OpenRaiser/Intern-Atlas huggingface dataset:
House-GAN++: Generative Adversarial Layout Refinement Networks
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement. A surprising discovery of our research is that a simple non-iterative training process, dubbed component-wise GT-conditioning, is effective in learning such a generator. The iterative generator also creates a new opportunity in further improving a metric of choice via meta-optimization techniques by controlling when to pass which input constraints during iterative layout refinement. Our qualitative and quantitative evaluation based on the three standard metrics demonstrate that the proposed system makes significant improvements over the current state-of-the-art, even competitive against the ground-truth floorplans, designed by professional architects.
Simulating time to event prediction with spatiotemporal echocardiography deep learning
Venue: arXiv cs.CV 2021
Year: 2021

Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring of individuals within the observation period. New methods for time-to-event prediction have been developed by extending the cox-proportional hazards model with neural networks. In this paper, to explore the feasibility of these methods when applied to deep learning with echocardiography videos, we utilize the Stanford EchoNet-Dynamic dataset with over 10,000 echocardiograms, and generate simulated survival datasets based on the expert annotated ejection fraction readings. By training on just the simulated survival outcomes, we show that spatiotemporal convolutional neural networks yield accurate survival estimates.
Detecting Extraneous Content in Podcasts
Venue: arXiv cs.CL 2021
Year: 2021

Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions. We present classifiers that leverage both textual and listening patterns in order to detect such content in podcast descriptions and audio transcripts. We demonstrate that our models are effective by evaluating them on the downstream task of podcast summarization and show that we can substantively improve ROUGE scores and reduce the extraneous content generated in the summaries.
Reconstructed spatial receptive field structures by reverse correlation technique explains the visual feature selectivity of units in deep convolutional neural networks
Venue: arXiv cs.NI 2021
Year: 2021

An important issue in dealing with Deep Convolutional Neural Networks (DCNN) is the 'black box problem', which represents the unknowns about internal information representation and processing, especially in the middle and higher layers. In this study, we adopted a systems neuroscience methodology to measure the visual feature selectivity and visualize the spatial receptive field of the units in VGG16. Orientation and spatial frequency tunings of each unit were measured using sinusoidal grating stimuli. The image category selectivity of each unit was also measured using natural image stimuli. The spatial structures of the receptive fields of all convolutional units were estimated by activation-weighted average (AWA) and activation-weighted covariance (AWC) analyses. In the middle layers (convolutional layers in block3 and block4), AWC analysis successfully reconstructed the receptive field that predicted the visual feature selectivity of the unit. Those results suggested the possibility that analyzing the reconstructed receptive field structure can be used to interpret the functional significance of the units and layers of a DCNN.
On Fast Computation of a Circulant Matrix-Vector Product
Venue: arXiv cs.DS 2021
Year: 2021

This paper deals with circulant matrices. It is shown that a circulant matrix can be multiplied by a vector in time O(n log(n)) in a ring with roots of unity without making use of an FFT algorithm. With our algorithm we achieve a speedup of a factor of about 2.25 for the multiplication of two polynomials with integer coefficients compared to multiplication by an FFT algorithm. Moreover this paper discusses multiplication of large integers as further application.
Critical Slowing Down Near Topological Transitions in Rate-Distortion Problems
Venue: arXiv cs.IT 2021
Year: 2021

In rate-distortion (RD) problems one seeks reduced representations of a source that meet a target distortion constraint. Such optimal representations undergo topological transitions at some critical rate values, when their cardinality or dimensionality change. We study the convergence time of the Arimoto-Blahut alternating projection algorithms, used to solve such problems, near those critical points, both for the rate-distortion and information bottleneck settings. We argue that they suffer from critical slowing down -- a diverging number of iterations for convergence -- near the critical points. This phenomenon can have theoretical and practical implications for both machine learning and data compression problems.
Experimental Body-input Three-stage DC offset Calibration Scheme for Memristive Crossbar
Venue: arXiv cs.ET 2021
Year: 2021

