OpenRaiser/Intern-Atlas huggingface dataset:
Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN
Venue: arXiv cs.CV 2020
Year: 2020

Decoding and reconstructing images from brain imaging data is a research area of high interest. Recent progress in deep generative neural networks has introduced new opportunities to tackle this problem. Here, we employ a recently proposed large-scale bi-directional generative adversarial network, called BigBiGAN, to decode and reconstruct natural scenes from fMRI patterns. BigBiGAN converts images into a 120-dimensional latent space which encodes class and attribute information together, and can also reconstruct images based on their latent vectors. We computed a linear mapping between fMRI data, acquired over images from 150 different categories of ImageNet, and their corresponding BigBiGAN latent vectors. Then, we applied this mapping to the fMRI activity patterns obtained from 50 new test images from 50 unseen categories in order to retrieve their latent vectors, and reconstruct the corresponding images. Pairwise image decoding from the predicted latent vectors was highly accurate (84%). Moreover, qualitative and quantitative assessments revealed that the resulting image reconstructions were visually plausible, successfully captured many attributes of the original images, and had high perceptual similarity with the original content. This method establishes a new state-of-the-art for fMRI-based natural image reconstruction, and can be flexibly updated to take into account any future improvements in generative models of natural scene images.
Exploiting RapidWright in the Automatic Generation of Application-Specific FPGA Overlays
Venue: arXiv cs.AR 2020
Year: 2020

Overlay architectures implemented on FPGA devices have been proposed as a means to increase FPGA adoption in general-purpose computing. They provide the benefits of software such as flexibility and programmability, thus making it easier to build dedicated compilers. However, existing overlays are generic, resource and power hungry with performance usually an order of magnitude lower than bare metal implementations. As a result, FPGA overlays have been confined to research and some niche applications. In this paper, we introduce Application-Specific FPGA Overlays (AS-Overlays), which can provide bare-metal performance to FPGA overlays, thus opening doors for broader adoption. Our approach is based on the automatic extraction of hardware kernels from data flow applications. Extracted kernels are then leveraged for application-specific generation of hardware accelerators. Reconfiguration of the overlay is done with RapidWright which allows to bypass the HDL design flow. Through prototyping, we demonstrated the viability and relevance of our approach. Experiments show a productivity improvement up to 20x compared to the state of the art FPGA overlays, while achieving over 1.33x higher Fmax than direct FPGA implementation and the possibility of lower resource and power consumption compared to bare metal.
A Tool for Conducting User Studies on Mobile Devices
Venue: arXiv cs.IR 2020
Year: 2020

With the ever-growing interest in the area of mobile information retrieval and the ongoing fast development of mobile devices and, as a consequence, mobile apps, an active research area lies in studying users' behavior and search queries users submit on mobile devices. However, many researchers require to develop an app that collects useful information from users while they search on their phones or participate in a user study. In this paper, we aim to address this need by providing a comprehensive Android app, called Omicron, which can be used to collect mobile query logs and perform user studies on mobile devices. Omicron, at its current version, can collect users' mobile queries, relevant documents, sensor data as well as user activity and interaction data in various study settings. Furthermore, we designed Omicron in such a way that it is conveniently extendable to conduct more specific studies and collect other types of sensor data. Finally, we provide a tool to monitor the participants and their data both during and after the collection process.
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning
Venue: arXiv cs.CV 2020
Year: 2020

Understanding how goal states control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train models. To annotate a large-scale image dataset with observed search fixations, we collected 16,184 fixations from people searching for either microwaves or clocks in a dataset of 4,366 images (MS-COCO). We then used this behaviorally-annotated dataset and the machine learning method of Inverse-Reinforcement Learning (IRL) to learn target-specific reward functions and policies for these two target goals. Finally, we used these learned policies to predict the fixations of 60 new behavioral searchers (clock = 30, microwave = 30) in a disjoint test dataset of kitchen scenes depicting both a microwave and a clock (thus controlling for differences in low-level image contrast). We found that the IRL model predicted behavioral search efficiency and fixation-density maps using multiple metrics. Moreover, reward maps from the IRL model revealed target-specific patterns that suggest, not just attention guidance by target features, but also guidance by scene context (e.g., fixations along walls in the search of clocks). Using machine learning and the psychologically-meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.
Documentation of Machine Learning Software
Venue: arXiv cs.SE 2020
Year: 2020

