pwc-archive/papers-with-abstracts huggingface dataset:
Effect of Channel Geometry and Flow Rates in Hydrodynamic Focusing on Impedance Detection of Circulating Tumor Cells
Authors: Hassan Raji, Iraj Dehghan Hamani
Date: 2022-03-08 00:00:00

Cells, other than their biological properties, have different electric and physical properties. In an impedance cytometer, cells should pass one by one in the detection region where pairs of electrodes are located. When cells are located between electrodes, the impedance changes, and this can be indicative of the presence of a cell. This is basically because the electric properties of cells are different from the medium between the electrodes which is important in determining the impedance. One of the most important aspects which influence the performance of an impedance cytometer performance is the microchannel design. In this work, in the first step, the microchannel was designed in a way to have the best detection in the impedance cytometer. In this regard, hydrodynamic focusing was selected to focus the population of cells entering from the inlet of the main channel. To find the optimal parameters of the microchannel, different geometry for the channel itself, along with flow rates and other parameters related to sheath flow were simulated. In the next step, impedance was measured in COMSOL for White blood cells, MCF7, and MDA-MB-231 breast cancer cells. The results show that by measuring the impedance of cells using the optimized channel design, CTCs can be successfully differentiated from WBCs.
Newton-Okounkov bodies of chemical reaction systems
Authors: Nida Obatake, Elise Walker
Date: 2022-03-08 00:00:00

Despite their noted potential in polynomial-system solving, there are few concrete examples of Newton-Okounkov bodies arising from applications. Accordingly, in this paper, we introduce a new application of Newton-Okounkov body theory to the study of chemical reaction networks, and compute several examples. An important invariant of a chemical reaction network is its maximum number of positive steady states, which is realized as the maximum number of positive real roots of a parametrized polynomial system. Here, we introduce a new upper bound on this number, namely the `Newton-Okounkov body bound' of a chemical reaction network. Through explicit examples, we show that the Newton-Okounkov body bound of a network gives a good upper bound on its maximum number of positive steady states. We also compare this Newton-Okounkov body bound to a related upper bound, namely the mixed volume of a chemical reaction network, and find that it often achieves better bounds.
Region or Global? A Principle for Negative Sampling in Graph-based Recommendation
Authors: Zhen Yang; Ming Ding; Xu Zou; Jie Tang; Bin Xu; Chang Zhou; Hongxia Yang
Date: 2022-03-01 00:00:00
Proceeding: IEEE Transactions on Knowledge and Data Engineering 2022 3
Tasks: Recommendation Systems

Graph-based recommendation systems are blossoming recently, which models user-item interactions as a user-item graph and utilizes graph neural networks (GNNs) to learn the embeddings for users and items. A fundamental challenge of graph-based recommendation is that there only exists observed positive user-item pairs in the user-item graph. Negative sampling is a vital technique to solve the one-class problem and is widely used in many recommendation methods. However, the previous works only focus on the design of negative sampling distribution but ignore the sampled region for negative sampling. In this work, we propose the Three-Region Principle to guide negative sampling, which suggests that we should negatively sample more items at an intermediate region and less adjacent and distant items. In light of this principle, we present the RecNS method, which is a general negative sampling method designed with two sampling strategies: positive-assisted sampling and exposure-augmented sampling. Instead of sampling existing negative items from graph data, we merge these two strategies in embedding space to generate negative item embeddings. Extensive experiments demonstrate that our proposed RecNS method significantly outperforms all negative sampling baselines, e.g., 10.47% for PinSage, 6.02% for NGCF, and 8.20% for LightGCN in terms of Recall@20 on the Alibaba dataset.
Biological Sequence Design with GFlowNets
Authors: Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
Date: 2022-03-02 00:00:00
Tasks: Active Learning, Diversity

Design of de novo biological sequences with desired properties, like protein and DNA sequences, often involves an active loop with several rounds of molecule ideation and expensive wet-lab evaluations. These experiments can consist of multiple stages, with increasing levels of precision and cost of evaluation, where candidates are filtered. This makes the diversity of proposed candidates a key consideration in the ideation phase. In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round. We also propose a scheme to incorporate existing labeled datasets of candidates, in addition to a reward function, to speed up learning in GFlowNets. We present empirical results on several biological sequence design tasks, and we find that our method generates more diverse and novel batches with high scoring candidates compared to existing approaches.
Risk sharing in equity-linked insurance products: Stackelberg equilibrium between an insurer and a reinsurer
Authors: Yevhen Havrylenko, Maria Hinken, Rudi Zagst
Date: 2022-03-08 00:00:00

