OpenRaiser/Intern-Atlas huggingface dataset:
Interpretable ECG classification via a query-based latent space traversal (qLST)
Venue: arXiv cs.LG 2021
Year: 2021

Electrocardiography (ECG) is an effective and non-invasive diagnostic tool that measures the electrical activity of the heart. Interpretation of ECG signals to detect various abnormalities is a challenging task that requires expertise. Recently, the use of deep neural networks for ECG classification to aid medical practitioners has become popular, but their black box nature hampers clinical implementation. Several saliency-based interpretability techniques have been proposed, but they only indicate the location of important features and not the actual features. We present a novel interpretability technique called qLST, a query-based latent space traversal technique that is able to provide explanations for any ECG classification model. With qLST, we train a neural network that learns to traverse in the latent space of a variational autoencoder trained on a large university hospital dataset with over 800,000 ECGs annotated for 28 diseases. We demonstrate through experiments that we can explain different black box classifiers by generating ECGs through these traversals.
Learning a Shared Model for Motorized Prosthetic Joints to Predict Ankle-Joint Motion
Venue: arXiv cs.RO 2021
Year: 2021

Control strategies for active prostheses or orthoses use sensor inputs to recognize the user's locomotive intention and generate corresponding control commands for producing the desired locomotion. In this paper, we propose a learning-based shared model for predicting ankle-joint motion for different locomotion modes like level-ground walking, stair ascent, stair descent, slope ascent, and slope descent without the need to classify between them. Features extracted from hip and knee joint angular motion are used to continuously predict the ankle angles and moments using a Feed-Forward Neural Network-based shared model. We show that the shared model is adequate for predicting the ankle angles and moments for different locomotion modes without explicitly classifying between the modes. The proposed strategy shows the potential for devising a high-level controller for an intelligent prosthetic ankle that can adapt to different locomotion modes.
Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry model
Venue: arXiv cs.CE 2021
Year: 2021

A flexible treatment for gas- and aerosol-phase chemical processes has been developed for models of diverse scale, from box models up to global models. At the core of this novel framework is an "abstracted aerosol representation" that allows a given chemical mechanism to be solved in atmospheric models with different aerosol representations (e.g., sectional, modal, or particle-resolved). This is accomplished by treating aerosols as a collection of condensed phases that are implemented according to the aerosol representation of the host model. The framework also allows multiple chemical processes (e.g., gas- and aerosol-phase chemical reactions, emissions, deposition, photolysis, and mass-transfer) to be solved simultaneously as a single system. The flexibility of the model is achieved by (1) using an object-oriented design that facilitates extensibility to new types of chemical processes and to new ways of representing aerosol systems; (2) runtime model configuration using JSON input files that permits making changes to any part of the chemical mechanism without recompiling the model; this widely used, human-readable format allows entire gas- and aerosol-phase chemical mechanisms to be described with as much complexity as necessary; and (3) automated comprehensive testing that ensures stability of the code as new functionality is introduced. Together, these design choices enable users to build a customized multiphase mechanism, without having to handle pre-processors, solvers or compilers. This new treatment compiles as a stand-alone library and has been deployed in the particle-resolved PartMC model and in the MONARCH chemical weather prediction system for use at regional and global scales. Results from the initial deployment will be discussed, along with future extension to more complex gas-aerosol systems, and the integration of GPU-based solvers.
Composing Energy Services in a Crowdsourced IoT Environment
Venue: arXiv cs.DC 2021
Year: 2021

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users' energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
Venue: arXiv cs.AI 2021
Year: 2021

With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning. However, the quality of solutions predicted by GNNs has not been well investigated in the literature. In this paper, we study the capability of GNNs in learning to solve Maximum Satisfiability (MaxSAT) problem, both from theoretical and practical perspectives. We build two kinds of GNN models to learn the solution of MaxSAT instances from benchmarks, and show that GNNs have attractive potential to solve MaxSAT problem through experimental evaluation. We also present a theoretical explanation of the effect that GNNs can learn to solve MaxSAT problem to some extent for the first time, based on the algorithmic alignment theory.
Versatile Inverse Reinforcement Learning via Cumulative Rewards
Venue: arXiv cs.LG 2021
Year: 2021

