-of-the-art results on two large-scale CSLR datasets. Visualizations further demonstrate the mechanism of TLP in correcting gloss borders. Code is released.
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Authors: Zizheng Huang, Haoxing Chen, Ziqi Wen, Chao Zhang, Huaxiong Li, Bo wang, Chunlin Chen
Date: 2022-07-16 00:00:00
Tasks: Contrastive Learning, Graph Representation Learning, Representation Learning, Self-Supervised Image Classification, Self-Supervised Learning, Sentence

Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains. However, the most common InfoNCE-based methods suffer from some dilemmas, such as \textit{uniformity-tolerance dilemma} (UTD) and \textit{gradient reduction}, both of which are related to a $\mathcal{P}_{ij}$ term. It has been identified that UTD can lead to unexpected performance degradation. We argue that the fixity of temperature is to blame for UTD. To tackle this challenge, we enrich the CL loss family by presenting a Model-Aware Contrastive Learning (MACL) strategy, whose temperature is adaptive to the magnitude of alignment that reflects the basic confidence of the instance discrimination task, then enables CL loss to adjust the penalty strength for hard negatives adaptively. Regarding another dilemma, the gradient reduction issue, we derive the limits of an involved gradient scaling factor, which allows us to explain from a unified perspective why some recent approaches are effective with fewer negative samples, and summarily present a gradient reweighting to escape this dilemma. Extensive remarkable empirical results in vision, sentence, and graph modality validate our approach's general improvement for representation learning and downstream tasks.
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Authors: Gensheng Pei, Fumin Shen, Yazhou Yao, Guo-Sen Xie, Zhenmin Tang, Jinhui Tang
Date: 2022-07-18 00:00:00
Tasks: Object, Optical Flow Estimation, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Salient Object Detection, Video Semantic Segmentation

Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS). Most of the previous methods directly extract and fuse the motion and appearance features for segmenting target objects in the UVOS setting. However, optical flow is intrinsically an instantaneous velocity of all pixels among consecutive frames, thus making the motion features not aligned well with the primary objects among the corresponding frames. To solve the above challenge, we propose a concise, practical, and efficient architecture for appearance and motion feature alignment, dubbed hierarchical feature alignment network (HFAN). Specifically, the key merits in HFAN are the sequential Feature AlignMent (FAM) module and the Feature AdaptaTion (FAT) module, which are leveraged for processing the appearance and motion features hierarchically. FAM is capable of aligning both appearance and motion features with the primary object semantic representations, respectively. Further, FAT is explicitly designed for the adaptive fusion of appearance and motion features to achieve a desirable trade-off between cross-modal features. Extensive experiments demonstrate the effectiveness of the proposed HFAN, which reaches a new state-of-the-art performance on DAVIS-16, achieving 88.7 $\mathcal{J}\&\mathcal{F}$ Mean, i.e., a relative improvement of 3.5% over the best published result.
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
Authors: Minhyeok Lee, Chaewon Park, Suhwan Cho, Sangyoun Lee
Date: 2022-07-16 00:00:00
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, Salient Object Detection, Superpixels

RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps. To solve this problem, we propose a novel superpixel prototype sampling network (SPSN) architecture. The proposed model splits the input RGB image and depth map into component superpixels to generate component prototypes. We design a prototype sampling network so that the network only samples prototypes corresponding to salient objects. In addition, we propose a reliance selection module to recognize the quality of each RGB and depth feature map and adaptively weight them in proportion to their reliability. The proposed method makes the model robust to inconsistencies between RGB images and depth maps and eliminates the influence of non-salient objects. Our method is evaluated on five popular datasets, achieving state-of-the-art performance. We prove the effectiveness of the proposed method through comparative experiments.
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks
Authors: Yabo Zhang, Mingshuai Yao, Yuxiang Wei, Zhilong Ji, Jinfeng Bai, WangMeng Zuo
Date: 2022-07-18 00:00:00
Tasks: Diversity, Domain Adaptation

