pwc-archive/papers-with-abstracts huggingface dataset:
Systematic Review of Methods and Prognostic Value of Mitotic Activity. Part 1: Feline Tumors
Authors: Christof A. Bertram, Taryn A. Donovan, Alexander Bartel
Date: 2023-05-02 00:00:00
Tasks: Articles, Diagnostic

Increased proliferation is a key driver of tumorigenesis, and quantification of mitotic activity is a standard task for prognostication. The goal of this systematic review is scholarly analysis of all available references on mitotic activity in feline tumors, and to provide an overview of the measuring methods and prognostic value. A systematic literature search in PubMed and Scopus and a manual search in Google Scholar was conducted. All articles on feline tumors that correlated mitotic activity with patient outcome were identified. Data analysis revealed that of the eligible 42 articles, the mitotic count (MC, mitotic figures per tumor area) was evaluated in 39 instances and the mitotic index (MI, mitotic figures per tumor cells) in three instances. The risk of bias was considered high for most studies (26/42, 62%) based on small study populations, insufficient details of the MC/MI methods, and lack of statistical measures for diagnostic accuracy or effect on outcome. The MC/MI methods varied markedly between studies. A significant association of the MC with survival was determined in 21/29 (72%) studies, while one study found an inverse effect. There were three tumor types with at least four studies and a prognostic association was found in 5/6 studies on mast cell tumors, 5/5 on mammary tumors and 3/4 on soft tissue sarcomas. The MI was shown to correlate with survival by two research groups, however a comparison to the MC was not conducted. An updated systematic review will be needed with of new literature for different tumor types.
NFT Wash Trading Detection
Authors: Derek Liu, Francesco Piccoli, Katie Chen, Adrina Tang, Victor Fang
Date: 2023-02-07 00:00:00

Wash trading is a form of market manipulation where the same entity sells an asset to themselves to drive up market prices, launder money under the cover of a legitimate transaction, or claim a tax loss without losing ownership of an asset. Although the practice is illegal with traditional assets, lack of supervision in the non-fungible token market enables criminals to wash trade and scam unsuspecting buyers while operating under regulators radar. AnChain.AI designed an algorithm that flags transactions within an NFT collection history as wash trades when a wallet repurchases a token within 30 days of previously selling it. The algorithm also identifies intermediate transactions within a wash trade cycle. Testing on 7 popular NFT collections reveals that on average, 0.14% of transactions, 0.11% of wallets, and 0.16% of tokens in each collection are involved in wash trading. These wash trades generate an overall total price manipulation, sales, and repurchase profit of \$900K, \$1.1M, and negative \$1.6M respectively. The results draw attention to the prevalent market manipulation taking place and inform unsuspecting buyers which tokens and sellers may be involved in criminal activity.
Construct sparse portfolio with mutual fund's favourite stocks in China A share market
Authors: Ke Zhang
Date: 2023-04-24 00:00:00

Unlike developed market, some emerging markets are dominated by retail and unprofessional trading. China A share market is a good and fitting example in last 20 years. Meanwhile, lots of research show professional investor in China A share market continuously generate excess return compare with total market index. Specifically, this excess return mostly come from stock selectivity ability instead of market timing. However for some reason such as fund capacity limit, fund manager change or market regional switch, it is very hard to find a fund could continuously beat market. Therefore, in order to get excess return from mutual fund industry, we use quantitative way to build the sparse portfolio that take advantage of favorite stocks by mutual fund in China A market. Firstly we do the analysis about favourite stocks by mutual fund and compare the different method to construct our portfolio. Then we build a sparse stock portfolio with constraint on both individual stock and industry exposure using portfolio optimizer to closely track the partial equity funds index 930950.CSI with median 0.985 correlation. This problem is much more difficult than tracking full information index or traditional ETF as higher turnover of mutual fund, just first 10 holding of mutual fund available and fund report updated quarterly with 15 days delay. Finally we build another low risk and balanced sparse portfolio that consistently outperform benchmark 930950.CSI.
Dynamic star-shaped risk measures and $g$-expectations
Authors: Dejian Tian, Xunlian Wang
Date: 2023-05-04 00:00:00

