Reviewerly/exHarmony huggingface dataset:
Learning Perceptual Inference by Contrasting

“Thinking in pictures,” [1] i.e., spatial-temporal reasoning, effortless and instantaneous for humans, is believed to be a significant ability to perform logical induction and a crucial factor in the intellectual history of technology development. Modern Artificial Intelligence (AI), fueled by massive datasets, deeper models, and mighty computation, has come to a stage where (super-)human-level performances are observed in certain specific tasks. However, current AI's ability in “thinking in pictures” is still far lacking behind. In this work, we study how to improve machines' reasoning ability on one challenging task of this kind: Raven's Progressive Matrices (RPM). Specifically, we borrow the very idea of “contrast effects” from the field of psychology, cognition, and education to design and train a permutation-invariant model. Inspired by cognitive studies, we equip our model with a simple inference module that is jointly trained with the perception backbone. Combining all the elements, we propose the Contrastive Perceptual Inference network (CoPINet) and empirically demonstrate that CoPINet sets the new state-of-the-art for permutation-invariant models on two major datasets. We conclude that spatial-temporal reasoning depends on envisaging the possibilities consistent with the relations between objects and can be solved from pixel-level inputs.
Privacy on the Edge: Customizable Privacy-Preserving Context Sharing in Hierarchical Edge Computing

The booming of edge computing enables and reshapes this big data era. However, privacy issues arise because increasing volume of data are published per second while the edge devices can only provide limited computing and storage resources. In addition, this has been aggravated by new emerging features of edge computing, such as decentralized and hierarchical infrastructure, mobility, and content-aware applications. Although some existing privacy preserving methods are extended to this domain, the privacy issues of data dissemination between multiple edge nodes and end users is barely studied. Motivated by this, we propose a dynamic customizable privacy-preserving model based on Markov decision process to obtain the optimized trade-off between customizable privacy protection and data utility. We start with establishing a game model between users and adversaries based on a QoS-based payoff function. A modified reinforcement learning algorithm is deployed to derive the exclusive Nash Equilibrium. Furthermore, the model can achieve fast convergence by the reduction of cardinality from n to 2. Extensive experimental results confirm the significance of the proposed model comparing to the existing work both in terms of effectiveness and feasibility.
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation

Deep neural network (DNN) based approaches have been widely investigated and deployed in medical image analysis. For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation. Even the baseline neural network models (U-Net, V-Net, etc.) have been proven to be very effective and efficient when the training process is set up properly. Nevertheless, to fully exploit the potentials of neural networks, we propose an automated searching approach for the optimal training strategy with reinforcement learning. The proposed approach can be utilized for tuning hyper-parameters, and selecting necessary data augmentation with certain probabilities. The proposed approach is validated on several tasks of 3D medical image segmentation. The performance of the baseline model is boosted after searching, and it can achieve comparable accuracy to other manually-tuned state-of-the-art segmentation approaches.
Scheduling Real-Time Parallel Applications in Cloud to Minimize Energy Consumption

Cloud computing has become an important paradigm in which scalable resources such as CPU, memory, disk and IO devices can be provided to users to remotely process their applications. In a cloud computing platform, energy consumption accounts for a significant cost portion. This article thus aims to present an energy-efficient scheduling algorithm for processing a user application with a real-time requirement. This problem is formulated as a non-linear mixed integer programming problem. We start with providing an optimal closed-form solution to its relaxation problem that aims to minimize the energy consumption without considering real-time requirements. To meet real-time requirements, we propose how to adjust task placement and resource allocation by making a good tradeoff between energy consumption and task execution time. Lastly, we find two equivalent optimal resource allocation strategies once task placement has been done. We then propose to adjust the start time of task execution such that an application’s completion time can be further shortened. Experimental results on two real-case enchmarks and extensive synthetic applications demonstrate that our proposed method finds a schedule that generally has 30 and 20 percent less energy consumption than enhancement heterogeneous earliest finish time (E-HEFT) and genetic algorithm, respectively. Besides, the proposed method has a higher rate to successfully find a feasible schedule than them, and its computation time is close to E-HEFT’s, but far less than the genetic algorithm's.
Requirements Classification with Interpretable Machine Learning and Dependency Parsing

