Reviewerly/exHarmony huggingface dataset:
Tabular Structure Detection from Document Images for Resource Constrained Devices Using A Row Based Similarity Measure

Tabular structures are used to present crucial information in a structured and crisp manner. Detection of such regions is of great importance for proper understanding of a document. Tabular structures can be of various layouts and types. Therefore, detection of these regions is a hard problem. Most of the existing techniques detect tables from a document image by using prior knowledge of the structures of the tables. However, these methods are not applicable for generalized tabular structures. In this work, we propose a similarity measure to find similarities between pairs of rows in a tabular structure. This similarity measure is utilized to identify a tabular region. Since the tabular regions are detected exploiting the similarities among all rows, the method is inherently independent of layouts of the tabular regions present in the training data. Moreover, the proposed similarity measure can be used to identify tabular regions without using large sets of parameters associated with recent deep learning based methods. Thus, the proposed method can easily be used with resource constrained devices such as mobile devices without much of an overhead.
A photon model based upon chaos produced by static, Schwinger-level electric field nonlinearities that satisfies all first-order properties

In this work we postulate that Schwinger's threshold for a dynamic electric field intensity to induce spatial nonlinearity is a special case and, more generally, it is the threshold field for both static and dynamic electric fields. Fields of this magnitude induce negative energy charges to adapt positive energy attributes; within an atom they also support inter-state energy transfers and intra-state chaotic mixing of time-varying fields. Nonlinearity-induced chaos forms the basis for the probabilistic nature of photon creation. Answers to physical problems at atomic and lower scales continuously evolve because chaotic-like electron movements change their configurations on a time scale of 10 zs. Within atoms, frequency mixing that creates an optical frequency field occurs in the nonlinear region surrounding the nucleus. On a probabilistic basis a ring of vacuum charge can be induced that forms into an equivalent waveguide that confines the energy as it travels permanently away from the atom. The propagating relativistically augmented fields losslessly induce charges that bind and protect the energy carrying fields. The photon charge-field ensemble, which we show is localizable, is thermodynamically closed and possesses all first-order photon properties including zero rest mass and permanent stability. For near neighbor photons traveling at a speed approaching c we find a small, constant, attractive force between photons with circularly antiparallel polarization.
Failures and Fixes: A Study of Software System Incident Response

This paper presents the results of a research study related to software system failures, with the goal of understanding how we might better evolve, maintain and support software systems in production. We have qualitatively analyzed thirty incidents: fifteen collected through in depth interviews with engineers, and fifteen sampled from publicly published incident reports (generally produced as part of postmortem reviews). Our analysis focused on understanding and categorizing how failures occurred, and how they were detected, investigated and mitigated. We also captured analytic insights related to the current state of the practice and associated challenges in the form of 11 key observations. For example, we observed that failures can cascade through a system leading to major outages; and that often engineers do not understand the scaling limits of systems they are supporting until those limits are exceeded. We argue that the challenges we have identified can lead to improvements to how systems are engineered and supported.
Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis

Deep Q-Learning is an important reinforcement learning algorithm, which involves training a deep neural network, called Deep Q-Network (DQN), to approximate the well-known Q-function. Although wildly successful under laboratory conditions, serious gaps between theory and practice as well as a lack of formal guarantees prevent its use in the real world. Adopting a dynamical systems perspective, we provide a theoretical analysis of a popular version of Deep Q-Learning under realistic and verifiable assumptions. More specifically, we prove an important result on the convergence of the algorithm, characterizing the asymptotic behavior of the learning process. Our result sheds light on hitherto unexplained properties of the algorithm and helps understand empirical observations, such as performance inconsistencies even after training. Unlike previous theories, our analysis accommodates state Markov processes with multiple stationary distributions. In spite of the focus on Deep Q-Learning, we believe that our theory may be applied to understand other deep learning algorithms
Why Shouldn't All Charts Be Scatter Plots? Beyond Precision-Driven Visualizations