Motivated by the results of static monetary or star-shaped risk measures, the paper investigates the representation theorems in the dynamic framework. We show that dynamic monetary risk measures can be represented as the lower envelope of a family of dynamic convex risk measures, and normalized dynamic star-shaped risk measures can be represented as the lower envelope of a family of normalized dynamic convex risk measures. The link between dynamic monetary risk measures and dynamic star-shaped risk measures are established. Besides, the sensitivity and time consistency problems are also studied. A specific normalized time consistent dynamic star-shaped risk measures induced by $ g $-expectations are illustrated and discussed in detail.
Tensor PCA from basis in tensor space
Authors: Claudio Turchetti, Laura Falaschetti
Date: 2023-04-11 00:00:00

The aim of this paper is to present a mathematical framework for tensor PCA. The proposed approach is able to overcome the limitations of previous methods that extract a low dimensional subspace by iteratively solving an optimization problem. The core of the proposed approach is the derivation of a basis in tensor space from a real self-adjoint tensor operator, thus reducing the problem of deriving a basis to an eigenvalue problem. Three different cases have been studied to derive: i) a basis from a self-adjoint tensor operator; ii) a rank-1 basis; iii) a basis in a subspace. In particular, the equivalence between eigenvalue equation for a real self-adjoint tensor operator and standard matrix eigenvalue equation has been proven. For all the three cases considered, a subspace approach has been adopted to derive a tensor PCA. Experiments on image datasets validate the proposed mathematical framework.
Towards antigenic peptide discovery with better MHC-I binding prediction and improved benchmark methodology
Authors: Stanisław Giziński, Grzegorz Preibisch, Piotr Kucharski, Michał Tyrolski, Michał Rembalski, Piotr Grzegorczyk, Anna Gambin
Date: 2023-05-05 00:00:00
Proceeding: Machine Learning for Drug Discovery Workshop, ICLR 2023 5
Tasks: Language Modeling, Language Modelling, MHC presentation prediction

The Major Histocompatibility Complex (MHC) is a crucial component of the cellular immune system in vertebrates, responsible for, among others, presenting peptides derived from intracellular proteins. The MHC-I presentation is vital in the immune response and holds great promise in vaccine development and cancer immunotherapy. In this study, we analyze the limitations of existing methods and benchmarks for MHC-I presentation. We introduce a new benchmark to measure crucial generalization properties and models’ reliability on unseen MHC molecules and peptides. Finally, we present HLABERT, a pre-trained language model which significantly surpasses prior methods on our benchmark and also sets new state-of-the-art on the old benchmarks.
Block Attention and Switchable Normalization Based Deep Learning Framework for Segmentation of Retinal Vessels
Authors: Sabri Deari, Ilkay Oksuz, Sezer Ulukaya
Date: 2023-04-10 00:00:00
Proceeding: IEEE Access 2023 4
Tasks: Retinal Vessel Segmentation, Segmentation

The presence of high blood sugar levels damages blood vessels and causes an eye condition called diabetic retinopathy. The ophthalmologist can detect this disease by looking at the variations in retinal blood vasculature. Manual segmentation of vessels requires highly skilled specialists, and not possible for many patients to be done quickly in their daily routine. For these reasons, it is of great importance to isolate retinal vessels precisely, quickly, and accurately. The difficulty distinguishing the retinal vessels from the background, and the small number of samples in the databases make this segmentation problem difficult. In this study, we propose a novel network called Block Feature Map Distorted Switchable Normalization U-net with Global Context Informative Convolutional Block Attention Module (BFMD SN U-net with GCI- CBAM). We improve the traditional Fully Convolutional Segmentation Networks in multiple aspects with the proposed model as follows; The model converges in earlier epochs, adapts more flexibly to different data, is more robust against overfitting, and gets better feature refinement at different dilation rates to cope with different sizes of retinal vessels. We evaluate the proposed network on two reference retinal datasets, DRIVE and CHASE DB1, and achieve state-of-the-art performances with 97.00 % accuracy and 98.71 % AUC in DRIVE and 97.62 % accuracy and 99.11 % AUC on CHASE DB1 databases. Additionally, the convergence step of the model is reduced and it has fewer parameters than the baseline U-net. In summary, the proposed model surpasses the U-net -based approaches used for retinal vessel separation in the literature.
Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision
Authors: Chinmay Belthangady, Will Stedden, Beau Norgeot
Date: 2021-09-20 00:00:00
Proceeding: BMC Medical Research Methodology 2021 9
Tasks: Causal Inference, Diversity