Machine Learning software documentation is different from most of the documentations that were studied in software engineering research. Often, the users of these documentations are not software experts. The increasing interest in using data science and in particular, machine learning in different fields attracted scientists and engineers with various levels of knowledge about programming and software engineering. Our ultimate goal is automated generation and adaptation of machine learning software documents for users with different levels of expertise. We are interested in understanding the nature and triggers of the problems and the impact of the users' levels of expertise in the process of documentation evolution. We will investigate the Stack Overflow Q/As and classify the documentation related Q/As within the machine learning domain to understand the types and triggers of the problems as well as the potential change requests to the documentation. We intend to use the results for building on top of the state of the art techniques for automatic documentation generation and extending on the adoption, summarization, and explanation of software functionalities.
Modeling Events and Events of Events in Software Engineering
Venue: arXiv cs.SE 2020
Year: 2020

A model is a simplified representation of portion of reality that hides a system s nonessential characteristics. It provides a means for reducing complexity as well as visualization and communication and a basis for building it. Most models involve graphic languages during many of the software lifecycle phases. A new model, called thinging machine (TM), has recently been developed as an extension of the input-process-output framework. The paper focuses on events in a TM, offering a new perspective that captures a system s dynamic behaviors and a means of diagrammatically modeling events. The event notion is an important factor in giving semantics to specifications and providing a natural way to specify the interfaces and observable behavior of system components. Specifically, five generic TM event processes are analyzed: create, process, receive, release, and transfer. All events can be mapped (or reduced) to the events of these five event processes
Lifted Reed-Solomon Codes with Application to Batch Codes
Venue: arXiv cs.IT 2020
Year: 2020

Guo, Kopparty and Sudan have initiated the study of error-correcting codes derived by lifting of affine-invariant codes. Lifted Reed-Solomon (RS) codes are defined as the evaluation of polynomials in a vector space over a field by requiring their restriction to every line in the space to be a codeword of the RS code. In this paper, we investigate lifted RS codes and discuss their application to batch codes, a notion introduced in the context of private information retrieval and load-balancing in distributed storage systems. First, we improve the estimate of the code rate of lifted RS codes for lifting parameter $m\ge 3$ and large field size. Second, a new explicit construction of batch codes utilizing lifted RS codes is proposed. For some parameter regimes, our codes have a better trade-off between parameters than previously known batch codes.
Algorithmic Aspects of Some Variants of Domination in Graphs
Venue: arXiv cs.DM 2020
Year: 2020

A set $S \subseteq V$ is a dominating set in G if for every u \in V \ S, there exists $v \in S$ such that $(u,v) \in E$, i.e., $N[S] = V$. A dominating set $S$ is an Isolate Dominating Set} (IDS) if the induced subgraph $G[S]$ has at least one isolated vertex. It is known that Isolate Domination Decision problem (IDOM) is NP-complete for bipartite graphs. In this paper, we extend this by showing that the IDOM is NP-complete for split graphs and perfect elimination bipartite graphs, a subclass of bipartite graphs. A set $S \subseteq V$ is an independent set if G[S] has no edge. A set S \subseteq V is a secure dominating set of $G$ if, for each vertex $u \in V \setminus S$, there exists a vertex $v \in S$ such that $ (u,v) \in E $ and $(S \ \{v\}) \cup \{u\}$ is a dominating set of $G$. In addition, we initiate the study of a new domination parameter called, independent secure domination. A set $S\subseteq V$ is an Independent Secure Dominating Set (InSDS) if $S$ is an independent set and a secure dominating set of $G$. The minimum size of an InSDS in $G$ is called the independent secure domination number of $G$ and is denoted by $γ_{is}(G)$. Given a graph $ G$ and a positive integer $ k,$ the InSDM problem is to check whether $ G $ has an independent secure dominating set of size at most $ k.$ We prove that InSDM is NP-complete for bipartite graphs and linear time solvable for bounded tree-width graphs and threshold graphs, a subclass of split graphs. The MInSDS problem is to find an independent secure dominating set of minimum size, in the input graph. Finally, we prove that the MInSDS problem is APX-hard for graphs with maximum degree $5.$
On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views
Venue: arXiv cs.IT 2020
Year: 2020

