pwc-archive/papers-with-abstracts huggingface dataset:
Inferring exemplar discriminability in brain representations
Authors: Hamed Nili, Alexander Walther, Arjen Alink, Nikolaus Kriegeskorte
Date: 2022-05-13 00:00:00
Tasks: Sensitivity, Specificity, valid

Representational distinctions within categories are important in all perceptual modalities and also in cognitive and motor representations. Recent pattern-information studies of brain activity have used condition-rich designs to sample the stimulus space more densely. To test whether brain response patterns discriminate among a set of stimuli (e.g. exemplars within a category) with good sensitivity, we can pool statistical evidence over all pairwise comparisons. Here we describe a wide range of statistical tests of exemplar discriminability and assess the validity (specificity) and power (sensitivity) of each test. The tests include previously used and novel, parametric and nonparametric tests, which treat subject as a random or fixed effect, and are based on different dissimilarity measures, different test statistics, and different inference procedures. We use simulated and real data to determine which tests are valid and which are most sensitive. A popular test statistic reflecting exemplar information is the exemplar discriminability index (EDI), which is defined as the average of the pattern dissimilarity estimates between different exemplars minus the average of the pattern dissimilarity estimates between repetitions of identical exemplars. The popular across-subject t test of the EDI (typically using correlation distance as the pattern dissimilarity measure) requires the assumption that the EDI is 0-mean normal under H0. Although this assumption is not strictly true, our simulations suggest that the test controls the false-positives rate at the nominal level, and is thus valid, in practice. However, test statistics based on average Mahalanobis distances or average linear-discriminant t values (both accounting for the multivariate error covariance among responses) are substantially more powerful for both random- and fixed-effects inference.
The Traill island model for lemming dynamics, how it compares to Fennoscandian vole dynamics models, and a proposed simplification
Authors: Frédéric Barraquand, John-André Henden, Olivier Gilg, Rolf A Ims, Nigel G Yoccoz
Date: 2022-05-19 00:00:00

The Traill island model of Gilg et al. (2003) is a landmark attempt at mechanistic modelling of the cyclic population dynamics of rodents, focusing on a high Arctic community. It models the dynamics of one prey, the collared lemming, and four predators : the stoat, the Arctic fox, the long-tailed skua and the snowy owl. In the present short note, we first summarize how the model works in light of theory on seasonally forced predator-prey systems, with a focus on the temporal dynamics of predation rates. We show notably how the impact of generalist predation, which is able here to initiate population declines, differs slightly from that of generalist predation in other mechanistic models of rodent-mustelid interactions such as Turchin & Hanski (1997). We then provide a low-dimensional approximation with a single generalist predator compartment that mimics the essential features of the Traill island model: cycle periodicity, amplitude, shape, as well as generalist-induced declines. This simpler model should be broadly applicable to model other lemming populations that predominantly grow under the snow during the winter period. Matlab computer codes for Gilg et al. (2003), its two-dimensional approximation, as well as alternative lemming population dynamics models are provided.
Slim: interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology
Authors: Chris Gorman, Davide Punzo, Igor Octaviano, Steve Pieper, William J. R. Longabaugh, David A. Clunie, Ron Kikinis, Andrey Y. Fedorov, Markus D. Herrmann
Date: 2022-05-18 00:00:00

The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. Slim is an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
Quadtree-accelerated Real-time Monocular Dense Mapping
Authors: Kaixuan Wang; Wenchao Ding; Shaojie Shen
Date: 2018-10-05 00:00:00
Proceeding: IROS 2018 10
Tasks: 3D Reconstruction

In this paper, we propose a novel mapping method for robotic navigation. High-quality dense depth maps are estimated and fused into 3D reconstructions in real-time using a single localized moving camera. The quadtree structure of the intensity image is used to reduce the computation burden by estimating the depth map in multiple resolutions. Both the quadtree-based pixel selection and the dynamic belief propagation are proposed to speed up the mapping process: pixels are selected and optimized with the computation resource according to their levels in the quadtree. Solved depth estimations are further interpolated and fused temporally into full resolution depth maps and fused into dense 3D maps using truncated signed distance function (TSDF). We compare our method with other state-of-the-art methods using the public datasets. Onboard UAV autonomous flight is also used to further prove the usability and efficiency of our method on portable devices. For the benefit of the community, the implementation is also released as open source at https://github.com/HKUST-Aerial-Robotics/open_quadtree_mapping.
EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment
Authors: Ziwei Luo, Lei Yu, Xuan Mo, Youwei Li, Lanpeng Jia, Haoqiang Fan, Jian Sun, Shuaicheng Liu
Date: 2021-06-15 00:00:00
Proceeding: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2021 6
Tasks: Burst Image Reconstruction, Burst Image Super-Resolution, Multi-Frame Super-Resolution, Super-Resolution

