Datasets:

NagaYu
/

dendro-lowbackground

doc_id string	arxiv_id string	title string	text string	url string	doi string	category string	published string	era string	dendro_not_after string	dendro_not_after_year int64	dendro_human_origin_p float64	dendro_ci_low float64	dendro_ci_high float64	dendro_abstained bool	dendro_operators int64	dendro_evidence_logodds float64	dendro_flags list	dendro_ancestor null	dendro_explanation string	low_background bool
arxiv:1706.01945	1706.01945	Practical Integer-to-Binary Mapping for Quantum Annealers	Practical Integer-to-Binary Mapping for Quantum Annealers Recent advancements in quantum annealing hardware and numerous studies in this area suggests that quantum annealers have the potential to be effective in solving unconstrained binary quadratic programming problems. Naturally, one may desire to expand the applic...	https://arxiv.org/abs/1706.01945	10.1007/s11128-019-2213-x	quant-ph	2017-06-06T00:00:00+00:00	pre2021	2017-06-06	2,017	0.995191	0.851517	0.99652	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2017-06-06, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.4% of ...	true
arxiv:1706.08447	1706.08447	On the selection of polynomials for the DLP quasi-polynomial time algorithm in small characteristic	On the selection of polynomials for the DLP quasi-polynomial time algorithm in small characteristic In this paper we characterize the set of polynomials $f\in\mathbb F_q[X]$ satisfying the following property: there exists a positive integer $d$ such that for any positive integer $\ell$ less or equal than the degree of...	https://arxiv.org/abs/1706.08447	null	math.NT	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1707.04610	1707.04610	Cloud-based or On-device: An Empirical Study of Mobile Deep Inference	Cloud-based or On-device: An Empirical Study of Mobile Deep Inference Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g., implementing real-time image recognition and conversational system. Given a trained deep learning model, applications usually need to perform a seri...	https://arxiv.org/abs/1707.04610	null	cs.PF	2018-04-15T00:00:00+00:00	pre2021	2018-04-15	2,018	0.994885	0.851029	0.996459	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2018-04-15, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.4% of ...	true
arxiv:1707.08214	1707.08214	Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks	Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks In this paper, we introduce a novel type of Rectified Linear Unit (ReLU), called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an unbounded positive and negative image, can be used as ...	https://arxiv.org/abs/1707.08214	10.1016/j.patrec.2018.09.006	cs.CL	2017-10-31T00:00:00+00:00	pre2021	2017-10-31	2,017	0.99509	0.851355	0.9965	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2017-10-31, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.4% of ...	true
arxiv:1708.07653	1708.07653	Gap-planar Graphs	Gap-planar Graphs We introduce the family of $k$-gap-planar graphs for $k \geq 0$, i.e., graphs that have a drawing in which each crossing is assigned to one of the two involved edges and each edge is assigned at most $k$ of its crossings. This definition is motivated by applications in edge casing, as a $k$-gap-plana...	https://arxiv.org/abs/1708.07653	10.1016/j.tcs.2018.05.031	cs.CG	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1709.00906	1709.00906	Secrecy Rate Region of SWIPT Wiretap Interference Channels	Secrecy Rate Region of SWIPT Wiretap Interference Channels The secrecy rate region of wiretap interference channels with a multi-antenna passive eavesdropper is studied under receiver energy harvesting constraints. To stay operational in the network, the legitimate receivers demand energy alongside information, which ...	https://arxiv.org/abs/1709.00906	null	cs.IT	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1710.05053	1710.05053	Automated Scalable Bayesian Inference via Hilbert Coresets	Automated Scalable Bayesian Inference via Hilbert Coresets The automation of posterior inference in Bayesian data analysis has enabled experts and nonexperts alike to use more sophisticated models, engage in faster exploratory modeling and analysis, and ensure experimental reproducibility. However, standard automated ...	https://arxiv.org/abs/1710.05053	null	stat.ML	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1711.02030	1711.02030	Towards Optimal Energy Harvesting Receiver Design in MIMO Systems	Towards Optimal Energy Harvesting Receiver Design in MIMO Systems In this paper, we investigate a multiple-input multiple-output (MIMO) system with simultaneous information detection (ID) and energy harvesting (EH) receiver. This point-to-point system operates in the vicinity of active interfering nodes. The receiver ...	https://arxiv.org/abs/1711.02030	null	cs.IT	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1711.05914	1711.05914	How Generative Adversarial Networks and Their Variants Work: An Overview	How Generative Adversarial Networks and Their Variants Work: An Overview Generative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distributi...	https://arxiv.org/abs/1711.05914	10.1145/3301282	cs.LG	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1711.06712	1711.06712	Optimal Combination of Image Denoisers	Optimal Combination of Image Denoisers Given a set of image denoisers, each having a different denoising capability, is there a provably optimal way of combining these denoisers to produce an overall better result? An answer to this question is fundamental to designing an ensemble of weak estimators for complex scenes...	https://arxiv.org/abs/1711.06712	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1711.07172	1711.07172	Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives	Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives Fast development of sharing services has become a crucial part of the cyber-enabled world construction process, as sharing services reinvent how people exchange and obtain goods or services. However, privacy leakage or ...	