OpenRaiser/Intern-Atlas huggingface dataset:
Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels
Venue: arXiv cs.CV 2020
Year: 2020

Pathologist-defined labels are the gold standard for histopathological data sets, regardless of well-known limitations in consistency for some tasks. To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms. In order to assess robustness of those algorithms and reproducibility of their methods it is necessary to test on several independent datasets. The influence of different labeling methods of these available datasets is currently unknown. To tackle this, we present an alternative set of labels for the images of the auxiliary mitosis dataset of the TUPAC16 challenge. Additional to manual mitotic figure screening, we used a novel, algorithm-aided labeling process, that allowed to minimize the risk of missing rare mitotic figures in the images. All potential mitotic figures were independently assessed by two pathologists. The novel, publicly available set of labels contains 1,999 mitotic figures (+28.80%) and additionally includes 10,483 labels of cells with high similarities to mitotic figures (hard examples). We found significant difference comparing F_1 scores between the original label set (0.549) and the new alternative label set (0.735) using a standard deep learning object detection architecture. The models trained on the alternative set showed higher overall confidence values, suggesting a higher overall label consistency. Findings of the present study show that pathologists-defined labels may vary significantly resulting in notable difference in the model performance. Comparison of deep learning-based algorithms between independent datasets with different labeling methods should be done with caution.
COBRA: Compression via Abstraction of Provenance for Hypothetical Reasoning
Venue: arXiv cs.DB 2020
Year: 2020

Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Recent work has proposed to leverage ideas from data provenance tracking towards supporting efficient hypothetical reasoning: instead of a costly re-execution of the underlying application, one may assign values to a pre-computed provenance expression. A prime challenge in leveraging this approach for large-scale data and complex applications lies in the size of the provenance. To this end, we present a framework that allows to reduce provenance size. Our approach is based on reducing the provenance granularity using abstraction. We propose a demonstration of COBRA, a system that allows examine the effect of the provenance compression on the anticipated analysis results. We will demonstrate the usefulness of COBRA in the context of business data analysis.
Truss-based Structural Diversity Search in Large Graphs
Venue: arXiv cs.DB 2020
Year: 2020

Social decisions made by individuals are easily influenced by information from their social neighborhoods. A key predictor of social contagion is the multiplicity of social contexts inside the individual's contact neighborhood, which is termed structural diversity. However, the existing models have limited decomposability for analyzing large-scale networks, and suffer from the inaccurate reflection of social context diversity. In this paper, we propose a new truss-based structural diversity model to overcome the weak decomposability. Based on this model, we study a novel problem of truss-based structural diversity search in a graph G, that is, to find the r vertices with the highest truss-based structural diversity and return their social contexts. o tackle this problem, we propose an online structural diversity search algorithm in $O(ρ(m+\mathcal{T}))$ time, where $ρ$, $m$, and $\mathcal{T}$ are respectively the arboricity, the number of edges, and the number of triangles in $G$. To improve the efficiency, we design an elegant and compact index, called TSD-index, for further expediting the search process. We further optimize the structure of TSD-index into a highly compressed GCT-index. Our GCT-index-based structural diversity search utilizes the global triangle information for fast index construction and finds answers in $O(m)$ time. Extensive experiments demonstrate the effectiveness and efficiency of our proposed model and algorithms, against state-of-the-art methods.
Improved Analysis of UCRL2 with Empirical Bernstein Inequality
Venue: arXiv cs.LG 2020
Year: 2020

We consider the problem of exploration-exploitation in communicating Markov Decision Processes. We provide an analysis of UCRL2 with Empirical Bernstein inequalities (UCRL2B). For any MDP with $S$ states, $A$ actions, $Γ\leq S$ next states and diameter $D$, the regret of UCRL2B is bounded as $\widetilde{O}(\sqrt{DΓS A T})$.
Grading video interviews with fairness considerations
Venue: arXiv cs.CV 2020
Year: 2020