A central concept in information visualization research and practice is the notion of visual variable effectiveness, or the perceptual precision at which values are decoded given visual channels of encoding. Formative work from Cleveland & McGill has shown that position along a common axis is the most effective visual variable for comparing individual values. One natural conclusion is that any chart that is not a dot plot or scatterplot is deficient and should be avoided. In this paper we refute a caricature of this "scatterplots only" argument as a way to call for new perspectives on how information visualization is researched, taught, and evaluated.
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification

Learning embeddings that are invariant to the pose of the object is crucial in visual image retrieval and re-identification. The existing approaches for person, vehicle, or animal re-identification tasks suffer from high intra-class variance due to deformable shapes and different camera viewpoints. To overcome this limitation, we propose to align the image embedding with a predefined order of the keypoints. The proposed keypoint aligned embeddings model (KAE-Net) learns part-level features via multi-task learning which is guided by keypoint locations. More specifically, KAE-Net extracts channels from a feature map activated by a specific keypoint through learning the auxiliary task of heatmap reconstruction for this keypoint. The KAE-Net is compact, generic and conceptually simple. It achieves state of the art performance on the benchmark datasets of CUB-200-2011, Cars196 and VeRi-776 for retrieval and re-identification tasks.
Avoiding Negative Side Effects due to Incomplete Knowledge of AI Systems

Autonomous agents acting in the real-world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model -- handcrafted or machine acquired -- is inevitable due to practical limitations of any modeling technique for complex real-world settings. Due to the limited fidelity of its model, an agent's actions may have unexpected, undesirable consequences during execution. Learning to recognize and avoid such negative side effects of an agent's actions is critical to improve the safety and reliability of autonomous systems. Mitigating negative side effects is an emerging research topic that is attracting increased attention due to the rapid growth in the deployment of AI systems and their broad societal impacts. This article provides a comprehensive overview of different forms of negative side effects and the recent research efforts to address them. We identify key characteristics of negative side effects, highlight the challenges in avoiding negative side effects, and discuss recently developed approaches, contrasting their benefits and limitations. The article concludes with a discussion of open questions and suggestions for future research directions.
Automated Trajectory Synthesis for UAV Swarms Based on Resilient Data Collection Objectives

The use of Unmanned Aerial Vehicles (UAVs) for collecting data from remotely located sensor systems is emerging. The data can be time-sensitive and require to be transmitted to a data processing center. However, planning the trajectory of a collaborative UAV swarm depends on multi-fold constraints, such as data collection requirements, UAV maneuvering capacity, and budget limitation. Since a UAV may fail or be compromised, it is important to provide necessary resilience to such contingencies, thus ensuring data security. It is important to provide the UAVs with efficient spatio-temporal trajectories so that they can efficiently cover necessary data sources. In this work, we present Synth4UAV, a formal approach for automated synthesis of efficient trajectories for a UAV swarm by logically modeling the aerial space and data point topology, UAV moves, and associated constraints in terms of the turning and climbing angle, fuel usage, data collection point coverage, data freshness, and resiliency properties. We use efficient, logical formulas to encode and solve the complex model. The solution to the model provides the routing and maneuvering plan for each UAV, including the time to visit the points on the paths and corresponding fuel usage such that the necessary data points are visited while satisfying the resiliency requirements. We evaluate the proposed trajectory synthesizer, and the results show that the relationship among different parameters follow the requirements while the tool scales well with the problem size.
Inpainting-based Video Compression in FullHD

Compression methods based on inpainting are an evolving alternative to classical transform-based codecs for still images. Attempts to apply these ideas to video compression are rare, since reaching real-time performance is very challenging. Therefore, current approaches focus on simplified frame-by-frame reconstructions that ignore temporal redundancies. As a remedy, we propose a highly efficient, real-time capable prediction and correction approach that fully relies on partial differential equations (PDEs) in all steps of the codec: Dense variational optic flow fields yield accurate motion-compensated predictions, while homogeneous diffusion inpainting is applied for intra prediction. To compress residuals, we introduce a new highly efficient block-based variant of pseudodifferential inpainting. Our novel architecture outperforms other inpainting-based video codecs in terms of both quality and speed. For the first time in inpainting-based video compression, we can decompress FullHD (1080p) videos in real-time with a fully CPU-based implementation, outperforming previous approaches by roughly one order of magnitude.
Automated Search for Resource-Efficient Branched Multi-Task Networks