We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR). The proposed framework is known as Enhanced Burst Super-Resolution (EBSR), which divides the MFSR problem into three parts: alignment, fusion, and reconstruction. We propose a Feature Enhanced Pyramid Cascading and Deformable convolution (FEPCD) module to align multiple low-resolution burst images in the feature level. And then the aligned features are fused by a Cross Non-Local Fusion (CNLF) module. Finally, the SR image is reconstructed by the Long Range Concatenation Network (LRCN). In addition, we build a cascading residual pathway structure (CR) to improve the performance. We conduct several experiments to analyze and demonstrate these modules. Our EBSR model won the champion in the real track and second place in the synthetic track in the NTIRE21 Burst Super-Resolution Challenge.
Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion
Authors: Hao Zou, Xuemeng Yang, Tianxin Huang, Chujuan Zhang, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
Date: 2021-09-27 00:00:00
Proceeding: IEEE International Workshop on Intelligent Robots and Systems (IROS) 2021 9
Tasks: 3D Semantic Scene Completion, Autonomous Driving, Decoder

An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems. In this paper, we focus on semantic scene completion, which is a task of jointly estimating the volumetric occupancy and semantic labels of objects. Since the real-world data is sparse and occluded, this is an extremely challenging task. We propose a novel framework, named Up-to-Down network (UDNet), to achieve the large-scale semantic scene completion with an encoder-decoder architecture for voxel grids. The novel up-to-down block can effectively aggregate multi-scale context information to improve labeling coherence, and the atrous spatial pyramid pooling module is leveraged to expand the receptive field while preserving detailed geometric information. Besides, the proposed multi-scale fusion mechanism efficiently aggregates global background information and improves the semantic completion accuracy. Moreover, to further satisfy the needs of different tasks, our UDNet can accomplish the multi-resolution semantic completion, achieving faster but coarser completion. Detailed experiments in the semantic scene completion benchmark of SemanticKITTI illustrate that our proposed framework surpasses the state-of-the-art methods with remarkable margins and a real-time inference speed by using only voxel grids as input.
Cancelable Biometric Template Generation using Convolutional Autoencoder
Authors: Gourav Siddhad, Pritee Khanna, Aparajita Ojha
Date: 2021-03-26 00:00:00
Proceeding: Computer Vision and Image Processing 2021 3

Convolutional autoencoders are a great tool for extracting features from images and compressing them to a lower dimension called latent space. A latent space vector is generated from the input images by extracting the relevant and the most useful features required for approximating the images. In the proposed work, a convolutional autoencoder is used for feature extraction, random noise and random convolution are used for generating cancelable template from these features. This architecture has been trained for palm vein, wrist vein, and palm print images combined from different datasets namely, CASIA, CIEPUT, and PolyU. The proposed method has been experimented and evaluated for various modalities such as palm print, palm vein, and wrist vein. The evaluation of these methods has been done in three different scenarios for addressing different uses and attacks possible.
Semantic Point Completion Network for 3D Semantic Scene Completion
Authors: Min Zhong, Gang Zeng
Date: 2020-08-29 00:00:00
Proceeding: ECAI 2020 8
Tasks: 3D Semantic Scene Completion, Decoder, Semantic Segmentation

Semantic scene completion (SSC) is composed of scene
completion (SC) and semantic segmentation. Most of the existing
methods carry out SSC in a regular 3D grid space, where 3D CNNs
cause unnecessary computational cost on empty voxels. In this work,
a Semantic Point Completion Network (SPCNet) is proposed to
address SSC in the point cloud space. Specifically, SPCNet is an
Encoder-decoder architecture, in which an Observed Point Encoder
is applied to extract the features of observed points, and an Observed
to Occluded Point Decoder is responsible for mapping the features
to the occluded points. Based on the SPCNet, we further introduce
an Image-point Fused Semantic Point Completion Network (IPFSPCNet), which aims to boost the performance of SSC by combining
the texture with geometry information. Evaluations are conducted on
two public datasets. Experimental results show that our method can
address the SC problem in the point cloud space. Compared to stateof-the-art approaches, our method can achieve satisfying results on
the SSC task.
Real-time prediction of nuclear power plant parameter trends following operator actions
Authors: Junyong Bae, Geunhee Kim, Seung Jun Lee
Date: 2021-11-05 00:00:00
Proceeding: Expert Systems with Applications 2021 11
Tasks: Prediction