Observational studies are increasingly being used to provide supplementary evidence in addition to Randomized Control Trials (RCTs) because they provide a scale and diversity of participants and outcomes that would be infeasible in an RCT. Additionally, they more closely reflect the settings in which the studied interventions will be applied in the future. Well-established propensity-score-based methods exist to overcome the challenges of working with observational data to estimate causal effects. These methods also provide quality assurance diagnostics to evaluate the degree to which bias has been removed and the estimates can be trusted. In large medical datasets it is common to find the same underlying health condition being treated with a variety of distinct drugs or drug combinations. Conventional methods require a manual iterative workflow, making them scale poorly to studies with many intervention arms. In such situations, automated causal inference methods that are compatible with traditional propensity-score-based workflows are highly desirable.
CANet: Context aware network with dual-stream pyramid for medical image segmentation
Authors: Xie Xiwang, Zhang Weidong, Pan Xipeng, Xie Lijie, Shao Feng, Zhao Wenyi, An Jubai
Date: 2023-03-05 00:00:00
Proceeding: Biomedical Signal Processing and Control 2023 3
Tasks: 2D Semantic Segmentation, Decoder, Image Segmentation, Medical Image Segmentation, Object, Segmentation, Semantic Segmentation

Owing to the various object types and scales, complicated backgrounds, and similar appearance between tissues in medical images, it is difficult to extract some valuable information from different medical images. In this paper, we propose a context aware network with dual-stream pyramid (CANet) for medical image segmentation, which comprises a dual-stream pyramid module and an encoder–decoder module with context aware. Concretely, the dual-stream pyramid captures numerous complementary features at different layers by adopting multi-resolution input versions and multi-scale convolutional units, which is conductive to learning the local detail features in various scales. The encoder–decoder module with context aware progressively concatenates semantic features of the encoder branch with high-level features of the decoder branch in an efficient manner, which aims to suppress complicated backgrounds and highlight the most attractive object in medical images. Quantitative and qualitative experiments demonstrate that our CANet favorably performs against 13 state-of-the-art object segmentation methods on three publicly available medical image segmentation datasets. The code will be released at: https://github.com/Xie-Xiwang/BSPC2022_CANet.
Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior
Authors: Chenguang Li, Jia Shi, Ya Wang, Guangliang Cheng
Date: 2022-06-20 00:00:00
Proceeding: CVPR 2022 6
Tasks: 3D Lane Detection, Data Augmentation, Lane Detection

In this paper, we propose an advanced approach in targeting the problem of monocular 3D lane detection by leveraging geometry structure underneath the process of 2D to 3D lane reconstruction. Inspired by previous methods, we first analyze the geometry heuristic between the 3D lane and its 2D representation on the ground and propose to impose explicit supervision based on the structure prior, which makes it achievable to build inter-lane and intra-lane relationships to facilitate the reconstruction of 3D lanes from local to global. Second, to reduce the structure loss in 2D lane representation, we directly extract top view lane information from front view images, which tremendously eases the confusion of distant lane features in previous methods. Furthermore, we propose a novel task-specific data augmentation method by synthesizing new training data for both segmentation and reconstruction tasks in our pipeline, to counter the imbalanced data distribution of camera pose and ground slope to improve generalization on unseen data. Our work marks the first attempt to employ the geometry
prior information into DNN-based 3D lane detection and makes it achievable for detecting lanes in an extra-long distance, doubling the original detection range. The proposed method can be smoothly adopted by other frameworks without extra costs. Experimental results show that our work outperforms state-of-the-art approaches by 3.8% F-Score on Apollo 3D synthetic dataset at real-time speed of 82 FPS without introducing extra parameters.
Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation
Authors: Jorge Martinez-Gil
Date: 2023-05-05 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Sense Disambiguation