The multi-modal nature of many vision problems calls for neural network architectures that can perform multiple tasks concurrently. Typically, such architectures have been handcrafted in the literature. However, given the size and complexity of the problem, this manual architecture exploration likely exceeds human design abilities. In this paper, we propose a principled approach, rooted in differentiable neural architecture search, to automatically define branching (tree-like) structures in the encoding stage of a multi-task neural network. To allow flexibility within resource-constrained environments, we introduce a proxyless, resource-aware loss that dynamically controls the model size. Evaluations across a variety of dense prediction tasks show that our approach consistently finds high-performing branching structures within limited resource budgets.
Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data

The security of private information is becoming the bedrock of an increasingly digitized society. While the users are flooded with passwords and PINs, these gold-standard explicit authentications are becoming less popular and valuable. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. However, these hard-biometric-based systems require dedicated devices with powerful sensors and authentication models, which are often limited to most of the market wearables. Still, market wearables are collecting various private information of a user and are becoming an integral part of life: accessing cars, bank accounts, etc. Therefore, time demands a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from modern market wearables. In this work, we present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals. From our detailed analysis using the "leave-one-out" validation, we find that a lighter $k$-Nearest Neighbor ($k$-NN) model with $k = 2$ can obtain an average accuracy of $0.93 \pm 0.06$, $F_1$ score $0.93 \pm 0.03$, and {\em false positive rate} (FPR) below $0.08$ at 50\% level of confidence, which shows the promise of this work.
CycleGAN Voice Conversion of Spectral Envelopes using Adversarial Weights

This paper tackles GAN optimization and stability issues in the context of voice conversion. First, to simplify the conversion task, we propose to use spectral envelopes as inputs. Second we propose two adversarial weight training paradigms, the generalized weighted GAN and the generator impact GAN, both aim at reducing the impact of the generator on the discriminator, so both can learn more gradually and efficiently during training. Applying an energy constraint to the cycleGAN paradigm considerably improved conversion quality. A subjective experiment conducted on a voice conversion task on the voice conversion challenge 2018 dataset shows first that despite a significantly reduced network complexity, the proposed method achieves state-of-the-art results, and second that the proposed weighted GAN methods outperform a previously proposed one.
Gentzen-Mints-Zucker duality

The Curry-Howard correspondence is often described as relating proofs (in intutionistic natural deduction) to programs (terms in simply-typed lambda calculus). However this narrative is hardly a perfect fit, due to the computational content of cut-elimination and the logical origins of lambda calculus. We revisit Howard's work and interpret it as an isomorphism between a category of proofs in intuitionistic sequent calculus and a category of terms in simply-typed lambda calculus. In our telling of the story the fundamental duality is not between proofs and programs but between local (sequent calculus) and global (lambda calculus or natural deduction) points of view on a common logico-computational mathematical structure.
Möbius cancellation on polynomial sequences and the quadratic Bateman-Horn conjecture over function fields

We establish cancellation in short sums of certain special trace functions over $\mathbb{F}_q[u]$ below the P\'{o}lya-Vinogradov range, with savings approaching square-root cancellation as $q$ grows. This is used to resolve the $\mathbb{F}_q[u]$-analog of Chowla's conjecture on cancellation in M\"{o}bius sums over polynomial sequences, and of the Bateman-Horn conjecture in degree $2$, for some values of $q$. A final application is to sums of trace functions over primes in $\mathbb{F}_q[u]$.
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection

Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivates the development of weakly supervised and few-shot object detection methods. However, these methods largely underperform with respect to their strongly supervised counterpart, as weak training signals \emph{often} result in partial or oversized detections. Towards solving this problem we introduce, for the first time, an online annotation module (OAM) that learns to generate a many-shot set of \emph{reliable} annotations from a larger volume of weakly labelled images. Our OAM can be jointly trained with any fully supervised two-stage object detection method, providing additional training annotations on the fly. This results in a fully end-to-end strategy that only requires a low-shot set of fully annotated images. The integration of the OAM with Fast(er) R-CNN improves their performance by $17\%$ mAP, $9\%$ AP50 on PASCAL VOC 2007 and MS-COCO benchmarks, and significantly outperforms competing methods using mixed supervision.
Turkish Text Classification: From Lexicon Analysis to Bidirectional Transformer