We study the optimal investment-reinsurance problem in the context of equity-linked insurance products. Such products often have a capital guarantee, which can motivate insurers to purchase reinsurance. Since a reinsurance contract implies an interaction between the insurer and the reinsurer, we model the optimization problem as a Stackelberg game. The reinsurer is the leader in the game and maximizes its expected utility by selecting its optimal investment strategy and a safety loading in the reinsurance contract it offers to the insurer. The reinsurer can assess how the insurer will rationally react on each action of the reinsurer. The insurance company is the follower and maximizes its expected utility by choosing its investment strategy and the amount of reinsurance the company purchases at the price offered by the reinsurer. In this game, we derive the Stackelberg equilibrium for general utility functions. For power utility functions, we calculate the equilibrium explicitly and find that the reinsurer selects the largest reinsurance premium such that the insurer may still buy the maximal amount of reinsurance. Since in the equilibrium the insurer is indifferent in the amount of reinsurance, in practice, the reinsurer should consider charging a smaller reinsurance premium than the equilibrium one. Therefore, we propose several criteria for choosing such a discount rate and investigate its wealth-equivalent impact on the expected utility of each party.
Proximal PanNet: A Model-Based Deep Network for Pansharpening
Authors: Xiangyong Cao, Yang Chen, Wenfei Cao
Date: 2022-02-12 00:00:00
Tasks: Deep Learning, Pansharpening

Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series
Authors: Hussein, Amir; Hajj, Hazem
Date: 2022-02-15 00:00:00
Proceeding: ACM Transactions on Internet of Things 2022 2
Tasks: Activity Recognition, Domain Adaptation, Relation, Representation Learning, Time Series, Time Series Analysis

In many real-world scenarios, machine learning models fall short in prediction performance due to data characteristics changing from training on one source domain to testing on a target domain. There has been extensive research to address this problem with Domain Adaptation (DA) for learning domain invariant features. However, when considering advances for time series, those methods remain limited to the use of hard parameter sharing (HPS) between source and target models, and the use of domain adaptation objective function. To address these challenges, we propose a soft parameter sharing (SPS) DA architecture with representation learning while modeling the relation as non-linear between parameters of source and target models and modeling the adaptation loss function as the squared Maximum Mean Discrepancy (MMD). The proposed architecture advances the state-of-the-art for time series in the context of activity recognition and in fields with other modalities, where SPS has been limited to a linear relation. An additional contribution of our work is to provide a study that demonstrates the strengths and limitations of HPS versus SPS. Experiment results showed the success of the method in three domain adaptation cases of multivariate time series activity recognition with different users and sensors.
PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation
Authors: Wentao Liu, Tong Tian, Weijin Xu, Huihua Yang, Xipeng Pan, Songlin Yan, Lemeng Wang
Date: 2022-03-09 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The success of Transformer in computer vision has attracted increasing attention in the medical imaging community. Especially for medical image segmentation, many excellent hybrid architectures based on convolutional neural networks (CNNs) and Transformer have been presented and achieve impressive performance. However, most of these methods, which embed modular Transformer into CNNs, struggle to reach their full potential. In this paper, we propose a novel hybrid architecture for medical image segmentation called PHTrans, which parallelly hybridizes Transformer and CNN in main building blocks to produce hierarchical representations from global and local features and adaptively aggregate them, aiming to fully exploit their strengths to obtain better segmentation performance. Specifically, PHTrans follows the U-shaped encoder-decoder design and introduces the parallel hybird module in deep stages, where convolution blocks and the modified 3D Swin Transformer learn local features and global dependencies separately, then a sequence-to-volume operation unifies the dimensions of the outputs to achieve feature aggregation. Extensive experimental results on both Multi-Atlas Labeling Beyond the Cranial Vault and Automated Cardiac Diagnosis Challeng datasets corroborate its effectiveness, consistently outperforming state-of-the-art methods. The code is available at: https://github.com/lseventeen/PHTrans.
Onception: Active Learning with Expert Advice for Real World Machine Translation
Authors: Vânia Mendonça, Ricardo Rei, Luisa Coheur, Alberto Sardinha
Date: 2022-03-09 00:00:00
Tasks: Active Learning, Machine Translation, Translation