This tutorial paper focuses on the variants of the bottleneck problem taking an information theoretic perspective and discusses practical methods to solve it, as well as its connection to coding and learning aspects. The intimate connections of this setting to remote source-coding under logarithmic loss distortion measure, information combining, common reconstruction, the Wyner-Ahlswede-Korner problem, the efficiency of investment information, as well as, generalization, variational inference, representation learning, autoencoders, and others are highlighted. We discuss its extension to the distributed information bottleneck problem with emphasis on the Gaussian model and highlight the basic connections to the uplink Cloud Radio Access Networks (CRAN) with oblivious processing. For this model, the optimal trade-offs between relevance (i.e., information) and complexity (i.e., rates) in the discrete and vector Gaussian frameworks is determined. In the concluding outlook, some interesting problems are mentioned such as the characterization of the optimal inputs ("features") distributions under power limitations maximizing the "relevance" for the Gaussian information bottleneck, under "complexity" constraints.
Design Principles and Clinician Preferences for Pharmacogenomic Clinical Decision Support Alerts
Venue: arXiv cs.HC 2020
Year: 2020

OBJECTIVE: To better understand clinician needs and preferences for the display of pharmacogenomic (PGx) information in clinical decision support (CDS) tools. MATERIALS AND METHODS: We developed a semi-structured interview to collect feedback and preferences in six key areas of PGx CDS design, from clinicians who had prior experience with live PGx CDS tools. Eight clinicians from Northwestern Medicine's (NM) General Internal Medicine clinic participated in the study. RESULTS: Clinicians expressed preference for interruptive pop-up alerts during order entry, brief descriptions of relevant drug-gene interactions, and a clear and specific recommended alternative course of action when a medication is contraindicated. They did not wish to see detailed genetic data, preferring phenotypic information predicted from the genotype. Nor did they wish to be interrupted when genetic test results do not indicate a change in treatment plan. Clinicians reported little familiarity with Clinical Pharmacogenetic Implementation Consortium prescribing recommendations but reported trusting recommendations of their professional societies and resources like UpToDate. Analysis of unstructured comments concurred with structured results, indicating a general uncertainty among participants around how to interpret and apply PGx information in practice. DISCUSSION: Results point to several underlying principles that can inform future PGx CDS alert designs: Be Specific and Actionable; Be Brief; Display Phenotypes not Genotypes; Rely on Sources Clinicians Already Trust; and, Be Adaptable to Learning Effects. CONCLUSION: This study is part of a broader socio-technical design approach to PGx CDS design underway at NM and provides a baseline for future PGx CDS development. Designs based on these results have the potential to improve clinician education and adherence levels, and to improve patient outcomes.
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
Venue: arXiv cs.LG 2020
Year: 2020

In this paper we study the smooth convex-concave saddle point problem. Specifically, we analyze the last iterate convergence properties of the Extragradient (EG) algorithm. It is well known that the ergodic (averaged) iterates of EG converge at a rate of $O(1/T)$ (Nemirovski, 2004). In this paper, we show that the last iterate of EG converges at a rate of $O(1/\sqrt{T})$. To the best of our knowledge, this is the first paper to provide a convergence rate guarantee for the last iterate of EG for the smooth convex-concave saddle point problem. Moreover, we show that this rate is tight by proving a lower bound of $Ω(1/\sqrt{T})$ for the last iterate. This lower bound therefore shows a quadratic separation of the convergence rates of ergodic and last iterates in smooth convex-concave saddle point problems.
Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive $p$-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. To learn an optimal control policy, we first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy among UAVs in which the altitude of UAVs and channel access probability of IoT devices are dynamically and jointly controlled to attain the maximal long-term network capacity while maintaining energy sustainability of UAVs. The proposed algorithm outperforms Deep RL based solutions with reward shaping to account for energy costs, and achieves a temporal average system capacity which is $82.4\%$ higher than that of a feasible DRL based solution, and only $6.47\%$ lower compared to that of the energy-constraint-free system.
Dissecting the Workload of a Major Adult Video Portal
Venue: arXiv cs.CY 2020
Year: 2020

