OpenRaiser/Intern-Atlas huggingface dataset:
Benchmarking features from different radiomics toolkits / toolboxes using Image Biomarkers Standardization Initiative
Venue: arXiv cs.CV 2020
Year: 2020

There is no consensus regarding the radiomic feature terminology, the underlying mathematics, or their implementation. This creates a scenario where features extracted using different toolboxes could not be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, the image biomarker standardization initiative (IBSI) established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray level discretization approaches. Since these programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and the UpSet diagrams and also quantified using two ad hoc metrics. Morphology features earns lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be identical and reliable. Further studies are needed to standardize the workflow of radiomic feature extraction.
Combinatorial Pure Exploration of Dueling Bandit
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we study combinatorial pure exploration for dueling bandits (CPE-DB): we have multiple candidates for multiple positions as modeled by a bipartite graph, and in each round we sample a duel of two candidates on one position and observe who wins in the duel, with the goal of finding the best candidate-position matching with high probability after multiple rounds of samples. CPE-DB is an adaptation of the original combinatorial pure exploration for multi-armed bandit (CPE-MAB) problem to the dueling bandit setting. We consider both the Borda winner and the Condorcet winner cases. For Borda winner, we establish a reduction of the problem to the original CPE-MAB setting and design PAC and exact algorithms that achieve both the sample complexity similar to that in the CPE-MAB setting (which is nearly optimal for a subclass of problems) and polynomial running time per round. For Condorcet winner, we first design a fully polynomial time approximation scheme (FPTAS) for the offline problem of finding the Condorcet winner with known winning probabilities, and then use the FPTAS as an oracle to design a novel pure exploration algorithm ${\sf CAR}$-${\sf Cond}$ with sample complexity analysis. ${\sf CAR}$-${\sf Cond}$ is the first algorithm with polynomial running time per round for identifying the Condorcet winner in CPE-DB.
Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications
Venue: arXiv cs.CY 2020
Year: 2020

Web-scale applications can ship code on a daily to weekly cadence. These applications rely on online metrics to monitor the health of new releases. Regressions in metric values need to be detected and diagnosed as early as possible to reduce the disruption to users and product owners. Regressions in metrics can surface due to a variety of reasons: genuine product regressions, changes in user population, and bias due to telemetry loss (or processing) are among the common causes. Diagnosing the cause of these metric regressions is costly for engineering teams as they need to invest time in finding the root cause of the issue as soon as possible. We present Lumos, a Python library built using the principles of AB testing to systematically diagnose metric regressions to automate such analysis. Lumos has been deployed across the component teams in Microsoft's Real-Time Communication applications Skype and Microsoft Teams. It has enabled engineering teams to detect 100s of real changes in metrics and reject 1000s of false alarms detected by anomaly detectors. The application of Lumos has resulted in freeing up as much as 95% of the time allocated to metric-based investigations. In this work, we open source Lumos and present our results from applying it to two different components within the RTC group over millions of sessions. This general library can be coupled with any production system to manage the volume of alerting efficiently.
Distance Correlation Sure Independence Screening for Accelerated Feature Selection in Parkinson's Disease Vocal Data
Venue: arXiv cs.LG 2020
Year: 2020

With the abundance of machine learning methods available and the temptation of using them all in an ensemble method, having a model-agnostic method of feature selection is incredibly alluring. Principal component analysis was developed in 1901 and has been a strong contender in this role since, but in the end is an unsupervised method. It offers no guarantee that the features that are selected have good predictive power because it does not know what is being predicted. To this end, Peng et al. developed the minimum redundancy-maximum relevance (mRMR) method in 2005. It uses the mutual information not only between predictors but also includes the mutual information with the response in its calculation. Estimating mutual information and entropy tend to be expensive and problematic endeavors, which leads to excessive processing times even for dataset that is approximately 750 by 750 in a Leave-One-Subject-Out jackknife situation. To remedy this, we use a method from 2012 called Distance Correlation Sure Independence Screening (DC-SIS) which uses the distance correlation measure of Székely et al. to select features that have the greatest dependence with the response. We show that this method produces statistically indistinguishable results to the mRMR selection method on Parkinson's Disease vocal diagnosis data 90 times faster.
Optimizing Interactive Systems via Data-Driven Objectives
Venue: arXiv cs.AI 2020
Year: 2020

Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior. However, it is often challenging to find an objective to optimize for interactive systems (e.g., policy learning in task-oriented dialog systems). Generally, such objectives are manually crafted and rarely capture complex user needs in an accurate manner. We propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. We introduce Interactive System Optimizer (ISO), a novel algorithm that uses these inferred objectives for optimization. Our main contribution is a new general principled approach to optimizing interactive systems using data-driven objectives. We demonstrate the high effectiveness of ISO over several simulations.
Successful implementation of discrete event simulation: the case of an Italian emergency department
Venue: arXiv cs.CY 2020
Year: 2020

This paper focuses on the study of a practical management problem faced by a healthcare {\it emergency department} (ED) located in the north of Italy. The objective of our study was to propose organisational changes in the selected ED, which admits approximately 7000 patients per month, aiming at improving key performance indicators related to patient satisfaction, such as the waiting time. Our study is based on a design thinking process that adopts a {\it discrete event simulation} (DES) model as the main tool for proposing changes. We used the DES model to propose and evaluate the impact of different improving scenarios. The model is based on historical data, on the observation of the current ED situation, and information obtained from the ED staff. The results obtained by the DES model have been compared with those related to the existing ED setting, and then validated by the ED managers. Based on the results we obtained, one of the tested scenarios was selected by the ED for implementation.
On the Interoperability of Decentralized Exposure Notification Systems
Venue: arXiv cs.DC 2020
Year: 2020

This report summarizes the requirements and proposes a high-level solution for interoperability across recently proposed COVID-19 exposure notification efforts. Our focus is on interoperability across exposure notification (EN) applications which are based on the decentralized Bluetooth Low Energy (BLE) protocol driven by Google/Apple Exposure Notifications API (including DP3T and similar protocols). We distinguish different interoperability use cases, such as worldwide public EN interoperability, as well as interoperability in the enterprise EN systems. This report also proposes an API and a backend implementation architecture for EN interoperability. Finally, we propose using a permissioned blockchain-based solution for managing EN backend certificates and configurations (without storing any users' data on the blockchain) for helping address EN interoperability challenges across different vendors.
Learning dynamics for improving control of overactuated flying systems
Venue: arXiv cs.RO 2020
Year: 2020

Overactuated omnidirectional flying vehicles are capable of generating force and torque in any direction, which is important for applications such as contact-based industrial inspection. This comes at the price of an increase in model complexity. These vehicles usually have non-negligible, repetitive dynamics that are hard to model, such as the aerodynamic interference between the propellers. This makes it difficult for high-performance trajectory tracking using a model-based controller. This paper presents an approach that combines a data-driven and a first-principle model for the system actuation and uses it to improve the controller. In a first step, the first-principle model errors are learned offline using a Gaussian Process (GP) regressor. At runtime, the first-principle model and the GP regressor are used jointly to obtain control commands. This is formulated as an optimization problem, which avoids ambiguous solutions present in a standard inverse model in overactuated systems, by only using forward models. The approach is validated using a tilt-arm overactuated omnidirectional flying vehicle performing attitude trajectory tracking. The results show that with our proposed method, the attitude trajectory error is reduced by 32% on average as compared to a nominal PID controller.
ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects
Venue: arXiv cs.CV 2020
Year: 2020

We revisit the problem of Object-Goal Navigation (ObjectNav). In its simplest form, ObjectNav is defined as the task of navigating to an object, specified by its label, in an unexplored environment. In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e.g., find a chair, by navigating to it. As the community begins to show increased interest in semantic goal specification for navigation tasks, a number of different often-inconsistent interpretations of this task are emerging. This document summarizes the consensus recommendations of this working group on ObjectNav. In particular, we make recommendations on subtle but important details of evaluation criteria (for measuring success when navigating towards a target object), the agent's embodiment parameters, and the characteristics of the environments within which the task is carried out. Finally, we provide a detailed description of the instantiation of these recommendations in challenges organized at the Embodied AI workshop at CVPR 2020 http://embodied-ai.org .
Downlink Analysis for Reconfigurable Intelligent Surfaces Aided NOMA Networks
Venue: arXiv cs.IT 2020
Year: 2020

