pwc-archive/papers-with-abstracts huggingface dataset:
Inference and Sampling of Point Processes from Diffusion Excursions
Authors: Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh
Date: 2023-06-01 00:00:00
Tasks: Point Processes

Point processes often have a natural interpretation with respect to a continuous process. We propose a point process construction that describes arrival time observations in terms of the state of a latent diffusion process. In this framework, we relate the return times of a diffusion in a continuous path space to new arrivals of the point process. This leads to a continuous sample path that is used to describe the underlying mechanism generating the arrival distribution. These models arise in many disciplines, such as financial settings where actions in a market are determined by a hidden continuous price or in neuroscience where a latent stimulus generates spike trains. Based on the developments in It\^o's excursion theory, we propose methods for inferring and sampling from the point process derived from the latent diffusion process. We illustrate the approach with numerical examples using both simulated and real data. The proposed methods and framework provide a basis for interpreting point processes through the lens of diffusions.
A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation
Authors: Jian Ding, Zhangsong Li
Date: 2023-06-01 00:00:00
Tasks: Graph Matching

We propose an efficient algorithm for matching two correlated Erd\H{o}s--R\'enyi graphs with $n$ vertices whose edges are correlated through a latent vertex correspondence. When the edge density $q= n^{- \alpha+o(1)}$ for a constant $\alpha \in [0,1)$, we show that our algorithm has polynomial running time and succeeds to recover the latent matching as long as the edge correlation is non-vanishing. This is closely related to our previous work on a polynomial-time algorithm that matches two Gaussian Wigner matrices with non-vanishing correlation, and provides the first polynomial-time random graph matching algorithm (regardless of the regime of $q$) when the edge correlation is below the square root of the Otter's constant (which is $\approx 0.338$).
On Mixing Rates for Bayesian CART
Authors: Jungeum Kim, Veronika Rockova
Date: 2023-05-31 00:00:00
Tasks: Bayesian Inference

The success of Bayesian inference with MCMC depends critically on Markov chains rapidly reaching the posterior distribution. Despite the plentitude of inferential theory for posteriors in Bayesian non-parametrics, convergence properties of MCMC algorithms that simulate from such ideal inferential targets are not thoroughly understood. This work focuses on the Bayesian CART algorithm which forms a building block of Bayesian Additive Regression Trees (BART). We derive upper bounds on mixing times for typical posteriors under various proposal distributions. Exploiting the wavelet representation of trees, we provide sufficient conditions for Bayesian CART to mix well (polynomially) under certain hierarchical connectivity restrictions on the signal. We also derive a negative result showing that Bayesian CART (based on simple grow and prune steps) cannot reach deep isolated signals in faster than exponential mixing time. To remediate myopic tree exploration, we propose Twiggy Bayesian CART which attaches/detaches entire twigs (not just single nodes) in the proposal distribution. We show polynomial mixing of Twiggy Bayesian CART without assuming that the signal is connected on a tree. Going further, we show that informed variants achieve even faster mixing. A thorough simulation study highlights discrepancies between spike-and-slab priors and Bayesian CART under a variety of proposals.
VMap: An Interactive Rectangular Space-filling Visualization for Map-like Vertex-centric Graph Exploration
Authors: Jiayi Xu, Han-Wei Shen
Date: 2023-05-31 00:00:00
Tasks: Layout Generation

