OpenRaiser/Intern-Atlas huggingface dataset:
Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flows
Venue: arXiv cs.CV 2021
Year: 2021

Quantifying uncertainty in medical image segmentation applications is essential, as it is often connected to vital decision-making. Compelling attempts have been made in quantifying the uncertainty in image segmentation architectures, e.g. to learn a density segmentation model conditioned on the input image. Typical work in this field restricts these learnt densities to be strictly Gaussian. In this paper, we propose to use a more flexible approach by introducing Normalizing Flows (NFs), which enables the learnt densities to be more complex and facilitate more accurate modeling for uncertainty. We prove this hypothesis by adopting the Probabilistic U-Net and augmenting the posterior density with an NF, allowing it to be more expressive. Our qualitative as well as quantitative (GED and IoU) evaluations on the multi-annotated and single-annotated LIDC-IDRI and Kvasir-SEG segmentation datasets, respectively, show a clear improvement. This is mostly apparent in the quantification of aleatoric uncertainty and the increased predictive performance of up to 14 percent. This result strongly indicates that a more flexible density model should be seriously considered in architectures that attempt to capture segmentation ambiguity through density modeling. The benefit of this improved modeling will increase human confidence in annotation and segmentation, and enable eager adoption of the technology in practice.
Implementation of a Multi-Beam MAC Protocol for Multi-Hop Wireless Networks in Riverbed Modeler
Venue: arXiv cs.NI 2021
Year: 2021

Recent advances in antenna technology have made the design of multi-beam antennas (MBA) feasible. Compared to an omni-directional or a single beam directional antenna, an MBA equipped node can achieve a throughput of up to m times, by simultaneously communicating on its m non-interfering beams. As a result, a few multi-beam directional medium access control (MAC) schemes have been proposed in the literature recently, which are implemented mostly on the in-house simulation setups in Matlab or C/C++. These implementations make many assumptions to simplify their design, without a thorough implementation of other network layers. However, the implementation of a multi-beam MAC scheme on the well-known discrete event network simulator platforms (such as the Riverbed Modeler, NS3, QualNet) is challenging as it requires extensive changes and additions to various source code modules. In fact, the network protocols in these simulator packages have been mainly designed for omni-directional communication, and very few implementations of directional MAC and other network protocols exist in the literature. This paper presents a framework to implement a multi-beam directional MAC scheme in multi-hop wireless networks, by using the Wireless Suite of Riverbed Modeler. The detailed implementation procedures are described for multi-beam antenna module, multi-beam node model, concurrent packet transmission and reception, scheduling, collision avoidance, retransmission, and local node synchronization. These MAC modules and methodology can be very helpful to the researchers and developers for implementing the single-beam as well as multi-beam directional MAC and routing protocols in Riverbed Modeler.
BEANNA: A Binary-Enabled Architecture for Neural Network Acceleration
Venue: arXiv cs.AR 2021
Year: 2021

Modern hardware design trends have shifted towards specialized hardware acceleration for computationally intensive tasks like machine learning and computer vision. While these complex workloads can be accelerated by commercial GPUs, domain-specific hardware is far more optimal when needing to meet the stringent memory, throughput, and power constraints of mobile and embedded devices. This paper proposes and evaluates a Binary-Enabled Architecture for Neural Network Acceleration (BEANNA), a neural network hardware accelerator capable of processing both floating point and binary network layers. Through the use of a novel 16x16 systolic array based matrix multiplier with processing elements that compute both floating point and binary multiply-adds, BEANNA seamlessly switches between high precision floating point and binary neural network layers. Running at a clock speed of 100MHz, BEANNA achieves a peak throughput of 52.8 GigaOps/second when operating in high precision mode, and 820 GigaOps/second when operating in binary mode. Evaluation of BEANNA was performed by comparing a hybrid network with floating point outer layers and binary hidden layers to a network with only floating point layers. The hybrid network accelerated using BEANNA achieved a 194% throughput increase, a 68% memory usage decrease, and a 66% energy consumption decrease per inference, all this at the cost of a mere 0.23% classification accuracy decrease on the MNIST dataset.
Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines
Venue: arXiv cs.HC 2021
Year: 2021

