OpenRaiser/Intern-Atlas huggingface dataset:
Cancer image classification based on DenseNet model
Venue: arXiv cs.CV 2020
Year: 2020

Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination. Image processing introduced a promising strategy to facilitate disease classification and detection while diminishing unnecessary expenses. In this paper, we propose a novel metastatic cancer image classification model based on DenseNet Block, which can effectively identify metastatic cancer in small image patches taken from larger digital pathology scans. We evaluate the proposed approach to the slightly modified version of the PatchCamelyon (PCam) benchmark dataset. The dataset is the slightly modified version of the PatchCamelyon (PCam) benchmark dataset provided by Kaggle competition, which packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task. The experiments indicated that our model outperformed other classical methods like Resnet34, Vgg19. Moreover, we also conducted data augmentation experiment and study the relationship between Batches processed and loss value during the training and validation process.
Integrating Deep Learning in Domain Sciences at Exascale
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, an open-source HPC deep learning framework. Many deep learning frameworks are targeted at data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e.g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization methods. Finally, we present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated in materials science, imaging, and climate applications.
Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases
Venue: arXiv cs.CV 2020
Year: 2020

Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay. Understanding the uncertainty of the prediction is also crucial. Most existing approaches however can only estimate uncertainty through repeated sampling of generative models. Additionally, most current predictive models are trained on datasets that assume complete observability of the crowd using an aerial view. These are generally not representative of real-world usage from a vehicle perspective, and can lead to the underestimation of uncertainty bounds when the on-board sensors are occluded. Inspired by prior work in motion prediction using spatio-temporal graphs, we propose a novel Graph Convolutional Neural Network (GCNN)-based approach, Attentional-GCNN, which aggregates information of implicit interaction between pedestrians in a crowd by assigning attention weight in edges of the graph. Our model can be trained to either output a probabilistic distribution or faster deterministic prediction, demonstrating applicability to autonomous vehicle use cases where either speed or accuracy with uncertainty bounds are required. To further improve the training of predictive models, we propose an automatically labelled pedestrian dataset collected from an intelligent vehicle platform representative of real-world use. Through experiments on a number of datasets, we show our proposed method achieves an improvement over the state of art by 10% Average Displacement Error (ADE) and 12% Final Displacement Error (FDE) with fast inference speeds.
An off-the-grid approach to multi-compartment magnetic resonance fingerprinting
Venue: arXiv cs.CV 2020
Year: 2020

We propose a novel numerical approach to separate multiple tissue compartments in image voxels and to estimate quantitatively their nuclear magnetic resonance (NMR) properties and mixture fractions, given magnetic resonance fingerprinting (MRF) measurements. The number of tissues, their types or quantitative properties are not a-priori known, but the image is assumed to be composed of sparse compartments with linearly mixed Bloch magnetisation responses within voxels. Fine-grid discretisation of the multi-dimensional NMR properties creates large and highly coherent MRF dictionaries that can challenge scalability and precision of the numerical methods for (discrete) sparse approximation. To overcome these issues, we propose an off-the-grid approach equipped with an extended notion of the sparse group lasso regularisation for sparse approximation using continuous (non-discretised) Bloch response models. Further, the nonlinear and non-analytical Bloch responses are approximated by a neural network, enabling efficient back-propagation of the gradients through the proposed algorithm. Tested on simulated and in-vivo healthy brain MRF data, we demonstrate effectiveness of the proposed scheme compared to the baseline multicompartment MRF methods.
Adversarial Refinement Network for Human Motion Prediction
Venue: arXiv cs.CV 2020
Year: 2020

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions.
Synthesis and Pruning as a Dynamic Compression Strategy for Efficient Deep Neural Networks
Venue: arXiv cs.AI 2020
Year: 2020