One-shot generative domain adaption aims to transfer a pre-trained generator on one domain to a new domain using one reference image only. However, it remains very challenging for the adapted generator (i) to generate diverse images inherited from the pre-trained generator while (ii) faithfully acquiring the domain-specific attributes and styles of the reference image. In this paper, we present a novel one-shot generative domain adaption method, i.e., DiFa, for diverse generation and faithful adaptation. For global-level adaptation, we leverage the difference between the CLIP embedding of reference image and the mean embedding of source images to constrain the target generator. For local-level adaptation, we introduce an attentive style loss which aligns each intermediate token of adapted image with its corresponding token of the reference image. To facilitate diverse generation, selective cross-domain consistency is introduced to select and retain the domain-sharing attributes in the editing latent $\mathcal{W}+$ space to inherit the diversity of pre-trained generator. Extensive experiments show that our method outperforms the state-of-the-arts both quantitatively and qualitatively, especially for the cases of large domain gaps. Moreover, our DiFa can easily be extended to zero-shot generative domain adaption with appealing results. Code is available at https://github.com/1170300521/DiFa.
Defect Transformer: An Efficient Hybrid Transformer Architecture for Surface Defect Detection
Authors: Junpu Wang, Guili Xu, Fuju Yan, Jinjin Wang, Zhengsheng Wang
Date: 2022-07-17 00:00:00
Tasks: Computational Efficiency, Decoder, Defect Detection, Position

Surface defect detection is an extremely crucial step to ensure the quality of industrial products. Nowadays, convolutional neural networks (CNNs) based on encoder-decoder architecture have achieved tremendous success in various defect detection tasks. However, due to the intrinsic locality of convolution, they commonly exhibit a limitation in explicitly modeling long-range interactions, critical for pixel-wise defect detection in complex cases, e.g., cluttered background and illegible pseudo-defects. Recent transformers are especially skilled at learning global image dependencies but with limited local structural information necessary for detailed defect location. To overcome the above limitations, we propose an efficient hybrid transformer architecture, termed Defect Transformer (DefT), for surface defect detection, which incorporates CNN and transformer into a unified model to capture local and non-local relationships collaboratively. Specifically, in the encoder module, a convolutional stem block is firstly adopted to retain more detailed spatial information. Then, the patch aggregation blocks are used to generate multi-scale representation with four hierarchies, each of them is followed by a series of DefT blocks, which respectively include a locally position-aware block for local position encoding, a lightweight multi-pooling self-attention to model multi-scale global contextual relationships with good computational efficiency, and a convolutional feed-forward network for feature transformation and further location information learning. Finally, a simple but effective decoder module is proposed to gradually recover spatial details from the skip connections in the encoder. Extensive experiments on three datasets demonstrate the superiority and efficiency of our method compared with other CNN- and transformer-based networks.
DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing
Authors: Haoran Luo, Haihong E, Ling Tan, Gengxian Zhou, Tianyu Yao, Kaiyang Wan
Date: 2022-07-18 00:00:00
Proceeding: AAAI 2023 6
Tasks: Attribute, Entity Typing, Entity Typing on DH-KGs, Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Link prediction on DH-KGs, Representation Learning

In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. This paper defines link prediction and entity typing tasks on DH-KG for the first time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms baseline models on DH-KG, according to experimental results. Finally, we provide an example of how this technology can be used to treat hypertension. Our model and new datasets are publicly available.
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
Authors: Wentao Chen, Zhang Zhang, Wei Wang, Liang Wang, Zilei Wang, Tieniu Tan
Date: 2022-07-16 00:00:00
Tasks: Few-Shot Learning

Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and aim to address the domain gap between the support set and the query set. Different from previous cross-domain FSL work (CD-FSL) that considers the domain shift between base and novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL), requires few-shot learners not only to adapt to the new domain, but also to be consistent between different domains within each novel class. To this end, we propose a novel approach, namely stabPA, to learn prototypical compact and cross-domain aligned representations, so that the domain shift and few-shot learning can be addressed simultaneously. We evaluate our approach on two new CDCS-FSL benchmarks built from the DomainNet and Office-Home datasets respectively. Remarkably, our approach outperforms multiple elaborated baselines by a large margin, e.g., improving 5-shot accuracy by 6.0 points on average on DomainNet. Code is available at https://github.com/WentaoChen0813/CDCS-FSL