Problem-driven visualization work is rooted in deeply understanding the data, actors, processes, and workflows of a target domain. However, an individual's personality traits and cognitive abilities may also influence visualization use. Diverse user needs and abilities raise natural questions for specificity in visualization design: Could individuals from different domains exhibit performance differences when using visualizations? Are any systematic variations related to their cognitive abilities? This study bridges domain-specific perspectives on visualization design with those provided by cognition and perception. We measure variations in visualization task performance across chemistry, computer science, and education, and relate these differences to variations in spatial ability. We conducted an online study with over 60 domain experts consisting of tasks related to pie charts, isocontour plots, and 3D scatterplots, and grounded by a well-documented spatial ability test. Task performance (correctness) varied with profession across more complex visualizations, but not pie charts, a comparatively common visualization. We found that correctness correlates with spatial ability, and the professions differ in terms of spatial ability. These results indicate that domains differ not only in the specifics of their data and tasks, but also in terms of how effectively their constituent members engage with visualizations and their cognitive traits. Analyzing participants' confidence and strategy comments suggests that focusing on performance neglects important nuances, such as differing approaches to engage with even common visualizations and potential skill transference. Our findings offer a fresh perspective on discipline-specific visualization with recommendations to help guide visualization design that celebrates the uniqueness of the disciplines and individuals we seek to serve.
Alleviating Mode Collapse in GAN via Diversity Penalty Module
Venue: arXiv cs.CV 2021
Year: 2021

The vanilla GAN (Goodfellow et al. 2014) suffers from mode collapse deeply, which usually manifests as that the images generated by generators tend to have a high similarity amongst them, even though their corresponding latent vectors have been very different. In this paper, we introduce a pluggable diversity penalty module (DPM) to alleviate mode collapse of GANs. It reduces the similarity of image pairs in feature space, i.e., if two latent vectors are different, then we enforce the generator to generate two images with different features. The normalized Gram matrix is used to measure the similarity. We compare the proposed method with Unrolled GAN (Metz et al. 2016), BourGAN (Xiao, Zhong, and Zheng 2018), PacGAN (Lin et al. 2018), VEEGAN (Srivastava et al. 2017) and ALI (Dumoulin et al. 2016) on 2D synthetic dataset, and results show that the diversity penalty module can help GAN capture much more modes of the data distribution. Further, in classification tasks, we apply this method as image data augmentation on MNIST, Fashion- MNIST and CIFAR-10, and the classification testing accuracy is improved by 0.24%, 1.34% and 0.52% compared with WGAN GP (Gulrajani et al. 2017), respectively. In domain translation, diversity penalty module can help StarGAN (Choi et al. 2018) generate more accurate attention masks and accelarate the convergence process. Finally, we quantitatively evaluate the proposed method with IS and FID on CelebA, CIFAR-10, MNIST and Fashion-MNIST, and the results suggest GAN with diversity penalty module gets much higher IS and lower FID compared with some SOTA GAN architectures.
Cable Driven Rehabilitation Robots: Comparison of Applications and Control Strategies
Venue: arXiv cs.RO 2021
Year: 2021

Significant attention has been paid to robotic rehabilitation using various types of actuator and power transmission. Amongst those, cable-driven rehabilitation robots (CDRRs) are relatively newer and their control strategies have been evolving in recent years. CDRRs offer several promising features, such as low inertia, lightweight, high payload-to-weight ratio, large work-space and configurability. In this paper, we categorize and review the cable-driven rehabilitation robots in three main groups concerning their applications for upper limb, lower limb, and waist rehabilitation. For each group, target movements are identified, and promising designs of CDRRs are analyzed in terms of types of actuators, controllers and their interactions with humans. Particular attention has been given to robots with verified clinical performance in actual rehabilitation settings. A large part of this paper is dedicated to comparing the control strategies and techniques of CDRRs under five main categories of: Impedance-based, PID-based, Admittance-based, Assist-as-needed (AAN) and Adaptive controllers. We have carefully contrasted the advantages and disadvantages of those methods with the aim of assisting the design of future CDRRs
TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding
Venue: arXiv cs.CV 2021
Year: 2021

Recently proposed fine-grained 3D visual grounding is an essential and challenging task, whose goal is to identify the 3D object referred by a natural language sentence from other distractive objects of the same category. Existing works usually adopt dynamic graph networks to indirectly model the intra/inter-modal interactions, making the model difficult to distinguish the referred object from distractors due to the monolithic representations of visual and linguistic contents. In this work, we exploit Transformer for its natural suitability on permutation-invariant 3D point clouds data and propose a TransRefer3D network to extract entity-and-relation aware multimodal context among objects for more discriminative feature learning. Concretely, we devise an Entity-aware Attention (EA) module and a Relation-aware Attention (RA) module to conduct fine-grained cross-modal feature matching. Facilitated by co-attention operation, our EA module matches visual entity features with linguistic entity features while RA module matches pair-wise visual relation features with linguistic relation features, respectively. We further integrate EA and RA modules into an Entity-and-Relation aware Contextual Block (ERCB) and stack several ERCBs to form our TransRefer3D for hierarchical multimodal context modeling. Extensive experiments on both Nr3D and Sr3D datasets demonstrate that our proposed model significantly outperforms existing approaches by up to 10.6% and claims the new state-of-the-art. To the best of our knowledge, this is the first work investigating Transformer architecture for fine-grained 3D visual grounding task.
Reachability-based Safe Planning for Multi-Vehicle Systems withMultiple Targets
Venue: arXiv cs.RO 2021
Year: 2021