The brain is a highly reconfigurable machine capable of task-specific adaptations. The brain continually rewires itself for a more optimal configuration to solve problems. We propose a novel strategic synthesis algorithm for feedforward networks that draws directly from the brain's behaviours when learning. The proposed approach analyses the network and ranks weights based on their magnitude. Unlike existing approaches that advocate random selection, we select highly performing nodes as starting points for new edges and exploit the Gaussian distribution over the weights to select corresponding endpoints. The strategy aims only to produce useful connections and result in a smaller residual network structure. The approach is complemented with pruning to further the compression. We demonstrate the techniques to deep feedforward networks. The residual sub-networks that are formed from the synthesis approaches in this work form common sub-networks with similarities up to ~90%. Using pruning as a complement to the strategic synthesis approach, we observe improvements in compression.
On the application of Physically-Guided Neural Networks with Internal Variables to Continuum Problems
Venue: arXiv cs.LG 2020
Year: 2020

Predictive Physics has been historically based upon the development of mathematical models that describe the evolution of a system under certain external stimuli and constraints. The structure of such mathematical models relies on a set of hysical hypotheses that are assumed to be fulfilled by the system within a certain range of environmental conditions. A new perspective is now raising that uses physical knowledge to inform the data prediction capability of artificial neural networks. A particular extension of this data-driven approach is Physically-Guided Neural Networks with Internal Variables (PGNNIV): universal physical laws are used as constraints in the neural network, in such a way that some neuron values can be interpreted as internal state variables of the system. This endows the network with unraveling capacity, as well as better predictive properties such as faster convergence, fewer data needs and additional noise filtering. Besides, only observable data are used to train the network, and the internal state equations may be extracted as a result of the training processes, so there is no need to make explicit the particular structure of the internal state model. We extend this new methodology to continuum physical problems, showing again its predictive and explanatory capacities when only using measurable values in the training set. We show that the mathematical operators developed for image analysis in deep learning approaches can be used and extended to consider standard functional operators in continuum Physics, thus establishing a common framework for both. The methodology presented demonstrates its ability to discover the internal constitutive state equation for some problems, including heterogeneous and nonlinear features, while maintaining its predictive ability for the whole dataset coverage, with the cost of a single evaluation.
Influence of Murder Incident of Ride-hailing Drivers on Ride-hailing User's Consuming Willingness in Nanchang
Venue: arXiv cs.CY 2020
Year: 2020

Due to the frequent murder incidents of ride-hailing drivers in China in 2018, ride-hailing companies took a series of measures to prevent such incidents and ensure ride-hailing passengers' safety. This study investigated users' willingness to use ride-hailing apps after murder incidents and users' attitudes toward Safety Rectification. We found that murder incidents of ride-hailing drivers had a significant adverse impact on people's usage of ride-hailing apps. Female users' consuming willingness was 0.633 times that of male users, such as" psychological harm" was more evident among females, and Safety Rectification had a calming effect for some users. Finally, we found that people were satisfied with ride-hailing apps' efficiency, but were not satisfied with safety and reliability, considered them important; female users were more concerned about the security than male users.
OAK: Ontology-Based Knowledge Map Model for Digital Agriculture
Venue: arXiv cs.DB 2020
Year: 2020

Nowadays, a huge amount of knowledge has been amassed in digital agriculture. This knowledge and know-how information are collected from various sources, hence the question is how to organise this knowledge so that it can be efficiently exploited. Although this knowledge about agriculture practices can be represented using ontology, rule-based expert systems, or knowledge model built from data mining processes, the scalability still remains an open issue. In this study, we propose a knowledge representation model, called an ontology-based knowledge map, which can collect knowledge from different sources, store it, and exploit either directly by stakeholders or as an input to the knowledge discovery process (Data Mining). The proposed model consists of two stages, 1) build an ontology as a knowledge base for a specific domain and data mining concepts, and 2) build the ontology-based knowledge map model for representing and storing the knowledge mined on the crop datasets. A framework of the proposed model has been implemented in agriculture domain. It is an efficient and scalable model, and it can be used as knowledge repository a digital agriculture.
Speeding up decimal multiplication
Venue: arXiv cs.DS 2020
Year: 2020