By activating blocked users and altering successive interference cancellation (SIC) sequences, reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems. This work investigates the downlink performance of RIS-aided NOMA networks via stochastic geometry. We first introduce the unique path loss model for RIS reflecting channels. Then, we evaluate the angle distributions based on a Poisson cluster process (PCP) framework, which theoretically demonstrates that the angles of incidence and reflection are uniformly distributed. Lastly, we derive closed-form expressions for coverage probabilities of the paired NOMA users. Our results show that 1) RIS-aided NOMA networks perform better than the traditional NOMA networks; 2) the SIC order in NOMA systems can be altered since RISs are able to change the channel gains of NOMA users.
A Note on Over-Smoothing for Graph Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Graph Neural Networks (GNNs) have achieved a lot of success on graph-structured data. However, it is observed that the performance of graph neural networks does not improve as the number of layers increases. This effect, known as over-smoothing, has been analyzed mostly in linear cases. In this paper, we build upon previous results \cite{oono2019graph} to further analyze the over-smoothing effect in the general graph neural network architecture. We show when the weight matrix satisfies the conditions determined by the spectrum of augmented normalized Laplacian, the Dirichlet energy of embeddings will converge to zero, resulting in the loss of discriminative power. Using Dirichlet energy to measure "expressiveness" of embedding is conceptually clean; it leads to simpler proofs than \cite{oono2019graph} and can handle more non-linearities.
Safe Learning under Uncertain Objectives and Constraints
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we consider non-convex optimization problems under \textit{unknown} yet safety-critical constraints. Such problems naturally arise in a variety of domains including robotics, manufacturing, and medical procedures, where it is infeasible to know or identify all the constraints. Therefore, the parameter space should be explored in a conservative way to ensure that none of the constraints are violated during the optimization process once we start from a safe initialization point. To this end, we develop an algorithm called Reliable Frank-Wolfe (Reliable-FW). Given a general non-convex function and an unknown polytope constraint, Reliable-FW simultaneously learns the landscape of the objective function and the boundary of the safety polytope. More precisely, by assuming that Reliable-FW has access to a (stochastic) gradient oracle of the objective function and a noisy feasibility oracle of the safety polytope, it finds an $ε$-approximate first-order stationary point with the optimal ${\mathcal{O}}({1}/{ε^2})$ gradient oracle complexity (resp. $\tilde{\mathcal{O}}({1}/{ε^3})$ (also optimal) in the stochastic gradient setting), while ensuring the safety of all the iterates. Rather surprisingly, Reliable-FW only makes $\tilde{\mathcal{O}}(({d^2}/{ε^2})\log 1/δ)$ queries to the noisy feasibility oracle (resp. $\tilde{\mathcal{O}}(({d^2}/{ε^4})\log 1/δ)$ in the stochastic gradient setting) where $d$ is the dimension and $δ$ is the reliability parameter, tightening the existing bounds even for safe minimization of convex functions. We further specialize our results to the case that the objective function is convex. A crucial component of our analysis is to introduce and apply a technique called geometric shrinkage in the context of safe optimization.
CacheOut: Leaking Data on Intel CPUs via Cache Evictions
Venue: arXiv cs.CR 2020
Year: 2020

Recent transient-execution attacks, such as RIDL, Fallout, and ZombieLoad, demonstrated that attackers can leak information while it transits through microarchitectural buffers. Named Microarchitectural Data Sampling (MDS) by Intel, these attacks are likened to "drinking from the firehose", as the attacker has little control over what data is observed and from what origin. Unable to prevent the buffers from leaking, Intel issued countermeasures via microcode updates that overwrite the buffers when the CPU changes security domains. In this work we present CacheOut, a new microarchitectural attack that is capable of bypassing Intel's buffer overwrite countermeasures. We observe that as data is being evicted from the CPU's L1 cache, it is often transferred back to the leaky CPU buffers where it can be recovered by the attacker. CacheOut improves over previous MDS attacks by allowing the attacker to choose which data to leak from the CPU's L1 cache, as well as which part of a cache line to leak. We demonstrate that CacheOut can leak information across multiple security boundaries, including those between processes, virtual machines, user and kernel space, and from SGX enclaves.
Handling Spontaneous Traffic Variations in 5G+ via Offloading onto mmWave-Capable UAV `Bridges'
Venue: arXiv cs.NI 2020
Year: 2020