There has been considerable interest in predicting human emotions and traits using facial images and videos. Lately, such work has come under criticism for poor labeling practices, inconclusive prediction results and fairness considerations. We present a careful methodology to automatically derive social skills of candidates based on their video response to interview questions. We, for the first time, include video data from multiple countries encompassing multiple ethnicities. Also, the videos were rated by individuals from multiple racial backgrounds, following several best practices, to achieve a consensus and unbiased measure of social skills. We develop two machine-learning models to predict social skills. The first model employs expert-guidance to use plausibly causal features. The second uses deep learning and depends solely on the empirical correlations present in the data. We compare errors of both these models, study the specificity of the models and make recommendations. We further analyze fairness by studying the errors of models by race and gender. We verify the usefulness of our models by determining how well they predict interview outcomes for candidates. Overall, the study provides strong support for using artificial intelligence for video interview scoring, while taking care of fairness and ethical considerations.
Predicting Illegal Fishing on the Patagonia Shelf from Oceanographic Seascapes
Venue: arXiv cs.LG 2020
Year: 2020

Many of the world's most important fisheries are experiencing increases in illegal fishing, undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending illegal, unreported, and unregulated (IUU) fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time-consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict illegal fishing on the Patagonian Shelf, one of the world's most productive regions for fisheries. Specifically, we focus on Chinese fishing vessels, which have consistently fished illegally in this region. We combine vessel location data with oceanographic seascapes -- classes of oceanic areas based on oceanographic variables -- as well as other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a Chinese vessel is operating illegally with 69-96% confidence, depending on the year and predictor variables used. These results offer a promising step towards preempting illegal activities, rather than reacting to them forensically.
Multi-Domain Image Completion for Random Missing Input Data
Venue: arXiv cs.CV 2020
Year: 2020

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared skeleton encoding and separate flesh encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.
Distributed-Memory DMRG via Sparse and Dense Parallel Tensor Contractions
Venue: arXiv cs.DC 2020
Year: 2020

The Density Matrix Renormalization Group (DMRG) algorithm is a powerful tool for solving eigenvalue problems to model quantum systems. DMRG relies on tensor contractions and dense linear algebra to compute properties of condensed matter physics systems. However, its efficient parallel implementation is challenging due to limited concurrency, large memory footprint, and tensor sparsity. We mitigate these problems by implementing two new parallel approaches that handle block sparsity arising in DMRG, via Cyclops, a distributed memory tensor contraction library. We benchmark their performance on two physical systems using the Blue Waters and Stampede2 supercomputers. Our DMRG performance is improved by up to 5.9X in runtime and 99X in processing rate over ITensor, at roughly comparable computational resource use. This enables higher accuracy calculations via larger tensors for quantum state approximation. We demonstrate that despite having limited concurrency, DMRG is weakly scalable with the use of efficient parallel tensor contraction mechanisms.
Differentially private cross-silo federated learning
Venue: arXiv cs.CR 2020
Year: 2020

Strict privacy is of paramount importance in distributed machine learning. Federated learning, with the main idea of communicating only what is needed for learning, has been recently introduced as a general approach for distributed learning to enhance learning and improve security. However, federated learning by itself does not guarantee any privacy for data subjects. To quantify and control how much privacy is compromised in the worst-case, we can use differential privacy. In this paper we combine additively homomorphic secure summation protocols with differential privacy in the so-called cross-silo federated learning setting. The goal is to learn complex models like neural networks while guaranteeing strict privacy for the individual data subjects. We demonstrate that our proposed solutions give prediction accuracy that is comparable to the non-distributed setting, and are fast enough to enable learning models with millions of parameters in a reasonable time. To enable learning under strict privacy guarantees that need privacy amplification by subsampling, we present a general algorithm for oblivious distributed subsampling. However, we also argue that when malicious parties are present, a simple approach using distributed Poisson subsampling gives better privacy. Finally, we show that by leveraging random projections we can further scale-up our approach to larger models while suffering only a modest performance loss.
Learning Entangled Single-Sample Gaussians in the Subset-of-Signals Model
Venue: arXiv cs.LG 2020
Year: 2020