Reading several ReRAMs simultaneously in a neuromorphic circuit increases power consumption and limits scalability. Applying small inference read pulses is a vain attempt when offset voltages of the read-out circuit are decisively more. This paper presents an experimental validation of a three-stage calibration scheme to calibrate the DC offset voltage across the rows of the memristive crossbar. The proposed method is based on biasing the body terminal of one of the differential pair MOSFETs of the buffer through a series of cascaded resistor banks arranged in three stages: coarse, fine and finer stages. The circuit is designed in a 130 nm CMOS technology, where the OxRAM-based binary memristors are built on top of it. A dedicated PCB and other auxiliary boards have been designed for testing the chip. Experimental results validate the presented approach, which is only limited by mismatch and electrical noise.
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning
Venue: arXiv cs.AI 2021
Year: 2021

Advances in unmanned aerial vehicle (UAV) design have opened up applications as varied as surveillance, firefighting, cellular networks, and delivery applications. Additionally, due to decreases in cost, systems employing fleets of UAVs have become popular. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a Partially Observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm's operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release
Venue: arXiv cs.CR 2021
Year: 2021

Large organizations such as social media companies continually release data, for example user images. At the same time, these organizations leverage their massive corpora of released data to train proprietary models that give them an edge over their competitors. These two behaviors can be in conflict as an organization wants to prevent competitors from using their own data to replicate the performance of their proprietary models. We solve this problem by developing a data poisoning method by which publicly released data can be minimally modified to prevent others from train-ing models on it. Moreover, our method can be used in an online fashion so that companies can protect their data in real time as they release it.We demonstrate the success of our approach onImageNet classification and on facial recognition.
Robust PDF Files Forensics Using Coding Style
Venue: arXiv cs.CR 2021
Year: 2021

Identifying how a file has been created is often interesting in security. It can be used by both attackers and defenders. Attackers can exploit this information to tune their attacks and defenders can understand how a malicious file has been created after an incident. In this work, we want to identify how a PDF file has been created. This problem is important because PDF files are extremely popular: many organizations publish PDF files online and malicious PDF files are commonly used by attackers. Our approach to detect which software has been used to produce a PDF file is based on coding style: given patterns that are only created by certain PDF producers. We have analyzed the coding style of 900 PDF files produced using 11 PDF producers on 3 different Operating Systems. We have obtained a set of 192 rules which can be used to identify 11 PDF producers. We have tested our detection tool on 508836 PDF files published on scientific preprints servers. Our tool is able to detect certain producers with an accuracy of 100%. Its overall detection is still high (74%). We were able to apply our tool to identify how online PDF services work and to spot inconsistency.
Multiple-Channel Real Time Filtering for a Myoelectric Prosthetic Hand-Arm Robot System
Venue: arXiv cs.RO 2021
Year: 2021

On the base of the developed master-slave prosthetic hand-arm robot system, which is controlled mainly based on signals obtained from bending sensors fixed on the data glove, the first idea deduced was to develop and add a multi-dimensional filter into the original control system to make the control signals cleaner and more stable at real time. By going further, a second new idea was also proposed to predict new control information based on the combination of a new algorithm and prediction control theory. In order to fulfill the first idea properly, the possible methods to process data in real time, the different ways to produce Gaussian distributed random data, the way to combine the new algorithm with the previous complex program project, and the way to simplify and reduce the running time of the algorithm to maintain the high efficiency, the real time processing with multiple channels of the sensory system and the real-time performance of the control system were researched. Eventually, the experiment on the same provided robot system gives the results of the first idea and shows the improved performance of the filter comparing with the original control method.
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Venue: arXiv cs.LG 2021
Year: 2021