Inverse Reinforcement Learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert. Current approaches usually do this with generative and uni-modal models, meaning that they encode a single behavior. In the common setting, where there are various solutions to a problem and the experts show versatile behavior this severely limits the generalization capabilities of these methods. We propose a novel method for Inverse Reinforcement Learning that overcomes these problems by formulating the recovered reward as a sum of iteratively trained discriminators. We show on simulated tasks that our approach is able to recover general, high-quality reward functions and produces policies of the same quality as behavioral cloning approaches designed for versatile behavior.
Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study
Venue: arXiv cs.SI 2021
Year: 2021

In this study, Call Detail Records (CDRs), covering Budapest, for the month of June in 2016 has been analyzed. During this observation period, the 2016 UEFA European Football Championship took place, which affected significantly the habit of the residents, despite the fact that not a single match was played in the city. We evaluated the fans' behavior in Budapest, during and after the Hungarian matches, and found that the mobile phone network activity reflects the football fans' behavior, demonstrating the potential of mobile phone network data within a social sensing system. The Call Detail Records are enriched with mobile phone properties to analyze the subscribers' devices. Applying the device information (Type Allocation Code) from the activity records, the Subscriber Identity Modules, that do not operate in cell phones are omitted from mobility analyses, allowing to focus on people. The mobile phone price is proposed and evaluated as a socioeconomic indicator, and correlation between the phone price and the mobility customs have been found. We also found that, beside the cell phone price, the subscriber age and the subscription type also have an effect on the mobility. On the other hand, these do not seem to affect the interest in football.
Benchmarking Various ML Solutions in Complex Intent-Based Network Management Systems
Venue: arXiv cs.NI 2021
Year: 2021

Intent-based networking (IBN) solutions to managing complex ICT systems have become one of the key enablers of intelligent and autonomous network management. As the number of machine learning (ML) techniques deployed in IBN increases, it becomes increasingly important to understand their expected performance. Whereas IBN concepts are generally specific to the use case envisioned, the underlying platforms are generally heterogenous, comprised of complex processing units, including CPU/GPU, CPU/FPGA and CPU/TPU combinations, which needs to be considered when running the ML techniques chosen. We focus on a case study of IBNs in the so-called ICT supply chain systems, where multiple ICT artifacts are integrated in one system based on heterogeneous hardware platforms. Here, we are interested in the problem of benchmarking the computational performance of ML technique defined by the intents. Our benchmarking method is based on collaborative filtering techniques, relying on ML-based methods like Singular Value Decomposition and Stochastic Gradient Descent, assuming initial lack of explicit knowledge about the expected number of operations, framework, or the device processing characteristics. We show that it is possible to engineer a practical IBN system with various ML techniques with an accurate estimated performance based on data from a few benchmarks only.
Fast Computation of Hahn Polynomials for High Order Moments
Venue: arXiv cs.CV 2021
Year: 2021

Discrete Hahn polynomials (DHPs) and their moments are considered to be one of the efficient orthogonal moments and they are applied in various scientific areas such as image processing and feature extraction. Commonly, DHPs are used as object representation; however, they suffer from the problem of numerical instability when the moment order becomes large. In this paper, an efficient method for computation of Hahn orthogonal basis is proposed and applied to high orders. This paper developed a new mathematical model for computing the initial value of the DHP and for different values of DHP parameters ($α$ and $β$). In addition, the proposed method is composed of two recurrence algorithms with an adaptive threshold to stabilize the generation of the DHP coefficients. It is compared with state-of-the-art algorithms in terms of computational cost and the maximum size that can be correctly generated. The experimental results show that the proposed algorithm performs better in both parameters for wide ranges of parameter values of ($α$ and $β$) and polynomial sizes.
Overcoming Digital Gravity when using AI in Public Health Decisions
Venue: arXiv cs.AI 2021
Year: 2021

In popular usage, Data Gravity refers to the ability of a body of data to attract applications, services and other data. In this work we introduce a broader concept, "Digital Gravity" which includes not just data, but other elements of the AI/ML workflow. This concept is born out of our recent experiences in developing and deploying an AI-based decision support platform intended for use in a public health context. In addition to data, examples of additional considerations are compute (infrastructure and software), DevSecOps (personnel and practices), algorithms/programs, control planes, middleware (considered separately from programs), and even companies/service providers. We discuss the impact of Digital Gravity on the pathway to adoption and suggest preliminary approaches to conceptualize and mitigate the friction caused by it.
FILIP: Fine-grained Interactive Language-Image Pre-Training
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the cross-modal interaction either via the similarity of the global feature of each modality which misses sufficient information, or finer-grained interactions using cross/self-attention upon visual and textual tokens. However, cross/self-attention suffers from inferior efficiency in both training and inference. In this paper, we introduce a large-scale Fine-grained Interactive Language-Image Pre-training (FILIP) to achieve finer-level alignment through a cross-modal late interaction mechanism, which uses a token-wise maximum similarity between visual and textual tokens to guide the contrastive objective. FILIP successfully leverages the finer-grained expressiveness between image patches and textual words by modifying only contrastive loss, while simultaneously gaining the ability to pre-compute image and text representations offline at inference, keeping both large-scale training and inference efficient. Furthermore, we construct a new large-scale image-text pair dataset called FILIP300M for pre-training. Experiments show that FILIP achieves state-of-the-art performance on multiple downstream vision-language tasks including zero-shot image classification and image-text retrieval. The visualization on word-patch alignment further shows that FILIP can learn meaningful fine-grained features with promising localization ability.
Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels
Venue: arXiv cs.CV 2021
Year: 2021