We present VMap, a map-like rectangular space-filling visualization, to perform vertex-centric graph exploration. Existing visualizations have limited support for quality optimization among rectangular aspect ratios, vertex-edge intersection, and data encoding accuracy. To tackle this problem, VMap integrates three novel components: (1) a desired-aspect-ratio (DAR) rectangular partitioning algorithm, (2) a two-stage rectangle adjustment algorithm, and (3) a simulated annealing based heuristic optimizer. First, to generate a rectangular space-filling layout of an input graph, we subdivide the 2D embedding of the graph into rectangles with optimization of rectangles' aspect ratios toward a desired aspect ratio. Second, to route graph edges between rectangles without vertex-edge occlusion, we devise a two-stage algorithm to adjust a rectangular layout to insert border space between rectangles. Third, to produce and arrange rectangles by considering multiple visual criteria, we design a simulated annealing based heuristic optimization to adjust vertices' 2D embedding to support trade-offs among aspect ratio quality and the encoding accuracy of vertices' weights and adjacency. We evaluated the effectiveness of VMap on both synthetic and application datasets. The resulting rectangular layout has better aspect ratio quality on synthetic data compared with the existing method for the rectangular partitioning of 2D points. On three real-world datasets, VMap achieved better encoding accuracy and attained faster generation speed compared with existing methods on graphs' rectangular layout generation. We further illustrate the usefulness of VMap for vertex-centric graph exploration through three case studies on visualizing social networks, representing academic communities, and displaying geographic information.
OVQA: A Clinically Generated Visual Question Answering Dataset
Authors: Yefan Huang, Xiaoli Wang, Feiyan Liu, Guofeng Huang
Date: 2022-07-07 00:00:00
Proceeding: SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval 2022 7
Tasks: Benchmarking, Medical Visual Question Answering, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Medical visual question answering (Med-VQA) is a challenging problem that aims to take a medical image and a clinical question about the image as input and output a correct answer in natural language. Current medical systems often require large-scale and high-quality labeled data for training and evaluation. To address the challenge, we present a new dataset, denoted by OVQA, which is generated from electronic medical records. We develop a semi-automatic data generation tool for constructing the dataset. First, medical entities are automatically extracted from medical records and filled into predefined templates for generating question and answer pairs. These pairs are then combined with medical images extracted from corresponding medical records, to generate candidates for visual question answering (VQA). The candidates are finally verified with high-quality labels annotated by experienced physicians. To evaluate the quality of OVQA, we conduct comprehensive experiments on state-of-the-art methods for the Med-VQA task to our dataset. The results show that our OVQA can be used as a benchmarking dataset for evaluating existing Med-VQA systems. The dataset can be downloaded from http://47.94.174.82/.
A dataset of clinically generated visual questions and answers about radiology images
Authors: Jason J. Lau, Soumya Gayen, Asma Ben Abacha, Dina Demner-Fushman
Date: 2018-11-20 00:00:00
Proceeding: Scientific Data 2018 11
Tasks: Decision Making, Medical Visual Question Answering, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area of artificial intelligence, Visual Question Answering (VQA) in the medical domain explores approaches to this form of clinical decision support. Success of such machine learning tools hinges on availability and design of collections composed of medical images augmented with question-answer pairs directed at the content of the image. We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers. Manual categorization of images and questions provides insight into clinically relevant tasks and the natural language to phrase them. Evaluating with well-known algorithms, we demonstrate the rich quality of this dataset over other automatically constructed ones. We propose VQA-RAD to encourage the community to design VQA tools with the goals of improving patient care.
SetGNER: General Named Entity Recognition as Entity Set Generation
Authors: Yuxin He, Buzhou Tang
Date: 2022-12-11 00:00:00
Proceeding: Empirical Methods in Natural Language Processing 2022 12
Tasks: named-entity-recognition, Named Entity Recognition, NER, Sentence

Recently, joint recognition of flat, nested and discontinuous entities has received increasing attention. Motivated by the observation that the target output of NER is essentially a set of sequences, we propose a novel entity set generation framework for general NER scenes in this paper. Different from sequence-to-sequence NER methods, our method does not force the entities to be generated in a predefined order and can get rid of the problem of error propagation and inefficient decoding. Distinguished from the set-prediction NER framework, our method treats each entity as a sequence and is capable of recognizing discontinuous mentions. Given an input sentence, the model first encodes the sentence in word-level and detects potential entity mentions based on the encoder’s output, then reconstructs entity mentions from the detected entity heads in parallel. To let the encoder of our model capture better right-to-left semantic structure, we also propose an auxiliary Inverse Generation Training task. Extensive experiments show that our model (w/o. Inverse Generation Training) outperforms state-of-the-art generative NER models by a large margin on two discontinuous NER datasets, two nested NER datasets and one flat NER dataset. Besides, the auxiliary Inverse Generation Training task is found to further improve the model’s performance on the five datasets.
pgMAP: a pipeline to enable guide RNA read mapping from dual-targeting CRISPR screens
Authors: Phoebe C. R. Parrish, Daniel J. Groso, James D. Thomas, Robert K. Bradley, Alice H. Berger
Date: 2023-06-01 00:00:00

We developed pgMAP, an analysis pipeline to map gRNA sequencing reads from dual-targeting CRISPR screens. pgMAP output includes a dual gRNA read counts table and quality control metrics including the proportion of correctly-paired reads and CRISPR library sequencing coverage across all time points and samples. pgMAP is implemented using Snakemake and is available open-source under the MIT license at https://github.com/fredhutch/pgmap_pipeline.
Towards Interpretable Seizure Detection Using Wearables
Authors: Irfan Al-Hussaini, Cassie S. Mitchell
Date: 2023-05-05 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023 5
Tasks: EEG, Management, Seizure Detection