Recently there have been a lot of interests in introducing UAVs for a wide range of applications, making ensuring safety of multi-vehicle systems a highly crucial problem. Hamilton-Jacobi (HJ) reachability is a promising tool for analyzing safety of vehicles for low-dimensional systems. However, reachability suffers from the curse of dimensionality, making its direct application to more than two vehicles intractable. Recent works have made it tractable to guarantee safety for 3 and 4 vehicles with reachability. However, the number of vehicles safety can be guaranteed for remains small. In this paper, we propose a novel reachability-based approach that guarantees safety for any number of vehicles while vehicles complete their objectives of visiting multiple targets efficiently, given any K-vehicle collision avoidance algorithm where K can in general be a small number. We achieve this by developing an approach to group vehicles into clusters efficiently and a control strategy that guarantees safety for any in-cluster and cross-cluster pair of vehicles for all time. Our proposed method is scalable to large number of vehicles with little computation overhead. We demonstrate our proposed approach with a simulation on 15 vehicles. In addition, we contribute a more general solution to the 3-vehicle collision avoidance problem from a past recent work, show that the prior work is a special case of our proposed generalization, and prove its validity.
Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment
Venue: arXiv cs.CV 2021
Year: 2021

Differentiation of colorectal polyps is an important clinical examination. A computer-aided diagnosis system is required to assist accurate diagnosis from colonoscopy images. Most previous studies at-tempt to develop models for polyp differentiation using Narrow-Band Imaging (NBI) or other enhanced images. However, the wide range of these models' applications for clinical work has been limited by the lagging of imaging techniques. Thus, we propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification (CPC) through directly using white-light (WL) colonoscopy images in the examination. In practice, during training, the auxiliary NBI images are utilized to train a teacher network and guide the student network to acquire richer feature representation from WL images. The feature transfer is realized by domain alignment and contrastive learning. Eventually the final student network has the ability to extract aligned features from only WL images to facilitate the CPC task. Besides, we release the first public-available paired CPC dataset containing WL-NBI pairs for the alignment training. Quantitative and qualitative evaluation indicates that the proposed method outperforms the previous methods in CPC, improving the accuracy by 5.6%with very fast speed.
Multi-clue reconstruction of sharing chains for social media images
Venue: arXiv cs.MM 2021
Year: 2021

The amount of multimedia content shared everyday, combined with the level of realism reached by recent fake-generating technologies, threatens to impair the trustworthiness of online information sources. The process of uploading and sharing data tends to hinder standard media forensic analyses, since multiple re-sharing steps progressively hide the traces of past manipulations. At the same time though, new traces are introduced by the platforms themselves, enabling the reconstruction of the sharing history of digital objects, with possible applications in information flow monitoring and source identification. In this work, we propose a supervised framework for the reconstruction of image sharing chains on social media platforms. The system is structured as a cascade of backtracking blocks, each of them tracing back one step of the sharing chain at a time. Blocks are designed as ensembles of classifiers trained to analyse the input image independently from one another by leveraging different feature representations that describe both content and container of the media object. Individual decisions are then properly combined by a late fusion strategy. Results highlight the advantages of employing multiple clues, which allow accurately tracing back up to three steps along the sharing chain.
Data Streaming and Traffic Gathering in Mesh-based NoC for Deep Neural Network Acceleration
Venue: arXiv cs.LG 2021
Year: 2021