https://arxiv.org/abs/1711.07172	10.1109/ACCESS.2019.2894344	cs.CY	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1712.02949	1712.02949	Journey to the Center of the Point Set	Journey to the Center of the Point Set $\renewcommand{\Re}{\mathbb{R}} \newcommand{\eps}{\varepsilon} \newcommand{\Net}{S} \newcommand{\tldO}{\widetilde{O}} \newcommand{\body}{C} $ We revisit an algorithm of Clarkson etal [CEMST96], that computes (roughly) a $1/(4d^2)$-centerpoint in $\tldO(d^9)$ time, for a point set...	https://arxiv.org/abs/1712.02949	null	cs.CG	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1801.06611	1801.06611	Multiple Description Convolutional Neural Networks for Image Compression	Multiple Description Convolutional Neural Networks for Image Compression Multiple description coding (MDC) is able to stably transmit the signal in the un-reliable and non-prioritized networks, which has been broadly studied for several decades. However, the traditional MDC doesn't well leverage image's context featur...	https://arxiv.org/abs/1801.06611	null	cs.MM	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1802.07292	1802.07292	Bots increase exposure to negative and inflammatory content in online social systems	Bots increase exposure to negative and inflammatory content in online social systems Societies are complex systems which tend to polarize into sub-groups of individuals with dramatically opposite perspectives. This phenomenon is reflected -- and often amplified -- in online social networks where, however, humans are n...	https://arxiv.org/abs/1802.07292	10.1073/pnas.1803470115	physics.soc-ph	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1802.07957	1802.07957	Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps	Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps In this paper, we propose a novel effective non-rigid object tracking framework based on the spatial-temporal consistent saliency detection. In contrast to most existing trackers that utilize a bounding box to specify the trac...	https://arxiv.org/abs/1802.07957	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1803.03562	1803.03562	An Integrated Inverse Space Sparse Representation Framework for Tumor Classification	An Integrated Inverse Space Sparse Representation Framework for Tumor Classification Microarray gene expression data-based tumor classification is an active and challenging issue. In this paper, an integrated tumor classification framework is presented, which aims to exploit information in existing available samples, ...	https://arxiv.org/abs/1803.03562	null	cs.CV	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1803.11347	1803.11347	Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning	Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations ca...	https://arxiv.org/abs/1803.11347	null	cs.LG	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1804.07672	1804.07672	Unsupervised learning of the brain connectivity dynamic using residual D-net	Unsupervised learning of the brain connectivity dynamic using residual D-net In this paper, we propose a novel unsupervised learning method to learn the brain dynamics using a deep learning architecture named residual D-net. As it is often the case in medical research, in contrast to typical deep learning tasks, the s...	https://arxiv.org/abs/1804.07672	null	stat.ML	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1805.04510	1805.04510	Social networks and geography: a review of the literature and its implications	Social networks and geography: a review of the literature and its implications The spatial metaphor of the network along with its accompanying abstractions, such as flow, movement, and connectivity, have been central themes throughout the relational turn in human geography. However, to date networks in geography have ...	https://arxiv.org/abs/1805.04510	null	cs.SI	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1805.06019	1805.06019	RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding	RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding We present a new hierarchical compression scheme for encoding light field images (LFI) that is suitable for interactive rendering. Our method (RLFC) exploits redundancies in the light field images by constructing a tree structure....	https://arxiv.org/abs/1805.06019	10.1145/3306131.3317018	cs.GR	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1805.09460	1805.09460	Cautious Deep Learning	Cautious Deep Learning Most classifiers operate by selecting the maximum of an estimate of the conditional distribution $p(y\|x)$ where $x$ stands for the features of the instance to be classified and $y$ denotes its label. This often results in a {\em hubristic bias}: overconfidence in the assignment of a definite lab...	https://arxiv.org/abs/1805.09460	null	stat.ML	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1806.08915	1806.08915	DALEX: explainers for complex predictive models	DALEX: explainers for complex predictive models Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters - a price that one needs to pay for elasticit...	https://arxiv.org/abs/1806.08915	null	stat.ML	2018-07-05T00:00:00+00:00	pre2021	2018-07-05	2,018	0.99473	0.850781	0.996428	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2018-07-05, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1808.00274	1808.00274	Multimotion Visual Odometry (MVO): Simultaneous Estimation of Camera and Third-Party Motions	Multimotion Visual Odometry (MVO): Simultaneous Estimation of Camera and Third-Party Motions Estimating motion from images is a well-studied problem in computer vision and robotics. Previous work has developed techniques to estimate the motion of a moving camera in a largely static environment (e.g., visual odometry) ...	