Operators in the main control room of a nuclear power plant (NPP) oversee all plant operations, and thus any 
human error committed by the operators can be critical. If the operators can be informed about the future trends 
of the significant plant parameters that will follow their actions, they will be able to detect a human error in a 
short time or even prevent it. Future parameter trends, in addition, can be used to confirm the appropriate 
operational plan and support accident diagnosis. To achieve fast and accurate future parameter trend prediction 
in NPPs, we propose a data-driven prediction model composed of a multi-step prediction strategy and artificial 
neural networks. To find the optimal model performance, we applied a multilayered perceptron, vanilla recur-
rent neural network, and long short-term memory (LSTM) network, and trained the various candidate models 
with emergency operation data generated from an NPP simulator. Application results showed that the prediction 
model with the multi-input multi-output strategy and LSTM networks was able to successfully address the 
multivariate problem of future parameter trend estimation considering operator action in multiple emergency 
situations. It is believed that the proposed model may support NPP operators in coping with human errors and 
diagnosing accidents.
Automated Scoring for Reading Comprehension via In-context BERT Tuning
Authors: Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard Baraniuk, Andrew Lan
Date: 2022-05-19 00:00:00
Tasks: Reading Comprehension

Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring often leverage textual representations based on pre-trained language models such as BERT and GPT as input to scoring models. Most existing approaches train a separate model for each item/question, which is suitable for scenarios such as essay scoring where items can be quite different from one another. However, these approaches have two limitations: 1) they fail to leverage item linkage for scenarios such as reading comprehension where multiple items may share a reading passage; 2) they are not scalable since storing one model per item becomes difficult when models have a large number of parameters. In this paper, we report our (grand prize-winning) solution to the National Assessment of Education Progress (NAEP) automated scoring challenge for reading comprehension. Our approach, in-context BERT fine-tuning, produces a single shared scoring model for all items with a carefully-designed input structure to provide contextual information on each item. We demonstrate the effectiveness of our approach via local evaluations using the training dataset provided by the challenge. We also discuss the biases, common error types, and limitations of our approach.
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation
Authors: Hao liu, Y ulan Guo, Y anni Ma, Yinjie Lei, and Gongjian Wen
Date: 2020-08-01 00:00:00
Proceeding: IEEE 2020 8
Tasks: Image Segmentation, Point Cloud Segmentation, Segmentation, Semantic Segmentation

Semantic context plays a significant role in image segmentation. However, few prior works have explored semantic contexts for 3D point cloud segmentation. In this paper, we propose a simple yet effective Point Context Encoding (PointCE) module to capture semantic contexts of a point cloud and adaptively highlight intermediate feature maps. We also introduce a Semantic Context Encoding loss (SCE-loss) to supervise the network to learn rich semantic context features. To avoid hyperparameter tuning and achieve better convergence performance, we further propose a geometric mean loss to integrate both SCE-loss and segmentation loss. Our PointCE module is general and lightweight, and can be integrated into any point cloud segmentation architecture to improve its segmentation performance with only marginal extra overheads. Experimental results on the ScanNet, S3DIS and Semantic3D datasets show that consistent and significant improvement can be achieved for several different networks by integrating our PointCE module.
Global context reasoning for semantic segmentation of 3d point clouds
Authors: Y anni Ma, Y ulan Guo, Hao liu, Yinjie Lei
Date: 2020-05-14 00:00:00
Proceeding: IEEE 2020 5
Tasks: Segmentation, Semantic Segmentation