The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge. Therefore, unsupervised word sense disambiguation methods have been developed to overcome that challenge without relying on annotated data. This research proposes a new context-aware approach to unsupervised word sense disambiguation, which provides a flexible mechanism for incorporating contextual information into the similarity measurement process. We experiment with a popular benchmark dataset to evaluate the proposed strategy and compare its performance with state-of-the-art unsupervised word sense disambiguation techniques. The experimental results indicate that our approach substantially enhances disambiguation accuracy and surpasses the performance of several existing techniques. Our findings underscore the significance of integrating contextual information in semantic similarity measurements to manage word sense ambiguity in unsupervised scenarios effectively.
Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems
Authors: Zeyan Li, Junjie Chen, Yihao Chen, Chengyang Luo, Yiwei Zhao, Yongqian Sun, Kaixin Sui, Xiping Wang, Dapeng Liu, Xing Jin, Qi Wang, Dan Pei
Date: 2023-05-05 00:00:00
Tasks: Attribute, Fault Diagnosis, Heuristic Search

Localizing root causes for multi-dimensional data is critical to ensure online service systems' reliability. When a fault occurs, only the measure values within specific attribute combinations are abnormal. Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multidimensional data. This paper proposes a generic and robust root cause localization approach for multi-dimensional data, PSqueeze. We propose a generic property of root cause for multi-dimensional data, generalized ripple effect (GRE). Based on it, we propose a novel probabilistic cluster method and a robust heuristic search method. Moreover, we identify the importance of determining external root causes and propose an effective method for the first time in literature. Our experiments on two real-world datasets with 5400 faults show that the F1-score of PSqueeze outperforms baselines by 32.89%, while the localization time is around 10 seconds across all cases. The F1-score in determining external root causes of PSqueeze achieves 0.90. Furthermore, case studies in several production systems demonstrate that PSqueeze is helpful to fault diagnosis in the real world.
DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
Authors: Yunze Man, Liang-Yan Gui, Yu-Xiong Wang
Date: 2023-05-05 00:00:00
Tasks: Domain Adaptation

Closing the domain gap between training and deployment and incorporating multiple sensor modalities are two challenging yet critical topics for self-driving. Existing work only focuses on single one of the above topics, overlooking the simultaneous domain and modality shift which pervasively exists in real-world scenarios. A model trained with multi-sensor data collected in Europe may need to run in Asia with a subset of input sensors available. In this work, we propose DualCross, a cross-modality cross-domain adaptation framework to facilitate the learning of a more robust monocular bird's-eye-view (BEV) perception model, which transfers the point cloud knowledge from a LiDAR sensor in one domain during the training phase to the camera-only testing scenario in a different domain. This work results in the first open analysis of cross-domain cross-sensor perception and adaptation for monocular 3D tasks in the wild. We benchmark our approach on large-scale datasets under a wide range of domain shifts and show state-of-the-art results against various baselines.
LMEye: An Interactive Perception Network for Large Language Models
Authors: Yunxin Li, Baotian Hu, Xinyu Chen, Lin Ma, Yong Xu, Min Zhang
Date: 2023-05-05 00:00:00
Tasks: Language Modelling, Large Language Model, Multimodal Large Language Model, Question Answering