Requirements classification is a traditional application of machine learning (ML) to RE that helps handle large requirements datasets. A prime example of an RE classification problem is the distinction between functional and non-functional (quality) requirements. State-of-the-art classifiers build their effectiveness on a large set of word features like text n-grams or POS n-grams, which do not fully capture the essence of a requirement. As a result, it is arduous for human analysts to interpret the classification results by exploring the classifier's inner workings. We propose the use of more general linguistic features, such as dependency types, for the construction of interpretable ML classifiers for RE. Through a feature engineering effort, in which we are assisted by modern introspection tools that reveal the hidden inner workings of ML classifiers, we derive a set of 17 linguistic features. While classifiers that use our proposed features fit the training set slightly worse than those that use high-dimensional feature sets, our approach performs generally better on validation datasets and it is more interpretable.
BIoT: Blockchain based IoT for Agriculture

Blockchain's most basic promise for the agriculture industry is that it removes the need for third parties otherwise required to ensure trust within buyer-seller relationships, or for that matter any source-destination relationship. In an environment enabled by blockchain technology, transactions become peer-to-peer with no use for intermediaries.Apart from providing the means to transact peer-to-peer, blockchain can create `smart contracts' that execute the terms of any agreement when specified conditions are met. Every time value changes hands, whether physical products, services or money, the transaction can be documented, creating a permanent history of the product or transaction, from source to ultimate destination. Blockchain can be of great help in this sector. A transparent and trusted system can be built by putting all the information about agricultural events on a blockchain. Farmers can also get instant data related to the seed quality, climate environment related data, payments, soil moisture, demand and sale price, etc. all on a single platform.The intent of this project is to store the sensor data in a blockchain and build a smart contract deployed in the Ethereum blockchain to facilitate buying and selling of crops and land.
A Transfer Learning Based Super-Resolution Microscopy for Biopsy Slice Images: The Joint Methods Perspective

Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is more costly than taking low-resolution ones. In this paper, we propose a joint framework containing a novel transfer learning strategy and a deep super-resolution framework to generate high-resolution slice images from low-resolution ones. The super-resolution framework called SRFBN+ is proposed by modifying a state-of-the-art framework SRFBN. Specifically, the structure of the feedback block of SRFBN was modified to be more flexible. Besides, it is challenging to use typical transfer learning strategies directly for the tasks on slice images, as the patterns on different types of biopsy slice images are varying. To this end, we propose a novel transfer learning strategy, called Channel Fusion Transfer Learning (CF-Trans). CF-Trans builds a middle domain by fusing the data manifolds of the source domain and the target domain, serving as a springboard for knowledge transfer. Thus, in the transfer learning setting, SRFBN+ can be trained on the source domain and then the middle domain and finally the target domain. Experiments on biopsy slice images validate SRFBN+ works well in generating super-resolution slice images, and CF-Trans is an efficient transfer learning strategy.
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation

We investigate and characterize the inherent resilience of conditional Generative Adversarial Networks (cGANs) against noise in their conditioning labels, and exploit this fact in the context of Unsupervised Domain Adaptation (UDA). In UDA, a classifier trained on the labelled source set can be used to infer pseudo-labels on the unlabelled target set. However, this will result in a significant amount of misclassified examples (due to the well-known domain shift issue), which can be interpreted as noise injection in the ground-truth labels for the target set. We show that cGANs are, to some extent, robust against such "shift noise". Indeed, cGANs trained with noisy pseudo-labels, are able to filter such noise and generate cleaner target samples. We exploit this finding in an iterative procedure where a generative model and a classifier are jointly trained: in turn, the generator allows to sample cleaner data from the target distribution, and the classifier allows to associate better labels to target samples, progressively refining target pseudo-labels. Results on common benchmarks show that our method performs better or comparably with the unsupervised domain adaptation state of the art.
Estimation of solar and wind energy resources over Lesotho and their complementarity by means of WRF yearly simulation at high resolution