Decimal multiplication is the task of multiplying two numbers in base $10^N.$ Specifically, we focus on the number-theoretic transform (NTT) family of algorithms. Using only portable techniques, we achieve a 3x-5x speedup over the mpdecimal library. In this paper we describe our implementation and discuss further possible optimizations. We also present a simple cache-efficient algorithm for in-place $2n \times n$ or $n \times 2n$ matrix transposition, the need for which arises in the "six-step algorithm" variation of the matrix Fourier algorithm, and which does not seem to be widely known. Another finding is that use of two prime moduli instead of three makes sense even considering the worst case of increasing the size of the input, and makes for simpler answer recovery.
Rephased CLuP
Venue: arXiv cs.IT 2020
Year: 2020

In \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19} we introduced CLuP, a \bl{\textbf{Random Duality Theory (RDT)}} based algorithmic mechanism that can be used for solving hard optimization problems. Due to their introductory nature, \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19} discuss the most fundamental CLuP concepts. On the other hand, in our companion paper \cite{Stojniccluplargesc20} we started the story of going into a bit deeper details that relate to many of other remarkable CLuP properties with some of them reaching well beyond the basic fundamentals. Namely, \cite{Stojniccluplargesc20} discusses how a somewhat silent RDT feature (its algorithmic power) can be utilized to ensure that CLuP can be run on very large problem instances as well. In particular, applying CLuP to the famous MIMO ML detection problem we showed in \cite{Stojniccluplargesc20} that its a large scale variant, $\text{CLuP}^{r_0}$, can handle with ease problems with \textbf{\emph{several thousands}} of unknowns with theoretically minimal complexity per iteration (only a single matrix-vector multiplication suffices). In this paper we revisit MIMO ML detection and discuss another remarkable phenomenon that emerges within the CLuP structure, namely the so-called \bl{\textbf{\emph{rephasing}}}. As MIMO ML enters the so-called low $α$ regime (fat system matrix with ratio of the number of rows and columns, $α$, going well below $1$) it becomes increasingly difficult even for the basic standard CLuP to handle it. However, the discovery of the rephasing ensures that CLuP remains on track and preserves its ability to achieve the ML performance. To demonstrate the power of the rephasing we also conducted quite a few numerical experiments, compared the results we obtained through them to the theoretical predictions, and observed an excellent agreement.
Studying Taxonomy Enrichment on Diachronic WordNet Versions
Venue: arXiv cs.CL 2020
Year: 2020

Ontologies, taxonomies, and thesauri are used in many NLP tasks. However, most studies are focused on the creation of these lexical resources rather than the maintenance of the existing ones. Thus, we address the problem of taxonomy enrichment. We explore the possibilities of taxonomy extension in a resource-poor setting and present methods which are applicable to a large number of languages. We create novel English and Russian datasets for training and evaluating taxonomy enrichment models and describe a technique of creating such datasets for other languages.
MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Resource-Constrained IoT Edge Devices
Venue: arXiv cs.CR 2020
Year: 2020

Traditional learning-based approaches for run-time Hardware Trojan detection require complex and expensive on-chip data acquisition frameworks and thus incur high area and power overhead. To address these challenges, we propose to leverage the power correlation between the executing instructions of a microprocessor to establish a machine learning-based run-time Hardware Trojan (HT) detection framework, called MacLeR. To reduce the overhead of data acquisition, we propose a single power-port current acquisition block using current sensors in time-division multiplexing, which increases accuracy while incurring reduced area overhead. We have implemented a practical solution by analyzing multiple HT benchmarks inserted in the RTL of a system-on-chip (SoC) consisting of four LEON3 processors integrated with other IPs like vga_lcd, RSA, AES, Ethernet, and memory controllers. Our experimental results show that compared to state-of-the-art HT detection techniques, MacLeR achieves 10\% better HT detection accuracy (i.e., 96.256%) while incurring a 7x reduction in area and power overhead (i.e., 0.025% of the area of the SoC and <0.07% of the power of the SoC). In addition, we also analyze the impact of process variation and aging on the extracted power profiles and the HT detection accuracy of MacLeR. Our analysis shows that variations in fine-grained power profiles due to the HTs are significantly higher compared to the variations in fine-grained power profiles caused by the process variations (PV) and aging effects. Moreover, our analysis demonstrates that, on average, the HT detection accuracy drop in MacLeR is less than 1% and 9% when considering only PV and PV with worst-case aging, respectively, which is ~10x less than in the case of the state-of-the-art ML-based HT detection technique.
Alone or With Others? Understanding Eating Episodes of College Students with Mobile Sensing
Venue: arXiv cs.CY 2020
Year: 2020