In the setting of entangled single-sample distributions, the goal is to estimate some common parameter shared by a family of $n$ distributions, given one single sample from each distribution. This paper studies mean estimation for entangled single-sample Gaussians that have a common mean but different unknown variances. We propose the subset-of-signals model where an unknown subset of $m$ variances are bounded by 1 while there are no assumptions on the other variances. In this model, we analyze a simple and natural method based on iteratively averaging the truncated samples, and show that the method achieves error $O \left(\frac{\sqrt{n\ln n}}{m}\right)$ with high probability when $m=Ω(\sqrt{n\ln n})$, matching existing bounds for this range of $m$. We further prove lower bounds, showing that the error is $Ω\left(\left(\frac{n}{m^4}\right)^{1/2}\right)$ when $m$ is between $Ω(\ln n)$ and $O(n^{1/4})$, and the error is $Ω\left(\left(\frac{n}{m^4}\right)^{1/6}\right)$ when $m$ is between $Ω(n^{1/4})$ and $O(n^{1 - ε})$ for an arbitrarily small $ε>0$, improving existing lower bounds and extending to a wider range of $m$.
Improved Detection of Adversarial Images Using Deep Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Machine learning techniques are immensely deployed in both industry and academy. Recent studies indicate that machine learning models used for classification tasks are vulnerable to adversarial examples, which limits the usage of applications in the fields with high precision requirements. We propose a new approach called Feature Map Denoising to detect the adversarial inputs and show the performance of detection on the mixed dataset consisting of adversarial examples generated by different attack algorithms, which can be used to associate with any pre-trained DNNs at a low cost. Wiener filter is also introduced as the denoise algorithm to the defense model, which can further improve performance. Experimental results indicate that good accuracy of detecting the adversarial examples can be achieved through our Feature Map Denoising algorithm.
A Strong XOR Lemma for Randomized Query Complexity
Venue: arXiv cs.CC 2020
Year: 2020

We give a strong direct sum theorem for computing $xor \circ g$. Specifically, we show that for every function g and every $k\geq 2$, the randomized query complexity of computing the xor of k instances of g satisfies $\overline{R}_\eps(xor\circ g) = Θ(k \overline{R}_{\eps/k}(g))$. This matches the naive success amplification upper bound and answers a conjecture of Blais and Brody (CCC19). As a consequence of our strong direct sum theorem, we give a total function g for which $R(xor \circ g) = Θ(k \log(k)\cdot R(g))$, answering an open question from Ben-David et al.(arxiv:2006.10957v1).
Neuromorphic Processing and Sensing: Evolutionary Progression of AI to Spiking
Venue: arXiv cs.NE 2020
Year: 2020

The increasing rise in machine learning and deep learning applications is requiring ever more computational resources to successfully meet the growing demands of an always-connected, automated world. Neuromorphic technologies based on Spiking Neural Network algorithms hold the promise to implement advanced artificial intelligence using a fraction of the computations and power requirements by modeling the functioning, and spiking, of the human brain. With the proliferation of tools and platforms aiding data scientists and machine learning engineers to develop the latest innovations in artificial and deep neural networks, a transition to a new paradigm will require building from the current well-established foundations. This paper explains the theoretical workings of neuromorphic technologies based on spikes, and overviews the state-of-art in hardware processors, software platforms and neuromorphic sensing devices. A progression path is paved for current machine learning specialists to update their skillset, as well as classification or predictive models from the current generation of deep neural networks to SNNs. This can be achieved by leveraging existing, specialized hardware in the form of SpiNNaker and the Nengo migration toolkit. First-hand, experimental results of converting a VGG-16 neural network to an SNN are shared. A forward gaze into industrial, medical and commercial applications that can readily benefit from SNNs wraps up this investigation into the neuromorphic computing future.
Efficient MDP Analysis for Selfish-Mining in Blockchains
Venue: arXiv cs.CR 2020
Year: 2020

A proof of work (PoW) blockchain protocol distributes rewards to its participants, called miners, according to their share of the total computational power. Sufficiently large miners can perform selfish mining - deviate from the protocol to gain more than their fair share. Such systems are thus secure if all miners are smaller than a threshold size so their best response is following the protocol. To find the threshold, one has to identify the optimal strategy for miners of different sizes, i.e., solve a Markov Decision Process (MDP). However, because of the PoW difficulty adjustment mechanism, the miners' utility is a non-linear ratio function. We therefore call this an Average Reward Ratio (ARR) MDP. Sapirshtein et al.\ were the first to solve ARR MDPs by solving a series of standard MDPs that converge to the ARR MDP solution. In this work, we present a novel technique for solving an ARR MDP by solving a single standard MDP. The crux of our approach is to augment the MDP such that it terminates randomly, within an expected number of rounds. We call this Probabilistic Termination Optimization (PTO), and the technique applies to any MDP whose utility is a ratio function. We bound the approximation error of PTO - it is inversely proportional to the expected number of rounds before termination, a parameter that we control. Empirically, PTO's complexity is an order of magnitude lower than the state of the art. PTO can be easily applied to different blockchains. We use it to tighten the bound on the threshold for selfish mining in Ethereum.
PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision
Venue: arXiv cs.CV 2020
Year: 2020