An application of the Weather Research and Forecasting model aiming to estimate wind and photovoltaic energy resources over Lesotho is presented. To this scope, the whole year 2015 was simulated in a two-way nesting mode reaching 1 km horizontal resolution. Different model parameterizations were tested and compared to available observations, then the best performing simulation was used to elaborate wind and photovoltaic potential maps over Lesotho. Concerning the photovoltaic potential, Lesotho presents a good potential countrywide, having values ranging from around 1600 kWh/kWp to 1750 kWh/kWp, with maxima in the highlands. The results also show that there are many promising areas for wind power exploitation. Zones with higher wind potential are mainly found all along the eastern border with South Africa, from north to south and in general in the highlands. The wind potential exhibits much more variability respect to the photovoltaic one, with estimated energy production ranging from 1000 kWh/kW up to 4000 kWh/kW. Finally, an evaluation of complementarity of wind and solar electricity generation was done at different time scales, showing that some areas with negative correlation coefficient exist, where it is possible to minimise intermittent production and allowing an optimal integration in the electricity grid.
Blockchain For Intelligent Transport System

Intelligent Transportation System (ITS) is gaining attention but at the same time, road accidents, congestion, delays, etc. have also increased. Relative information about such events is vital. Such information can be presented in legal processes as digital proof. Availability of the information is not a problem as multidimensional data have been recorded all the time by ITS. Recording all the information in ITS arises the problem of fetching relevant information and removing other facts and figure that are not required to describe certain situations such as an accident. To address this issue, we analyze road accident data and reduce various dimensions with Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-negative Matrix Factorization (NMF). We conduct comparative analysis with three datasets where error rate for PCA is 32% with Dataset1. Likewise, error rate for LDA and NMF are 36% and 35%, receptively. While keeping in mind that such reduced data is helpful in many legal processes, we introduce Blockchain in the framework. Blockchain can make data immutable thus can be considered as digital proof. Blockchain also requires a smart contract in this situation between insurance companies to collect data in case of any uncertain situation. Such analysis can offer a different point of views and trends in data. Information can be more explainable to define the situation and helps to develop a friendly environment for day-to-day customers. The proposed framework provides dimensionality reduction of data that eventually reduce the data dimension to store in Blockchain.
A Morphable Face Albedo Model

In this paper, we bring together two divergent strands of research: photometric face capture and statistical 3D face appearance modelling. We propose a novel lightstage capture and processing pipeline for acquiring ear-to-ear, truly intrinsic diffuse and specular albedo maps that fully factor out the effects of illumination, camera and geometry. Using this pipeline, we capture a dataset of 50 scans and combine them with the only existing publicly available albedo dataset (3DRFE) of 23 scans. This allows us to build the first morphable face albedo model. We believe this is the first statistical analysis of the variability of facial specular albedo maps. This model can be used as a plug in replacement for the texture model of the Basel Face Model and we make our new albedo model publicly available. We ensure careful spectral calibration such that our model is built in a linear sRGB space, suitable for inverse rendering of images taken by typical cameras. We demonstrate our model in a state of the art analysis-by-synthesis 3DMM fitting pipeline, are the first to integrate specular map estimation and outperform the Basel Face Model in albedo reconstruction.
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search

The most significant barrier to the advancement of Neural Architecture Search (NAS) is its demand for large computational resources, which hinders scientifically sound empirical evaluations. As a remedy, several tabular NAS benchmarks were proposed to simulate runs of NAS methods in seconds. However, all existing tabular NAS benchmarks are limited to extremely small architectural spaces since they rely on exhaustive evaluations of the space. This leads to unrealistic results that do not transfer to larger search spaces. To overcome this fundamental limitation, we propose NAS-Bench-301, the first surrogate NAS benchmark, using a search space containing 1018 architectures, many orders of magnitude larger than any previous tabular NAS benchmark. After motivating the benefits of a surrogate benchmark over a tabular one, we fit various regression models on our dataset, which consists of ∼60k architecture evaluations, and build surrogates via deep ensembles to model uncertainty. We benchmark a wide range of NAS algorithms using NAS-Bench-301 and obtain comparable results to the true benchmark at a fraction of the real cost. Finally, we show how NAS-Bench-301 can be used to generate new scientific insights.
Optimal fermion-to-qubit mapping via ternary trees with applications to reduced quantum states learning