Labeling data for modern machine learning is expensive and time-consuming. Latent variable models can be used to infer labels from weaker, easier-to-acquire sources operating on unlabeled data. Such models can also be trained using labeled data, presenting a key question: should a user invest in few labeled or many unlabeled points? We answer this via a framework centered on model misspecification in method-of-moments latent variable estimation. Our core result is a bias-variance decomposition of the generalization error, which shows that the unlabeled-only approach incurs additional bias under misspecification. We then introduce a correction that provably removes this bias in certain cases. We apply our decomposition framework to three scenarios -- well-specified, misspecified, and corrected models -- to 1) choose between labeled and unlabeled data and 2) learn from their combination. We observe theoretically and with synthetic experiments that for well-specified models, labeled points are worth a constant factor more than unlabeled points. With misspecification, however, their relative value is higher due to the additional bias but can be reduced with correction. We also apply our approach to study real-world weak supervision techniques for dataset construction.
Diagrammatic Formalism for Complex Systems: More than One Way to Eventize a Railcar System
Venue: arXiv cs.SE 2021
Year: 2021

This paper is in the intersection of software engineering and system engineering, two intimately intertwined disciplines. A dominating theme in this paper is the integral conceptualization of systems at large, as well as an underlying concern with software systems. In the software development life cycle, challenges still exist in translating requirements into a design artifact and then into an implementation (e.g., coding), then validating the results. From our perspective, software engineering requires an integrating paradigm toward a unified modeling orientation. Many methodologies, languages, and tools exist for facilitating system development processes. This paper is a venture into project development. To focus the materials, we concentrate on Harel s novel (and classic) development environment, which integrates a scenario-based engineering object orientation and statecharts through developing a railcar system. The railcar system is used as a detailed sample of translating requirements into a design artifact and then into an implementation, then validating the result. The project is re-cased as a single integrated modeling endeavor to be contrasted with the scenario and statecharts development. The result of this scheme is an enriched understanding through experimenting with and contrasting various development methods of software projects.
Enabling Software Resilience in GPGPU Applications via Partial Thread Protection
Venue: arXiv cs.SE 2021
Year: 2021

Graphics Processing Units (GPUs) are widely used by various applications in a broad variety of fields to accelerate their computation but remain susceptible to transient hardware faults (soft errors) that can easily compromise application output. By taking advantage of a general purpose GPU application hierarchical organization in threads, warps, and cooperative thread arrays, we propose a methodology that identifies the resilience of threads and aims to map threads with the same resilience characteristics to the same warp. This allows engaging partial replication mechanisms for error detection/correction at the warp level. By exploring 12 benchmarks (17 kernels) from 4 benchmark suites, we illustrate that threads can be remapped into reliable or unreliable warps with only 1.63% introduced overhead (on average), and then enable selective protection via replication to those groups of threads that truly need it. Furthermore, we show that thread remapping to different warps does not sacrifice application performance. We show how this remapping facilitates warp replication for error detection and/or correction and achieves an average reduction of 20.61% and 27.15% execution cycles, respectively comparing to standard duplication/triplication.
Measuring Presence in Augmented Reality Environments: Design and a First Test of a Questionnaire
Venue: arXiv cs.HC 2021
Year: 2021

Augmented Reality (AR) enriches a user's real environment by adding spatially aligned virtual objects (3D models, 2D textures, textual annotations, etc) by means of special display technologies. These are either worn on the body or placed in the working environment. From a technical point of view, AR faces three major challenges: (1) to generate a high quality rendering, (2) to precisely register (in position and orientation) the virtual objects (VOs) with the real environment, and (3) to do so in interactive real-time (Regenbrecht, Wagner, and Baratoff, 2002). The goal is to create the impression that the VOs are part of the real environment. Therefore, and similar to definitions of virtual reality (Steuer, 1992), it makes sense to define AR from a psychological point of view: Augmented Reality conveys the impression that VOs are present in the real environment. In order to evaluate how well this goal is reached, a psychological measurement of this type of presence is necessary. In the following, we will describe technological features of AR systems that make a special questionnaire version necessary, describe our approach to the questionnaire development, and the data collection strategy. Finally we will present first results of the application of the questionnaire in a recent study with 385 participants.
Causal Channels
Venue: arXiv cs.IT 2021
Year: 2021