Unmanned aerial vehicles (UAVs) are increasingly employed for numerous public and civil applications, such as goods delivery, medicine, surveillance, and telecommunications. For the latter, UAVs with onboard communication equipment may help temporarily offload traffic onto the neighboring cells in fifth-generation networks and beyond (5G+). In this paper, we propose and evaluate the use of UAVs traveling over the area of interest to relieve congestion in 5G+ systems under spontaneous traffic fluctuations. To this end, we assess two inherently different offloading schemes, named routed and controlled UAV `bridging'. Using the tools of renewal theory and stochastic geometry, we analytically characterize these schemes in terms of the fraction of traffic demand that can be offloaded onto the UAV `bridge' as our parameter of interest. This framework accounts for the unique features of millimeter-wave (mmWave) radio propagation and city deployment types with potential line-of-sight (LoS) link blockage by buildings. We also introduce enhancements to the proposed schemes that significantly improve the offloading gains. Our findings offer evidence that the UAV `bridges' may be used for efficient traffic offloading in various urban scenarios.
Road surface detection and differentiation considering surface damages
Venue: arXiv cs.CV 2020
Year: 2020

A challenge still to be overcome in the field of visual perception for vehicle and robotic navigation on heavily damaged and unpaved roads is the task of reliable path and obstacle detection. The vast majority of the researches have as scenario roads in good condition, from developed countries. These works cope with few situations of variation on the road surface and even fewer situations presenting surface damages. In this paper we present an approach for road detection considering variation in surface types, identifying paved and unpaved surfaces and also detecting damage and other information on other road surface that may be relevant to driving safety. We also present a new Ground Truth with image segmentation, used in our approach and that allowed us to evaluate our results. Our results show that it is possible to use passive vision for these purposes, even using images captured with low cost cameras.
Control-Aware Representations for Model-based Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE) is a promising approach that addresses this challenge by embedding the observations into a lower-dimensional latent space, estimating the latent dynamics, and utilizing it to perform control in the latent space. Two important questions in this area are how to learn a representation that is amenable to the control problem at hand, and how to achieve an end-to-end framework for representation learning and control. In this paper, we take a few steps towards addressing these questions. We first formulate a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space. We call this model control-aware representation learning (CARL). We derive a loss function for CARL that has close connection to the prediction, consistency, and curvature (PCC) principle for representation learning. We derive three implementations of CARL. In the offline implementation, we replace the locally-linear control algorithm (e.g.,~iLQR) used by the existing LCE methods with a RL algorithm, namely model-based soft actor-critic, and show that it results in significant improvement. In online CARL, we interleave representation learning and control, and demonstrate further gain in performance. Finally, we propose value-guided CARL, a variation in which we optimize a weighted version of the CARL loss function, where the weights depend on the TD-error of the current policy. We evaluate the proposed algorithms by extensive experiments on benchmark tasks and compare them with several LCE baselines.
Learning Semantically Enhanced Feature for Fine-Grained Image Classification
Venue: arXiv cs.CV 2020
Year: 2020

We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we first achieve the sub-feature semantic by arranging feature channels of a CNN into different groups through channel permutation. Meanwhile, to enhance the discriminability of sub-features, the groups are guided to be activated on object parts with strong discriminability by a weighted combination regularization. Our approach is parameter parsimonious and can be easily integrated into the backbone model as a plug-and-play module for end-to-end training with only image-level supervision. Experiments verified the effectiveness of our approach and validated its comparable performance to the state-of-the-art methods. Code is available at https://github.com/cswluo/SEF
DeepMnemonic: Password Mnemonic Generation via Deep Attentive Encoder-Decoder Model
Venue: arXiv cs.CR 2020
Year: 2020

Strong passwords are fundamental to the security of password-based user authentication systems. In recent years, much effort has been made to evaluate password strength or to generate strong passwords. Unfortunately, the usability or memorability of the strong passwords has been largely neglected. In this paper, we aim to bridge the gap between strong password generation and the usability of strong passwords. We propose to automatically generate textual password mnemonics, i.e., natural language sentences, which are intended to help users better memorize passwords. We introduce \textit{DeepMnemonic}, a deep attentive encoder-decoder framework which takes a password as input and then automatically generates a mnemonic sentence for the password. We conduct extensive experiments to evaluate DeepMnemonic on the real-world data sets. The experimental results demonstrate that DeepMnemonic outperforms a well-known baseline for generating semantically meaningful mnemonic sentences. Moreover, the user study further validates that the generated mnemonic sentences by DeepMnemonic are useful in helping users memorize strong passwords.