Seizure detection using machine learning is a critical problem for the timely intervention and management of epilepsy. We propose SeizFt, a robust seizure detection framework using EEG from a wearable device. It uses features paired with an ensemble of trees, thus enabling further interpretation of the model’s results. The efficacy of the underlying augmentation and class-balancing strategy is also demonstrated. This study was performed for the Seizure Detection Challenge 2023, an ICASSP Grand Challenge.
A coarse-to-fine approach for dynamic-to-static image translation
Authors: Teng Wang, Lin Wu, Changyin Sun
Date: 2021-10-25 00:00:00
Proceeding: Pattern Recognition 2021 10
Tasks: Image Inpainting, Image-to-Image Translation, Translation, Visual Place Recognition

Dynamic-to-static image translation aims to convert the dynamic scene into static so that dynamic elements are eliminated from the image. Recent works typically see the problem as an image-to-image translation task, and perform the learned feature mapping over the whole dynamic image to synthesize the static image, which leads to unnecessary detail loss in original static regions. To that end, we delicately formulate it as an image inpainting-like problem to fill the missing static pixels in dynamic regions while retaining original static regions. We achieve this by proposing a coarse-to-fine framework. At coarse stage, we utilize a simple encoder-decoder network to rough out the static image. Using the coarse predicted image, we explicitly infer a more accurate dynamic mask to identify both dynamic objects and their shadows, so that the task could be effectively converted to an image inpainting problem. At fine stage, we recover the missing static pixels in the estimated dynamic regions on the basis of their coarse predictions. We enhance the coarse predicted contents by proposing a mutual texture-structure attention module, which enables the dynamic regions to borrow textures and structures separately from distant locations based on contextual similarity. Several losses are combined as the training objective function to generate excellent results with global consistency and fine details. Qualitative and quantitative experiments verify the superiority of our method in restoring high-quality static contents over state-of-the-art models. In addition, we evaluate the usefulness of the recovered static images by using them as query images to improve visual place recognition in dynamic scenes.
Process Parameter Selection for Production of Stainless Steel 316L Using Efficient Multi-Objective Bayesian Optimization Algorithm
Authors: Chepiga Timur, Zhilyaev Petr, Ryabov Alexander, Simonov Alexey P, Dubinin Oleg N, Firsov Denis G, Kuzminova Yulia O, Evlashin Stanislav A
Date: 2023-01-25 00:00:00
Proceeding: Materials 2023 1
Tasks: Bayesian Optimization

Additive manufacturing is a modern technique to produce parts with a complex geometry. However, the choice of the printing parameters is a time-consuming and costly process. In this study, the parameter optimization for the laser powder bed fusion process was investigated. Using state-of-the art multi-objective Bayesian optimization, the set of the most-promising process parameters (laser power, scanning speed, hatch distance, etc.), which would yield parts with the desired hardness and porosity, was established. The Gaussian process surrogate model was built on 57 empirical data points, and through efficient sampling in the design space, we were able to obtain three points in the Pareto front in just over six iterations. The produced parts had a hardness ranging from 224–235 HV and a porosity in the range of 0.2–0.37%. The trained model recommended using the following parameters for high-quality parts: 58 W, 257 mm/s, 45 µm, with a scan rotation angle of 131 degrees. The proposed methodology greatly reduces the number of experiments, thus saving time and resources. The candidate process parameters prescribed by the model were experimentally validated and tested.
Disjoint-CNN for Multivariate Time Series Classification
Authors: Navid Mohammadi Foumani, Chang Wei Tan, Mahsa Salehi
Date: 2022-01-20 00:00:00
Proceeding: 2021 International Conference on Data Mining Workshops (ICDMW) 2022 1
Tasks: Classification, Deep Learning, Time Series, Time Series Classification

Time series classification algorithms have been mainly dominated by non-deep learning models.
Deep learning for Multivariate Time Series Classification (MTSC) has gained huge interest in recent years. 
Most state-of-the-art deep learning methods are convolutional-based where 1-dimensional (1D) convolutions are used to extract features from the 2-dimensional time series. This study shows that factorization of 1D convolution filters into disjoint temporal and spatial components yields significant accuracy improvements with almost no additional computational cost.
Based on our study on disjoint temporal-spatial filters, we have designed a novel filter block called ``1+1D", which emphasizes the interaction between dimensions to improve the model performance of the convolution-based on deep learning MTSC models. We also proposed a new and effective MTSC method called Disjoint-CNN using our proposed 1+1D filter blocks and through our extensive experiments show that our model (called Disjoint-CNN) outperforms the state-of-the-art MTSC models on 26 datasets in the UEA Multivariate time series archive, achieving the highest average rank among 9 MTSC benchmark models.
A Deep Multiscale Framework for Video Watermarking
Authors: Xiyang Luo1, Yinxiao Li1, Huiwen Chang1, Ce Liu1, Peyman Milanfar1, and Feng Yang
Date: 2023-03-28 00:00:00
Proceeding: IEEE Transactions on Image Processing 2023 3
Tasks: Video Editing