We consider causal models with two observed variables and one latent variables, each variable being discrete, with the goal of characterizing the possible distributions on outcomes that can result from controlling one of the observed variables. We optimize linear functions over the space of all possible interventional distributions, which allows us find properties of the interventional distribution even when we cannot uniquely identify what it is. We show that, under certain mild assumptions about the correlation between controlled variable and the latent variable, the resulting interventional distribution must be close to the observed conditional distribution in a quantitative sense. Specifically, we show that if the observed variables are sufficiently highly correlated, and the latent variable can only take on a small number of distinct values, then the variables will remain causally related after passing to the interventional distribution. Another result, possibly of more general interest, is a bound on the distance between the interventional distribution and the observed conditional distribution in terms of the mutual information between the controlled variable and the latent variable, which shows that the controlled variable and the latent variable must be tightly correlated for the interventional distribution to differ significantly from the observed distribution. We believe that this type of result may make it possible to rigorously consider 'weak' experiments, where the causal variable is not entirely independent from the environment, but only approximately so. More generally, we suggest a connection between the theory of causality to polynomial optimization, which give useful bounds on the space of interventional distributions.
Estimation and Planning of Exploration Over Grid Map Using A Spatiotemporal Model with Incomplete State Observations
Venue: arXiv cs.RO 2021
Year: 2021

Path planning over spatiotemporal models can be applied to a variety of applications such as UAVs searching for spreading wildfire in mountains or network of balloons in time-varying atmosphere deployed for inexpensive internet service. A notable aspect in such applications is the dynamically changing environment. However, path planning algorithms often assume static environments and only consider the vehicle's dynamics exploring the environment. We present a spatiotemporal model that uses a cross-correlation operator to consider spatiotemporal dependence. Also, we present an adaptive state estimator for path planning. Since the state estimation depends on the vehicle's path, the path planning needs to consider the trade-off between exploration and exploitation. We use a high-level decision-maker to choose an explorative path or an exploitative path. The overall proposed framework consists of an adaptive state estimator, a short-term path planner, and a high-level decision-maker. We tested the framework with a spatiotemporal model simulation where the state of each grid transits from normal, latent, and fire state. For the mission objective of visiting the grids with fire, the proposed framework outperformed the random walk (baseline) and the single-minded exploitation (or exploration) path.
Arraymetrics: Authentication Through Chaotic Antenna Array Geometries
Venue: arXiv cs.CR 2021
Year: 2021

Advances in computing have resulted in an emerging need for multi-factor authentication using an amalgamation of cryptographic and physical keys. This letter presents a novel authentication approach using a combination of signal and antenna activation sequences, and most importantly, perturbed antenna array geometries. Possible degrees of freedom in perturbing antenna array geometries affected physical properties and their detection are presented. Channel estimation for the plurality of validly authorized arrays is discussed. Accuracy is investigated as a function of signal-to-noise ratio (SNR) and number of authorized arrays. It is observed that the proposed authentication scheme can provide 1% false authentication rate at 10 dB SNR, while it is achieving less than 1% missed authentication rates.
IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Recommending relevant items to users is a crucial task on online communities such as Reddit and Twitter. For recommendation system, representation learning presents a powerful technique that learns embeddings to represent user behaviors and capture item properties. However, learning embeddings on online communities is a challenging task because the user interest keep evolving. This evolution can be captured from 1) interaction between user and item, 2) influence from other users in the community. The existing dynamic embedding models only consider either of the factors to update user embeddings. However, at a given time, user interest evolves due to a combination of the two factors. To this end, we propose Influence-aware and Attention-based Co-evolutionary Network (IACN). Essentially, IACN consists of two key components: interaction modeling and influence modeling layer. The interaction modeling layer is responsible for updating the embedding of a user and an item when the user interacts with the item. The influence modeling layer captures the temporal excitation caused by interactions of other users. To integrate the signals obtained from the two layers, we design a novel fusion layer that effectively combines interaction-based and influence-based embeddings to predict final user embedding. Our model outperforms the existing state-of-the-art models from various domains.