We introduce a fermion-to-qubit mapping defined on ternary trees, where any single Majorana operator on an<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-mode fermionic system is mapped to a multi-qubit Pauli operator acting nontrivially on<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo fence="false" stretchy="false">⌈</mml:mo><mml:msub><mml:mi>log</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>⁡</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>2</mml:mn><mml:mi>n</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false">)</mml:mo><mml:mo fence="false" stretchy="false">⌉</mml:mo></mml:math>qubits. The mapping has a simple structure and is optimal in the sense that it is impossible to construct Pauli operators in any fermion-to-qubit mapping acting nontrivially on less than<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>log</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>⁡</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>2</mml:mn><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>qubits on average. We apply it to the problem of learning<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-fermion reduced density matrix (RDM), a problem relevant in various quantum simulation applications. We show that one can determine individual elements of all<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-fermion RDMs in parallel, to precision<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ϵ</mml:mi></mml:math>, by repeating a single quantum circuit for<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≲</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>2</mml:mn><mml:mi>n</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mo stretchy="false">)</mml:mo><mml:mi>k</mml:mi></mml:msup><mml:msup><mml:mi>ϵ</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>times. This result is based on a method we develop here that allows one to determine individual elements of all<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-qubit RDMs in parallel, to precision<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ϵ</mml:mi></mml:math>, by repeating a single quantum circuit for<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≲</mml:mo><mml:msup><mml:mn>3</mml:mn><mml:mi>k</mml:mi></mml:msup><mml:msup><mml:mi>ϵ</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>times, independent of the system size. This improves over existing schemes for determining qubit RDMs.
Learning Object Placement by Inpainting for Compositional Data Augmentation

We study the problem of common sense placement of visual objects in an image. This involves multiple aspects of visual recognition: the instance segmentation of the scene, 3D layout, and common knowledge of how objects are placed and where objects are moving in the 3D scene. This seemingly simple task is difficult for current learning-based approaches because of the lack of labeled training pair of foreground objects paired with cleaned background scenes. We propose a self-learning framework that automatically generates the necessary training data without any manual labeling by detecting, cutting, and inpainting objects from an image. We propose a PlaceNet that predicts a diverse distribution of common sense locations when given a foreground object and a background scene. We show one practical use of our object placement network for augmenting training datasets by recomposition of object-scene with a key property of contextual relationship preservation. We demonstrate improvement of object detection and instance segmentation performance on both Cityscape [4] and KITTI [9] datasets. We also show that the learned representation of our PlaceNet displays strong discriminative power in image retrieval and classification.
GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles

SHapley Additive exPlanation (SHAP) values (Lundberg & Lee, 2017) provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values (Shapley, 1953). While exact calculation of SHAP values is computationally intractable in general, a recursive polynomial-time algorithm called TreeShap (Lundberg et al., 2020) is available for decision tree models. However, despite its polynomial time complexity, TreeShap can become a significant bottleneck in practical machine learning pipelines when applied to large decision tree ensembles. Unfortunately, the complicated TreeShap algorithm is difficult to map to hardware accelerators such as GPUs. In this work, we present GPUTreeShap, a reformulated TreeShap algorithm suitable for massively parallel computation on graphics processing units. Our approach first preprocesses each decision tree to isolate variable sized sub-problems from the original recursive algorithm, then solves a bin packing problem, and finally maps sub-problems to single-instruction, multiple-thread (SIMT) tasks for parallel execution with specialised hardware instructions. With a single NVIDIA Tesla V100-32 GPU, we achieve speedups of up to 19× for SHAP values, and speedups of up to 340× for SHAP interaction values, over a state-of-the-art multi-core CPU implementation executed on two 20-core Xeon E5-2698 v4 2.2 GHz CPUs. We also experiment with multi-GPU computing using eight V100 GPUs, demonstrating throughput of 1.2 M rows per second-equivalent CPU-based performance is estimated to require 6850 CPU cores.
Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

Recent advancements in deep learning (DL) have made possible new methodologies for analyzing massive datasets with intriguing implications in healthcare. Convolutional neural networks (CNN), which have proven to be successful supervised algorithms for classifying imaging data, are of particular interest in the neuroscience community for their utility in the classification of Alzheimer's disease (AD). AD is the leading cause of dementia in the aging population. There remains a critical unmet need for early detection of AD pathogenesis based on non-invasive neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). In this comprehensive review, we explore potential interdisciplinary approaches for early detection and provide insight into recent advances on AD classification using 3D CNN architectures for multi-modal PET/MRI data. We also consider the application of generative adversarial networks (GANs) to overcome pitfalls associated with limited data. Finally, we discuss increasing the robustness of CNNs by combining them with ensemble learning (EL).