Global contextual dependency is important for semantic segmentation of 3D point clouds. However, most existing approaches stack feature extraction layers to enlarge the receptive field to aggregate more contextual information of points along the spatial dimension. In this paper, we propose a Point Global Context Reasoning (PointGCR) module to capture global contextual information along the channel dimension. In PointGCR, an undirected graph representation (namely, ChannelGraph) is used to learn channel independencies. Specifically, channel maps are first represented as graph nodes and the independencies between nodes are then represented as graph edges. PointGCR is a plug-andplay and end-to-end trainable module. It can easily be integrated into an existing segmentation network and achieves a significant performance improvement. We conduct extensive experiments to evaluate the proposed PointGCR module on both indoor and outdoor datasets. Experimental results show that our PointGCR module efficiently captures global contextual dependencies and significantly improve the segmentation performance of several existing networks.
PGDP5K: A Diagram Parsing Dataset for Plane Geometry Problems
Authors: Yihan Hao, Mingliang Zhang, Fei Yin, Linlin Huang
Date: 2022-05-20 00:00:00
Tasks: Geometry Problem Solving

Diagram parsing is an important foundation for geometry problem solving, attracting increasing attention in the field of intelligent education and document image understanding. Due to the complex layout and between-primitive relationship, plane geometry diagram parsing (PGDP) is still a challenging task deserving further research and exploration. An appropriate dataset is critical for the research of PGDP. Although some datasets with rough annotations have been proposed to solve geometric problems, they are either small in scale or not publicly available. The rough annotations also make them not very useful. Thus, we propose a new large-scale geometry diagram dataset named PGDP5K and a novel annotation method. Our dataset consists of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types. Different from previous datasets, our PGDP5K dataset is labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships. What is more, combined with above annotations and geometric prior knowledge, it can generate intelligible geometric propositions automatically and uniquely. We performed experiments on PGDP5K and IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves only 66.07% F1 value. This shows that PGDP5K presents a challenge for future research. Our dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-PGDP5K/.
MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Authors: dianhai yu, Liang Shen, Hongxiang Hao, Weibao Gong, HuaChao Wu, Jiang Bian, LiRong Dai, Haoyi Xiong
Date: 2022-05-20 00:00:00
Tasks: CPU, Distributed Computing, GPU, Mixture-of-Experts

While modern internet services, such as chatbots, search engines, and online advertising, demand the use of large-scale deep neural networks (DNNs), distributed training and inference over heterogeneous computing systems are desired to facilitate these DNN models. Mixture-of-Experts (MoE) is one the most common strategies to lower the cost of training subject to the overall size of models/data through gating and parallelism in a divide-and-conquer fashion. While DeepSpeed has made efforts in carrying out large-scale MoE training over heterogeneous infrastructures, the efficiency of training and inference could be further improved from several system aspects, including load balancing, communication/computation efficiency, and memory footprint limits. In this work, we present a novel MoESys that boosts efficiency in both large-scale training and inference. Specifically, in the training procedure, the proposed MoESys adopts an Elastic MoE training strategy with 2D prefetch and Fusion communication over Hierarchical storage, so as to enjoy efficient parallelisms. For scalable inference in a single node, especially when the model size is larger than GPU memory, MoESys builds the CPU-GPU memory jointly into a ring of sections to load the model, and executes the computation tasks across the memory sections in a round-robin manner for efficient inference. We carried out extensive experiments to evaluate MoESys, where MoESys successfully trains a Unified Feature Optimization (UFO) model with a Sparsely-Gated Mixture-of-Experts model of 12B parameters in 8 days on 48 A100 GPU cards. The comparison against the state-of-the-art shows that MoESys outperformed DeepSpeed with 33% higher throughput (tokens per second) in training and 13% higher throughput in inference in general. Particularly, under unbalanced MoE Tasks, e.g., UFO, MoESys achieved 64% higher throughput with 18% lower memory footprints.
Towards Understanding Grokking: An Effective Theory of Representation Learning
Authors: Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
Date: 2022-05-20 00:00:00
Tasks: Memorization, Representation Learning

We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set. We present both a microscopic analysis anchored by an effective theory and a macroscopic analysis of phase diagrams describing learning performance across hyperparameters. We find that generalization originates from structured representations whose training dynamics and dependence on training set size can be predicted by our effective theory in a toy setting. We observe empirically the presence of four learning phases: comprehension, grokking, memorization, and confusion. We find representation learning to occur only in a "Goldilocks zone" (including comprehension and grokking) between memorization and confusion. We find on transformers the grokking phase stays closer to the memorization phase (compared to the comprehension phase), leading to delayed generalization. The Goldilocks phase is reminiscent of "intelligence from starvation" in Darwinian evolution, where resource limitations drive discovery of more efficient solutions. This study not only provides intuitive explanations of the origin of grokking, but also highlights the usefulness of physics-inspired tools, e.g., effective theories and phase diagrams, for understanding deep learning.