Text classification has seen an increased use in both academic and industry settings. Though rule based methods have been fairly successful, supervised machine learning has been shown to be most successful for most languages, where most research was done on English. In this article, the success of lexicon analysis, support vector machines, and extreme gradient boosting for the task of text classification and sentiment analysis are evaluated in Turkish and a pretrained transformer based classifier is proposed, outperforming previous methods for Turkish text classification. In the context of text classification, all machine learning models proposed in the article are domain-independent and do not require any task-specific modifications.
MPCC: Matching Priors and Conditionals for Clustering

Clustering is a fundamental task in unsupervised learning that depends heavily on the data representation that is used. Deep generative models have appeared as a promising tool to learn informative low-dimensional data representations. We propose Matching Priors and Conditionals for Clustering (MPCC), a GAN-based model with an encoder to infer latent variables and cluster categories from data, and a flexible decoder to generate samples from a conditional latent space. With MPCC we demonstrate that a deep generative model can be competitive/superior against discriminative methods in clustering tasks surpassing the state of the art over a diverse set of benchmark datasets. Our experiments show that adding a learnable prior and augmenting the number of encoder updates improve the quality of the generated samples, obtaining an inception score of 9.49 $\pm$ 0.15 and improving the Fr\'echet inception distance over the state of the art by a 46.9% in CIFAR10.
Amortized learning of neural causal representations

Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution. These models are often represented as Bayesian networks and learning them scales poorly with the number of variables. Moreover, these approaches cannot leverage previously learned knowledge to help with learning new causal models. In order to tackle these challenges, we represent a novel algorithm called \textit{causal relational networks} (CRN) for learning causal models using neural networks. The CRN represent causal models using continuous representations and hence could scale much better with the number of variables. These models also take in previously learned information to facilitate learning of new causal models. Finally, we propose a decoding-based metric to evaluate causal models with continuous representations. We test our method on synthetic data achieving high accuracy and quick adaptation to previously unseen causal models.
Compact Belief State Representation for Task Planning

Task planning in a probabilistic belief state domains allows generating complex and robust execution policies in those domains affected by state uncertainty. The performance of a task planner relies on the belief state representation. However, current belief state representation becomes easily intractable as the number of variables and execution time grows. To address this problem, we developed a novel belief state representation based on cartesian product and union operations over belief substates. These two operations and single variable assignment nodes form And-Or directed acyclic graph of Belief State (AOBS). We show how to apply actions with probabilistic outcomes and measure the probability of conditions holding over belief state. We evaluated AOBS performance in simulated forward state space exploration. We compared the size of AOBS with the size of Binary Decision Diagrams (BDD) that were previously used to represent belief state. We show that AOBS representation is not only much more compact than a full belief state but it also scales better than BDD for most of the cases.
Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following

Deep learning has recently started being applied to visual tracking of generic objects in video streams. For the purposes of robotics applications, it is very important for a target tracker to recover its track if it is lost due to heavy or prolonged occlusions or motion blur of the target. We present a real-time approach which fuses a generic target tracker and object detection module with a target re-identification module. Our work focuses on improving the performance of Convolutional Recurrent Neural Network-based object trackers in cases where the object of interest belongs to the category of \emph{familiar} objects. Our proposed approach is sufficiently lightweight to track objects at 85-90 FPS while attaining competitive results on challenging benchmarks.
An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration

How do analysts think about grouping and spatial operations? This overarching question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship between grouping and spatial structures, the decisions analysts make when exploring individual observations, and the role of external information. This work contributes the design and results of such a study, in which a group of participants are asked to organize the data contained within an unfamiliar quantitative dataset. We identify several overarching approaches taken by participants to design their organizational space, discuss the interactions performed by the participants, and propose design recommendations to improve the usability of future high-dimensional data exploration tools that make use of grouping (clustering) and spatial (dimension reduction) operations.