The increasing popularity of deep neural network (DNN) applications demands high computing power and efficient hardware accelerator architecture. DNN accelerators use a large number of processing elements (PEs) and on-chip memory for storing weights and other parameters. As the communication backbone of a DNN accelerator, networks-on-chip (NoC) play an important role in supporting various dataflow patterns and enabling processing with communication parallelism in a DNN accelerator. However, the widely used mesh-based NoC architectures inherently cannot support the efficient one-to-many and many-to-one traffic largely existing in DNN workloads. In this paper, we propose a modified mesh architecture with a one-way/two-way streaming bus to speedup one-to-many (multicast) traffic, and the use of gather packets to support many-to-one (gather) traffic. The analysis of the runtime latency of a convolutional layer shows that the two-way streaming architecture achieves better improvement than the one-way streaming architecture for an Output Stationary (OS) dataflow architecture. The simulation results demonstrate that the gather packets can help to reduce the runtime latency up to 1.8 times and network power consumption up to 1.7 times, compared with the repetitive unicast method on modified mesh architectures supporting two-way streaming.
SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning has achieved great success in a wide spectrum of multimedia applications such as image classification, natural language processing and multimodal data analysis. Recent years have seen the development of many deep learning frameworks that provide a high-level programming interface for users to design models, conduct training and deploy inference. However, it remains challenging to build an efficient end-to-end multimedia application with most existing frameworks. Specifically, in terms of usability, it is demanding for non-experts to implement deep learning models, obtain the right settings for the entire machine learning pipeline, manage models and datasets, and exploit external data sources all together. Further, in terms of adaptability, elastic computation solutions are much needed as the actual serving workload fluctuates constantly, and scaling the hardware resources to handle the fluctuating workload is typically infeasible. To address these challenges, we introduce SINGA-Easy, a new deep learning framework that provides distributed hyper-parameter tuning at the training stage, dynamic computational cost control at the inference stage, and intuitive user interactions with multimedia contents facilitated by model explanation. Our experiments on the training and deployment of multi-modality data analysis applications show that the framework is both usable and adaptable to dynamic inference loads. We implement SINGA-Easy on top of Apache SINGA and demonstrate our system with the entire machine learning life cycle.
Four-Valued Semantics for Deductive Databases
Venue: arXiv cs.DB 2021
Year: 2021

In this paper, we introduce a novel approach to deductive databases meant to take into account the needs of current applications in the area of data integration. To this end, we extend the formalism of standard deductive databases to the context of Four-valued logic so as to account for unknown, inconsistent, true or false information under the open world assumption. In our approach, a database is a pair (E,R) where E is the extension and R the set of rules. The extension is a set of pairs of the form (f, v) where f is a fact and v is a value that can be true, inconsistent or false - but not unknown (that is, unknown facts are not stored in the database). The rules follow the form of standard Datalog{neg} rules but, contrary to standard rules, their head may be a negative atom. Our main contributions are as follows: (i) we give an expression of first-degree entailment in terms of other connectors and exhibit a functionally complete set of basic connectors not involving first-degree entailment, (ii) we define a new operator for handling our new type of rules and show that this operator is monotonic and continuous, thus providing an effective way for defining and computing database semantics, and (iii) we argue that our framework allows for the definition of a new type of updates that can be used in most standard data integration applications.
UniCon: Unified Context Network for Robust Active Speaker Detection
Venue: arXiv cs.CV 2021
Year: 2021

We introduce a new efficient framework, the Unified Context Network (UniCon), for robust active speaker detection (ASD). Traditional methods for ASD usually operate on each candidate's pre-cropped face track separately and do not sufficiently consider the relationships among the candidates. This potentially limits performance, especially in challenging scenarios with low-resolution faces, multiple candidates, etc. Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information: spatial context to indicate the position and scale of each candidate's face, relational context to capture the visual relationships among the candidates and contrast audio-visual affinities with each other, and temporal context to aggregate long-term information and smooth out local uncertainties. Based on such information, our model optimizes all candidates in a unified process for robust and reliable ASD. A thorough ablation study is performed on several challenging ASD benchmarks under different settings. In particular, our method outperforms the state-of-the-art by a large margin of about 15% mean Average Precision (mAP) absolute on two challenging subsets: one with three candidate speakers, and the other with faces smaller than 64 pixels. Together, our UniCon achieves 92.0% mAP on the AVA-ActiveSpeaker validation set, surpassing 90% for the first time on this challenging dataset at the time of submission. Project website: https://unicon-asd.github.io/.
Parallel Capsule Networks for Classification of White Blood Cells
Venue: arXiv cs.CV 2021
Year: 2021

Capsule Networks (CapsNets) is a machine learning architecture proposed to overcome some of the shortcomings of convolutional neural networks (CNNs). However, CapsNets have mainly outperformed CNNs in datasets where images are small and/or the objects to identify have minimal background noise. In this work, we present a new architecture, parallel CapsNets, which exploits the concept of branching the network to isolate certain capsules, allowing each branch to identify different entities. We applied our concept to the two current types of CapsNet architectures, studying the performance for networks with different layers of capsules. We tested our design in a public, highly unbalanced dataset of acute myeloid leukaemia images (15 classes). Our experiments showed that conventional CapsNets show similar performance than our baseline CNN (ResNeXt-50) but depict instability problems. In contrast, parallel CapsNets can outperform ResNeXt-50, is more stable, and shows better rotational invariance than both, conventional CapsNets and ResNeXt-50.