Portrait mode is widely available on smartphone cameras to provide an enhanced photographic experience. One of the primary effects applied to images captured in portrait mode is a synthetic shallow depth of field (DoF). The synthetic DoF (or bokeh effect) selectively blurs regions in the image to emulate the effect of using a large lens with a wide aperture. In addition, many applications now incorporate a new image motion attribute (NIMAT) to emulate background motion, where the motion is correlated with estimated depth at each pixel. In this work, we follow the trend of rendering the NIMAT effect by introducing a modification on the blur synthesis procedure in portrait mode. In particular, our modification enables a high-quality synthesis of multi-view bokeh from a single image by applying rotated blurring kernels. Given the synthesized multiple views, we can generate aesthetically realistic image motion similar to the NIMAT effect. We validate our approach qualitatively compared to the original NIMAT effect and other similar image motions, like Facebook 3D image. Our image motion demonstrates a smooth image view transition with fewer artifacts around the object boundary.
Colored Noise Mechanism for Differentially Private Clustering
Venue: arXiv cs.CR 2021
Year: 2021

The goal of this paper is to propose and analyze a differentially private randomized mechanism for the $K$-means query. The goal is to ensure that the information received about the cluster-centroids is differentially private. The method consists in adding Gaussian noise with an optimum covariance. The main result of the paper is the analytical solution for the optimum covariance as a function of the database. Comparisons with the state of the art prove the efficacy of our approach.
Mitigating Divergence of Latent Factors via Dual Ascent for Low Latency Event Prediction Models
Venue: arXiv cs.LG 2021
Year: 2021

Real-world content recommendation marketplaces exhibit certain behaviors and are imposed by constraints that are not always apparent in common static offline data sets. One example that is common in ad marketplaces is swift ad turnover. New ads are introduced and old ads disappear at high rates every day. Another example is ad discontinuity, where existing ads may appear and disappear from the market for non negligible amounts of time due to a variety of reasons (e.g., depletion of budget, pausing by the advertiser, flagging by the system, and more). These behaviors sometimes cause the model's loss surface to change dramatically over short periods of time. To address these behaviors, fresh models are highly important, and to achieve this (and for several other reasons) incremental training on small chunks of past events is often employed. These behaviors and algorithmic optimizations occasionally cause model parameters to grow uncontrollably large, or \emph{diverge}. In this work present a systematic method to prevent model parameters from diverging by imposing a carefully chosen set of constraints on the model's latent vectors. We then devise a method inspired by primal-dual optimization algorithms to fulfill these constraints in a manner which both aligns well with incremental model training, and does not require any major modifications to the underlying model training algorithm. We analyze, demonstrate, and motivate our method on OFFSET, a collaborative filtering algorithm which drives Yahoo native advertising, which is one of VZM's largest and faster growing businesses, reaching a run-rate of many hundreds of millions USD per year. Finally, we conduct an online experiment which shows a substantial reduction in the number of diverging instances, and a significant improvement to both user experience and revenue.
Winning Solution of the AIcrowd SBB Flatland Challenge 2019-2020
Venue: arXiv cs.AI 2021
Year: 2021

This report describes the main ideas of the solution which won the AIcrowd SBB Flatland Challenge 2019-2020, with a score of 99% (meaning that, on average, 99% of the agents were routed to their destinations within the allotted time steps). The details of the task can be found on the competition's website. The solution consists of 2 major components: 1) A component which (re-)generates paths over a time-expanded graph for each agent 2) A component which updates the agent paths after a malfunction occurs, in order to try to preserve the same agent ordering of entering each cell as before the malfunction. The goal of this component is twofold: a) to (try to) avoid deadlocks b) to bring the system back to a consistent state (where each agent has a feasible path over the time-expanded graph). I am discussing both of these components, as well as a series of potentially promising, but unexplored ideas, below.
Target Layer Regularization for Continual Learning Using Cramer-Wold Generator
Venue: arXiv cs.LG 2021
Year: 2021