Active learning can play an important role in low-resource settings (i.e., where annotated data is scarce), by selecting which instances may be more worthy to annotate. Most active learning approaches for Machine Translation assume the existence of a pool of sentences in a source language, and rely on human annotators to provide translations or post-edits, which can still be costly. In this article, we assume a real world human-in-the-loop scenario in which: (i) the source sentences may not be readily available, but instead arrive in a stream; (ii) the automatic translations receive feedback in the form of a rating, instead of a correct/edited translation, since the human-in-the-loop might be a user looking for a translation, but not be able to provide one. To tackle the challenge of deciding whether each incoming pair source-translations is worthy to query for human feedback, we resort to a number of stream-based active learning query strategies. Moreover, since we not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice. Our experiments show that using active learning allows to converge to the best Machine Translation systems with fewer human interactions. Furthermore, combining multiple strategies using prediction with expert advice often outperforms several individual active learning strategies with even fewer interactions.
An Efficient Approach for Cross-Silo Federated Learning to Rank
Authors: Yansheng Wang, Yongxin Tong, Dingyuan Shi, Ke Xu
Date: 2021-06-21 00:00:00
Proceeding: 2021 IEEE 37th International Conference on Data Engineering 2021 6
Tasks: Federated Learning, Learning-To-Rank, Privacy Preserving

Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data. However, with the increasing awareness of data privacy, it is harder to collect data from multiple owners as before, and the resultant data isolation problem makes the performance of learned LTR models severely compromised. Inspired by the recent progress in federated learning, we propose a novel framework named Cross-Silo Federated Learning-to-Rank (CS-F-LTR), where the efficiency issue becomes the major bottleneck. To deal with the challenge, we first devise a privacy-preserving cross-party term frequency querying scheme based on sketching algorithms and differential privacy. To further improve the overall efficiency, we propose a new structure named reverse top-K sketch (RTK-Sketch) which significantly accelerates the feature generation process while holding theoretical guarantees on accuracy loss. Extensive experiments conducted on public datasets verify the effectiveness and efficiency of the proposed approach.
A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection
Authors: Yukun Su, Jingliang Deng, Ruizhou Sun, Guosheng Lin, Qingyao Wu
Date: 2022-03-09 00:00:00
Tasks: Co-Salient Object Detection, object-detection, Object Detection, Saliency Detection, Salient Object Detection, Semantic Segmentation, Video Salient Object Detection

Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. In the computer vision area, many researches focus on co-segmentation (CoS), co-saliency detection (CoSD) and video salient object detection (VSOD) to discover the co-occurrent objects. However, previous approaches design different networks on these similar tasks separately, and they are difficult to apply to each other, which lowers the upper bound of the transferability of deep learning frameworks. Besides, they fail to take full advantage of the cues among inter- and intra-feature within a group of images. In this paper, we introduce a unified framework to tackle these issues, term as UFO (Unified Framework for Co-Object Segmentation). Specifically, we first introduce a transformer block, which views the image feature as a patch token and then captures their long-range dependencies through the self-attention mechanism. This can help the network to excavate the patch structured similarities among the relevant objects. Furthermore, we propose an intra-MLP learning module to produce self-mask to enhance the network to avoid partial activation. Extensive experiments on four CoS benchmarks (PASCAL, iCoseg, Internet and MSRC), three CoSD benchmarks (Cosal2015, CoSOD3k, and CocA) and four VSOD benchmarks (DAVIS16, FBMS, ViSal and SegV2) show that our method outperforms other state-of-the-arts on three different tasks in both accuracy and speed by using the same network architecture , which can reach 140 FPS in real-time.
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution
Authors: Safarov SIrojbek
Date: 2021-02-19 00:00:00
Proceeding: Sensors 2021 2
Tasks: Decoder, Dimensionality Reduction, Medical Image Segmentation, Segmentation