Understanding food consumption patterns and contexts using mobile sensing is fundamental to build mobile health applications that require minimal user interaction to generate mobile food diaries. Many available mobile food diaries, both commercial and in research, heavily rely on self-reports, and this dependency limits the long term adoption of these apps by people. The social context of eating (alone, with friends, with family, with a partner, etc.) is an important self-reported feature that influences aspects such as food type, psychological state while eating, and the amount of food, according to prior research in nutrition and behavioral sciences. In this work, we use two datasets regarding the everyday eating behavior of college students in two countries, namely Switzerland (N_ch=122) and Mexico (N_mx=84), to examine the relation between the social context of eating and passive sensing data from wearables and smartphones. Moreover, we design a classification task, namely inferring eating-alone vs. eating-with-others episodes using passive sensing data and time of eating, obtaining accuracies between 77% and 81%. We believe that this is a first step towards understanding more complex social contexts related to food consumption using mobile sensing.
Evaluating Feedback Strategies for Virtual Human Trainers
Venue: arXiv cs.HC 2020
Year: 2020

In this paper we address feedback strategies for an autonomous virtual trainer. First, a pilot study was conducted to identify and specify feedback strategies for assisting participants in performing a given task. The task involved sorting virtual cubes according to areas of countries displayed on them. Two feedback strategies were specified. The first provides correctness feedback by fully correcting user responses at each stage of the task, and the second provides suggestive feedback by only notifying if and how a response can be corrected. Both strategies were implemented in a virtual training system and empirically evaluated. The correctness feedback strategy was preferred by the participants, was more effective time-wise, and was more effective in improving task performance skills. The overall system was also rated comparable to hypothetically performing the same task with real interactions.
A Use of Even Activation Functions in Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Despite broad interest in applying deep learning techniques to scientific discovery, learning interpretable formulas that accurately describe scientific data is very challenging because of the vast landscape of possible functions and the "black box" nature of deep neural networks. The key to success is to effectively integrate existing knowledge or hypotheses about the underlying structure of the data into the architecture of deep learning models to guide machine learning. Currently, such integration is commonly done through customization of the loss functions. Here we propose an alternative approach to integrate existing knowledge or hypotheses of data structure by constructing custom activation functions that reflect this structure. Specifically, we study a common case when the multivariate target function $f$ to be learned from the data is partially exchangeable, \emph{i.e.} $f(u,v,w)=f(v,u,w)$ for $u,v\in \mathbb{R}^d$. For instance, these conditions are satisfied for the classification of images that is invariant under left-right flipping. Through theoretical proof and experimental verification, we show that using an even activation function in one of the fully connected layers improves neural network performance. In our experimental 9-dimensional regression problems, replacing one of the non-symmetric activation functions with the designated "Seagull" activation function $\log(1+x^2)$ results in substantial improvement in network performance. Surprisingly, even activation functions are seldom used in neural networks. Our results suggest that customized activation functions have great potential in neural networks.
Multimodal Pretraining for Dense Video Captioning
Venue: arXiv cs.CV 2020
Year: 2020

Learning specific hands-on skills such as cooking, car maintenance, and home repairs increasingly happens via instructional videos. The user experience with such videos is known to be improved by meta-information such as time-stamped annotations for the main steps involved. Generating such annotations automatically is challenging, and we describe here two relevant contributions. First, we construct and release a new dense video captioning dataset, Video Timeline Tags (ViTT), featuring a variety of instructional videos together with time-stamped annotations. Second, we explore several multimodal sequence-to-sequence pretraining strategies that leverage large unsupervised datasets of videos and caption-like texts. We pretrain and subsequently finetune dense video captioning models using both YouCook2 and ViTT. We show that such models generalize well and are robust over a wide variety of instructional videos.