We propose an effective regularization strategy (CW-TaLaR) for solving continual learning problems. It uses a penalizing term expressed by the Cramer-Wold distance between two probability distributions defined on a target layer of an underlying neural network that is shared by all tasks, and the simple architecture of the Cramer-Wold generator for modeling output data representation. Our strategy preserves target layer distribution while learning a new task but does not require remembering previous tasks' datasets. We perform experiments involving several common supervised frameworks, which prove the competitiveness of the CW-TaLaR method in comparison to a few existing state-of-the-art continual learning models.
Natural Gradient Variational Inference with Gaussian Mixture Models
Venue: arXiv cs.LG 2021
Year: 2021

Bayesian methods estimate a measure of uncertainty by using the posterior distribution. One source of difficulty in these methods is the computation of the normalizing constant. Calculating exact posterior is generally intractable and we usually approximate it. Variational Inference (VI) methods approximate the posterior with a distribution usually chosen from a simple family using optimization. The main contribution of this work is described is a set of update rules for natural gradient variational inference with mixture of Gaussians, which can be run independently for each of the mixture components, potentially in parallel.
Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

In a multi-task reinforcement learning setting, the learner commonly benefits from training on multiple related tasks by exploiting similarities among them. At the same time, the trained agent is able to solve a wider range of different problems. While this effect is well documented for model-free multi-task methods, we demonstrate a detrimental effect when using a single learned dynamics model for multiple tasks. Thus, we address the fundamental question of whether model-based multi-task reinforcement learning benefits from shared dynamics models in a similar way model-free methods do from shared policy networks. Using a single dynamics model, we see clear evidence of task confusion and reduced performance. As a remedy, enforcing an internal structure for the learned dynamics model by training isolated sub-networks for each task notably improves performance while using the same amount of parameters. We illustrate our findings by comparing both methods on a simple gridworld and a more complex vizdoom multi-task experiment.
Assessing gender bias in medical and scientific masked language models with StereoSet
Venue: arXiv cs.CL 2021
Year: 2021

NLP systems use language models such as Masked Language Models (MLMs) that are pre-trained on large quantities of text such as Wikipedia create representations of language. BERT is a powerful and flexible general-purpose MLM system developed using unlabeled text. Pre-training on large quantities of text also has the potential to transparently embed the cultural and social biases found in the source text into the MLM system. This study aims to compare biases in general purpose and medical MLMs with the StereoSet bias assessment tool. The general purpose MLMs showed significant bias overall, with BERT scoring 57 and RoBERTa scoring 61. The category of gender bias is where the best performances were found, with 63 for BERT and 73 for RoBERTa. Performances for profession, race, and religion were similar to the overall bias scores for the general-purpose MLMs.Medical MLMs showed more bias in all categories than the general-purpose MLMs except for SciBERT, which showed a race bias score of 55, which was superior to the race bias score of 53 for BERT. More gender (Medical 54-58 vs. General 63-73) and religious (46-54 vs. 58) biases were found with medical MLMs. This evaluation of four medical MLMs for stereotyped assessments about race, gender, religion, and profession showed inferior performance to general-purpose MLMs. These medically focused MLMs differ considerably in training source data, which is likely the root cause of the differences in ratings for stereotyped biases from the StereoSet tool.
Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery
Venue: arXiv cs.SI 2021
Year: 2021

Characterizing the reputation of an evaluator is particularly significant for consumer to obtain useful information from online rating systems. Furthermore, to overcome the difficulties with spam attacks on the rating system and to get the reliable on reputation of evaluators is an important topic in the research. We have noticed that most of the existing evaluator reputation evaluation methods only rely on the evaluator's rating information and abnormal behavior to establish a reputation system, which miss the systematic aspects of the rating systems including the structure of the evaluator-object bipartite network and the effects of nonlinear effects. This study we propose an improved reputation evaluation method by combining the structure of the evaluator-object bipartite network with rating information and introducing penalty and reward factors. This novel method has been empirically analyzed on a large-scale artificial data set and two real data sets. The results show that the proposed method is more accurate and robust in the presence of spam attacks. This fresh idea contributes a new way for building reputation evaluation models in sparse bipartite rating network.