Colon carcinoma is one of the leading causes of cancer-related death in both men and women. Automatic colorectal polyp segmentation and detection in colonoscopy videos help endoscopists to identify colorectal disease more easily, making it a promising method to prevent colon cancer. In this study, we developed a fully automated pixel-wise polyp segmentation model named A-DenseUNet. The proposed architecture adapts different datasets, adjusting for the unknown depth of the network by sharing multiscale encoding information to the different levels of the decoder side. We also used multiple dilated convolutions with various atrous rates to observe a large field of view without increasing the computational cost and prevent loss of spatial information, which would cause dimensionality reduction. We utilized an attention mechanism to remove noise and inappropriate information, leading to the comprehensive re-establishment of contextual features. Our experiments demonstrated that the proposed architecture achieved significant segmentation results on public datasets. A-DenseUNet achieved a 90% Dice coefficient score on the Kvasir-SEG dataset and a 91% Dice coefficient score on the CVC-612 dataset, both of which were higher than the scores of other deep learning models such as UNet++, ResUNet, U-Net, PraNet, and ResUNet++ for segmenting polyps in colonoscopy images.
Pareto Optimality for Fairness-constrained Collaborative Filtering
Authors: Qianxiu Hao, Qianqian Xu, Zhiyong Yang, Qingming Huang
Date: 2021-09-17 00:00:00
Proceeding: MM '21: Proceedings of the 29th ACM International Conference on Multimedia 2021 9
Tasks: Collaborative Filtering, Fairness

The well-known collaborative filtering (CF) models typically optimize a single objective summed over all historical user-item interactions. Due to inevitable imbalances and biases in real-world data, they may develop a policy that unfairly discriminates against certain subgroups with low sample frequencies. To balance overall recommendation performance and fairness, prevalent solutions apply fairness constraints or regularizations to enforce equality of certain performance across different subgroups. However, simply enforcing equality of performance may lead to large performance degradation of those advantaged subgroups. To address this issue, we formulate a constrained Multi-Objective Optimization (MOO) problem. In contrast to the single objective, we treat the performance of each subgroup equivalently as an objective. This ensures that the imbalanced subgroup sample frequency does not affect the gradient information. We further propose fairness constraints to limit the search space to obtain more balanced solutions. To solve the constrained MOO problem, a gradient-based constrained MOO algorithm is proposed to seek a proper Pareto optimal solution for the performance trade-off. Extensive experiments on synthetic and real-world datasets show that our approach could help improve the recommendation accuracy of disadvantaged groups, while not damaging the overall performance.
Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method
Authors: Alvin Shek, Bo Ying Su, Rui Chen, Changliu Liu
Date: 2022-03-09 00:00:00
Tasks: Object

For robots to be effectively deployed in novel environments and tasks, they must be able to understand the feedback expressed by humans during intervention. This can either correct undesirable behavior or indicate additional preferences. Existing methods either require repeated episodes of interactions or assume prior known reward features, which is data-inefficient and can hardly transfer to new tasks. We relax these assumptions by describing human tasks in terms of object-centric sub-tasks and interpreting physical interventions in relation to specific objects. Our method, Object Preference Adaptation (OPA), is composed of two key stages: 1) pre-training a base policy to produce a wide variety of behaviors, and 2) online-updating according to human feedback. The key to our fast, yet simple adaptation is that general interaction dynamics between agents and objects are fixed, and only object-specific preferences are updated. Our adaptation occurs online, requires only one human intervention (one-shot), and produces new behaviors never seen during training. Trained on cheap synthetic data instead of expensive human demonstrations, our policy correctly adapts to human perturbations on realistic tasks on a physical 7DOF robot. Videos, code, and supplementary material are provided.
Data-Efficient Structured Pruning via Submodular Optimization
Authors: Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien
Date: 2022-03-09 00:00:00

Structured pruning is an effective approach for compressing large pre-trained neural networks without significantly affecting their performance. However, most current structured pruning methods do not provide any performance guarantees, and often require fine-tuning, which makes them inapplicable in the limited-data regime. We propose a principled data-efficient structured pruning method based on submodular optimization. In particular, for a given layer, we select neurons/channels to prune and corresponding new weights for the next layer, that minimize the change in the next layer's input induced by pruning. We show that this selection problem is a weakly submodular maximization problem, thus it can be provably approximated using an efficient greedy algorithm. Our method is guaranteed to have an exponentially decreasing error between the original model and the pruned model outputs w.r.t the pruned size, under reasonable assumptions. It is also one of the few methods in the literature that uses only a limited-number of training data and no labels. Our experimental results demonstrate that our method outperforms state-of-the-art methods in the limited-data regime.