Current state of the art methods for generating semantic segmentation rely heavily on a large set of images that have each pixel labeled with a class of interest label or background. Coming up with such labels, especially in domains that require an expert to do annotations, comes at a heavy cost in time and money. Several methods have shown that we can learn semantic segmentation from less expensive image-level labels, but the effectiveness of point level labels, a healthy compromise between all pixels labelled and none, still remains largely unexplored. This paper presents a novel procedure for producing semantic segmentation from images given some point level annotations. This method includes point annotations in the training of a convolutional neural network (CNN) for producing improved localization and class activation maps. Then, we use another CNN for predicting semantic affinities in order to propagate rough class labels and create pseudo semantic segmentation labels. Finally, we propose training a CNN that is normally fully supervised using our pseudo labels in place of ground truth labels, which further improves performance and simplifies the inference process by requiring just one CNN during inference rather than two. Our method achieves state of the art results for point supervised semantic segmentation on the PASCAL VOC 2012 dataset \cite{everingham2010pascal}, even outperforming state of the art methods for stronger bounding box and squiggle supervision.
Fast Real-time Counterfactual Explanations
Venue: arXiv cs.CV 2020
Year: 2020

Counterfactual explanations are considered, which is to answer {\it why the prediction is class A but not B.} Different from previous optimization based methods, an optimization-free Fast ReAl-time Counterfactual Explanation (FRACE) algorithm is proposed benefiting from the development of multi-domain image to image translation algorithms. Built from starGAN, a transformer is trained as a residual generator conditional on a classifier constrained under a proposal perturbation loss which maintains the content information of the query image, but just the class-specific semantic information is changed. The transformer can transfer the query image to any counterfactual class, and during inference, our explanation can be generated by it only within a forward time. It is fast and can satisfy the real-time practical application. Because of the adversarial training of GAN, our explanation is also more realistic compared to other counterparts. The experimental results demonstrate that our proposal is better than the existing state of the art in terms of quality and speed.
Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis
Venue: arXiv cs.CV 2020
Year: 2020

The urban intersection is a typically dynamic and complex scenario for intelligent vehicles, which exists a variety of driving behaviors and traffic participants. Accurately modelling the driver behavior at the intersection is essential for intelligent transportation systems (ITS). Previous researches mainly focus on using attention mechanism to model the degree of correlation. In this research, a canonical correlation analysis (CCA)-based framework is proposed. The value of canonical correlation is used for feature selection. Gaussian mixture model and Gaussian process regression are applied for driver behavior modelling. Two experiments using simulated and naturalistic driving data are designed for verification. Experimental results are consistent with the driver's judgment. Comparative studies show that the proposed framework can obtain a better performance.
Cost Minimization for Cooperative Computation Framework in MEC Networks
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, a cooperative task computation framework exploits the computation resource in UEs to accomplish more tasks meanwhile minimizes the power consumption of UEs. The system cost includes the cost of UEs' power consumption and the penalty of unaccomplished tasks and the system cost is minimized by jointly optimizing binary offloading decisions, the computational frequencies, and the offloading transmit power. To solve the formulated mixed-integer non-linear programming problem, three efficient algorithms are proposed, i.e., integer constraints relaxation-based iterative algorithm (ICRBI), heuristic matching algorithm, and the decentralized algorithm. The ICRBI algorithm achieves the best performance at the cost of the highest complexity, while the heuristic matching algorithm significantly reduces the complexity while still providing reasonable performance. As the previous two algorithms are centralized, the decentralized algorithm is also provided to further reduce the complexity, and it is suitable for the scenarios that cannot provide the central controller. The simulation results are provided to validate the performance gain in terms of the total system cost obtained by the proposed cooperative computation framework.