Video watermarking embeds a message into a cover
video in an imperceptible manner, which can be retrieved
even if the video undergoes certain modifications or distor￾tions. Traditional watermarking methods are often manu￾ally designed for particular types of distortions and thus
cannot simultaneously handle a broad spectrum of distor￾tions. To this end, we propose a robust deep learning-based
solution for video watermarking that is end-to-end train￾able. Our model consists of a novel multiscale design where
the watermarks are distributed across multiple spatial￾temporal scales. It gains robustness against various dis￾tortions through a differentiable distortion layer, whereas
non-differentiable distortions, such as popular video com￾pression standards, are modeled by a differentiable proxy.
Extensive evaluations on a wide variety of distortions show
that our method outperforms traditional video watermark￾ing methods as well as deep image watermarking models by
a large margin. We further demonstrate the practicality of
our method on a realistic video-editing application.
Improvement of e-commerce recommendation systems with deep hybrid collaborative filtering with content: A case study
Authors: Filip Wójcik, Michał Górnik
Date: 2020-11-24 00:00:00
Proceeding: Econometrics. Ekonometria. Advances in Applied Data Analysis 2020 11
Tasks: Collaborative Filtering, Recommendation Systems

This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with Content (DHCF) as a product recommendation engine. Its main goal is to provide better shopping suggestions for customers on the e-commerce platform. The system was tested on 2018 Amazon Reviews Dataset, using repeated cross validation and compared with other approaches: collaborative filtering (CF) and deep collaborative filtering (DCF) in terms of mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). DCF and DHCF were proved to be significantly better than the CF. DHCF proved to be better than DCF in terms of MAE and MAPE, it also scored the best on separate test data. The significance of the differences was checked by means of a Friedman test, followed by post-hoc comparisons to control p-value. The experiment shows that DHCF can outperform other approaches considered in the study, with more robust scores.
TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers
Authors: Julian Moosmann, Marco Giordano, Christian Vogt, Michele Magno
Date: 2023-05-22 00:00:00
Tasks: Object, object-detection, Object Detection, Quantization, Real-Time Object Detection

This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrollers in the power domain of milliwatts, with less than 0.5MB memory available for storing convolutional neural network (CNN) weights. The proposed quantized network architecture with 422k parameters, enables real-time object detection on embedded microcontrollers, and it has been evaluated to exploit CNN accelerators. In particular, the proposed network has been deployed on the MAX78000 microcontroller achieving high frame-rate of up to 180fps and an ultra-low energy consumption of only 196{\mu}J per inference with an inference efficiency of more than 106 MAC/Cycle. TinyissimoYOLO can be trained for any multi-object detection. However, considering the small network size, adding object detection classes will increase the size and memory consumption of the network, thus object detection with up to 3 classes is demonstrated. Furthermore, the network is trained using quantization-aware training and deployed with 8-bit quantization on different microcontrollers, such as STM32H7A3, STM32L4R9, Apollo4b and on the MAX78000's CNN accelerator. Performance evaluations are presented in this paper.
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)
Authors: Ruoxi Sun, Sercan Ö. Arik, Alex Muzio, Lesly Miculicich, Satya Gundabathula, Pengcheng Yin, Hanjun Dai, Hootan Nakhost, Rajarishi Sinha, Zifeng Wang, Tomas Pfister
Date: 2023-05-26 00:00:00
Tasks: Data Augmentation, In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Text to SQL, Text-To-SQL

Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially revolutionizing how humans interact with data. This paper introduces the SQL-PaLM framework, a comprehensive solution for understanding and enhancing Text-to-SQL using LLMs, using in the learning regimes of few-shot prompting and instruction fine-tuning. With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error filtering. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs. In particular, we investigate how performance can be improved through expanded training data coverage and diversity, synthetic data augmentation, and integrating query-specific database content. We propose a test-time selection method to further refine accuracy by integrating SQL outputs from multiple paradigms with execution feedback as guidance. Additionally, we tackle the practical challenge of navigating intricate databases with a significant number of tables and columns, proposing efficient techniques for accurately selecting relevant database elements to enhance Text-to-SQL performance. Our holistic approach yields substantial advancements in Text-to-SQL, as demonstrated on two key public benchmarks, Spider and BIRD. Through comprehensive ablations and error analyses, we shed light on the strengths and weaknesses of our framework, offering valuable insights into Text-to-SQL's future work.