Adult content constitutes a major source of Internet traffic. As with many other platforms, these sites are incentivized to engage users and maintain them on the site. This engagement (e.g., through recommendations) shapes the journeys taken through such sites. Using data from a large content delivery network, we explore session journeys within an adult website. We take two perspectives. We first inspect the corpus available on these platforms. Following this, we investigate the session access patterns. We make a number of observations that could be exploited for optimizing delivery, e.g., that users often skip within video streams.
Predicting IoT Service Adoption towards Smart Mobility in Malaysia: SEM-Neural Hybrid Pilot Study
Venue: arXiv cs.HC 2020
Year: 2020

Smart city is synchronized with digital environment and its transportation system is vitalized with RFID sensors, Internet of Things (IoT) and Artificial Intelligence. However, without user's behavioral assessment of technology, the ultimate usefulness of smart mobility cannot be achieved. This paper aims to formulate the research framework for prediction of antecedents of smart mobility by using SEM-Neural hybrid approach towards preliminary data analysis. This research undertook smart mobility services adoption in Malaysia as study perspective and applied the Technology Acceptance Model (TAM) as theoretical basis. An extended TAM model was hypothesized with five external factors (digital dexterity, IoT service quality, intrusiveness concerns, social electronic word of mouth and subjective norm). The data was collected through a pilot survey in Klang Valley, Malaysia. Then responses were analyzed for reliability, validity and accuracy of model. Finally, the causal relationship was explained by Structural Equation Modeling (SEM) and Artificial Neural Networking (ANN). The paper will share better understanding of road technology acceptance to all stakeholders to refine, revise and update their policies. The proposed framework will suggest a broader approach to individual level technology acceptance.
Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis
Venue: arXiv cs.LG 2020
Year: 2020

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays. In addition, we propose methods for inferring both variable-lag Granger causality and Transfer Entropy relations. In our approaches, we utilize an optimal warping path of Dynamic Time Warping (DTW) to infer variable-lag causal relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. The software of this work is available in the R-CRAN package: VLTimeCausality.
Palpatine: Mining Frequent Sequences for Data Prefetching in NoSQL Distributed Key-Value Stores
Venue: arXiv cs.DC 2020
Year: 2020

This paper presents PALPATINE, the first in-memory application-level cache for Distributed Key-Value (DKV) data stores, capable of prefetching data that is likely to be accessed in an immediate future. To predict data accesses, PALPATINE continuously captures frequent access patterns to the back store by means of data mining techniques. With these patterns, PALPATINE builds a stochastic graph of accessed items, and makes prefetching decisions based on it. Experimental evaluation indicates that PALPATINE can improve the latency of a specific DKV store by more that an order of magnitude.
Bayesian Networks in Healthcare: Distribution by Medical Condition
Venue: arXiv cs.CY 2020
Year: 2020

Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare. Hitherto, research works have not investigated the types of medical conditions being modelled with BNs, nor whether any differences exist in how and why they are applied to different conditions. This research seeks to identify and quantify the range of medical conditions for which healthcare-related BN models have been proposed, and the differences in approach between the most common medical conditions to which they have been applied. We found that almost two-thirds of all healthcare BNs are focused on four conditions: cardiac, cancer, psychological and lung disorders. We believe that a lack of understanding regarding how BNs work and what they are capable of exists, and that it is only with greater understanding and promotion that we may ever realise the full potential of BNs to effect positive change in daily healthcare practice.