Training a Multimodal Large Language Model (MLLM) from scratch, like GPT-4, is resource-intensive. Regarding Large Language Models (LLMs) as the core processor for multimodal information, our paper introduces LMEye, a human-like eye with a play-and-plug interactive perception network, designed to enable dynamic interaction between LLMs and external vision information. Previous methods incorporate visual information into LLMs with a simple visual mapping network or Q-former from BLIP-2. Such networks project the image feature once yet do not consider the interaction between the image and the human input query. Hence, the obtained visual information without being connected to human intention may be inadequate for LLMs to generate intention-following responses, which we refer to as static visual information. LMEye addresses this issue by allowing the LLM to request the desired visual information aligned with various human instructions, which we term as the dynamic visual information interaction. Specifically, LMEye consists of a simple visual mapping network to provide the basic perception of an image for LLMs. It also contains additional modules responsible for acquiring requests from LLMs, performing request-based visual information interaction, and transmitting the resulting interacted visual information to LLMs, respectively. In this way, LLMs act to understand the human query, deliver the corresponding request to the request-based visual information interaction module, and generate the response based on the interleaved multimodal information. We evaluate LMEye through extensive experiments on some multimodal benchmarks, demonstrating that it significantly improves the zero-shot performance on various multimodal tasks compared to previous methods, with less parameters.
Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements
Authors: Jiacheng Liu, Wenya Wang, Dianzhuo Wang, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi
Date: 2023-05-05 00:00:00

Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures. We consider a retrospective verification approach that reflects on the correctness of LM outputs, and introduce Vera, a general-purpose model that estimates the plausibility of declarative statements based on commonsense knowledge. Trained on ~7M commonsense statements created from 19 QA datasets and two large-scale knowledge bases, and with a combination of three training objectives, Vera is a versatile model that effectively separates correct from incorrect statements across diverse commonsense domains. When applied to solving commonsense problems in the verification format, Vera substantially outperforms existing models that can be repurposed for commonsense verification, and it further exhibits generalization capabilities to unseen tasks and provides well-calibrated outputs. We find that Vera excels at filtering LM-generated commonsense knowledge and is useful in detecting erroneous commonsense statements generated by models like ChatGPT in real-world settings.
Mining bias-target Alignment from Voronoi Cells
Authors: Rémi Nahon, Van-Tam Nguyen, Enzo Tartaglione
Date: 2023-05-05 00:00:00
Proceeding: ICCV 2023 1
Tasks: Fairness

Despite significant research efforts, deep neural networks are still vulnerable to biases: this raises concerns about their fairness and limits their generalization. In this paper, we propose a bias-agnostic approach to mitigate the impact of bias in deep neural networks. Unlike traditional debiasing approaches, we rely on a metric to quantify ``bias alignment/misalignment'' on target classes, and use this information to discourage the propagation of bias-target alignment information through the network. We conduct experiments on several commonly used datasets for debiasing and compare our method to supervised and bias-specific approaches. Our results indicate that the proposed method achieves comparable performance to state-of-the-art supervised approaches, although it is bias-agnostic, even in presence of multiple biases in the same sample.
Causal Discovery with Stage Variables for Health Time Series
Authors: Bharat Srikishan, Samantha Kleinberg
Date: 2023-05-05 00:00:00
Tasks: Causal Discovery, Causal Inference, Time Series

Using observational data to learn causal relationships is essential when randomized experiments are not possible, such as in healthcare. Discovering causal relationships in time-series health data is even more challenging when relationships change over the course of a disease, such as medications that are most effective early on or for individuals with severe disease. Stage variables such as weeks of pregnancy, disease stages, or biomarkers like HbA1c, can influence what causal relationships are true for a patient. However, causal inference within each stage is often not possible due to limited amounts of data, and combining all data risks incorrect or missed inferences. To address this, we propose Causal Discovery with Stage Variables (CDSV), which uses stage variables to reweight data from multiple time-series while accounting for different causal relationships in each stage. In simulated data, CDSV discovers more causes with fewer false discoveries compared to baselines, in eICU it has a lower FDR than baselines, and in MIMIC-III it discovers more clinically relevant causes of high blood pressure.