https://arxiv.org/abs/1808.00274	10.1109/IROS.2018.8594213	cs.RO	2018-08-14T00:00:00+00:00	pre2021	2018-08-14	2,018	0.994634	0.850629	0.996409	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2018-08-14, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1808.02152	1808.02152	Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification	Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification For fine-grained visual classification, objects usually share similar geometric structure but present variant local appearance and different pose. Therefore, localizing and extracting discriminative local features play a crucial role i...	https://arxiv.org/abs/1808.02152	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1808.10632	1808.10632	A novel extension of Generalized Low-Rank Approximation of Matrices based on multiple-pairs of transformations	A novel extension of Generalized Low-Rank Approximation of Matrices based on multiple-pairs of transformations Dimensionality reduction is a main step in the learning process which plays an essential role in many applications. The most popular methods in this field like SVD, PCA, and LDA, only can be applied to data w...	https://arxiv.org/abs/1808.10632	null	cs.LG	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1809.01431	1809.01431	Pre-training on high-resource speech recognition improves low-resource speech-to-text translation	Pre-training on high-resource speech recognition improves low-resource speech-to-text translation We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech recognition (ASR) task, and then fine-tun...	https://arxiv.org/abs/1809.01431	null	cs.CL	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1809.08722	1809.08722	Online Object and Task Learning via Human Robot Interaction	Online Object and Task Learning via Human Robot Interaction This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new tools and motions are taught on the fly. The robotic system developed was one of the five finalists in the KUKA Innovation Award ...	https://arxiv.org/abs/1809.08722	null	cs.RO	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1810.01155	1810.01155	Learning-Based Physical Layer Communications for Multi-agent Collaboration	Learning-Based Physical Layer Communications for Multi-agent Collaboration Consider a collaborative task carried out by two autonomous agents that are able to communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the value of the joint state of bot...	https://arxiv.org/abs/1810.01155	null	cs.IT	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1810.01545	1810.01545	A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation	A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation In the problem of domain adaptation for binary classification, the learner is presented with labeled examples from a source domain, and must correctly classify unlabeled examples from a target domain, which may differ from the source. Previous work o...	https://arxiv.org/abs/1810.01545	null	stat.ML	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1810.04249	1810.04249	Data-dependent compression of random features for large-scale kernel approximation	Data-dependent compression of random features for large-scale kernel approximation Kernel methods offer the flexibility to learn complex relationships in modern, large data sets while enjoying strong theoretical guarantees on quality. Unfortunately, these methods typically require cubic running time in the data set si...	https://arxiv.org/abs/1810.04249	null	stat.ML	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1810.04329	1810.04329	Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach	Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their run...	https://arxiv.org/abs/1810.04329	10.1109/UCC.2018.00018	cs.DC	2018-10-10T00:00:00+00:00	pre2021	2018-10-10	2,018	0.994471	0.850369	0.996376	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2018-10-10, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1810.04632	1810.04632	Non-linear process convolutions for multi-output Gaussian processes	Non-linear process convolutions for multi-output Gaussian processes The paper introduces a non-linear version of the process convolution formalism for building covariance functions for multi-output Gaussian processes. The non-linearity is introduced via Volterra series, one series per each output. We provide closed-fo...	https://arxiv.org/abs/1810.04632	null	stat.ML	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1810.07304	1810.07304	Tracking Users across the Web via TLS Session Resumption	Tracking Users across the Web via TLS Session Resumption User tracking on the Internet can come in various forms, e.g., via cookies or by fingerprinting web browsers. A technique that got less attention so far is user tracking based on TLS and specifically based on the TLS session resumption mechanism. To the best of ...	https://arxiv.org/abs/1810.07304	10.1145/3274694.3274708	cs.CR	2018-10-16T00:00:00+00:00	pre2021	2018-10-16	2,018	0.994451	0.850338	0.996372	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2018-10-16, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1810.12630	1810.12630	Spreading of Memes on Multiplex Networks	Spreading of Memes on Multiplex Networks A model for the spreading of online information or "memes" on multiplex networks is introduced and analyzed using branching-process methods. The model generalizes that of [Gleeson et al., Phys.Rev. X., 2016] in two ways. First, even for a monoplex (single-layer) network, the mo...	https://arxiv.org/abs/1810.12630	10.1088/1367-2630/ab05ef	physics.soc-ph	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1811.00090	1811.00090	SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning	SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierar...	https://arxiv.org/abs/1811.00090	null	cs.AI	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1811.01088	1811.01088	Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks	Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks Pretraining sentence encoders with language modeling and related unsupervised tasks has recently been shown to be very effective for language understanding tasks. By supplementing language model-style pretraining with further traini...	https://arxiv.org/abs/1811.01088	null	cs.CL	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1811.05321	1811.05321	Correction of AI systems by linear discriminants: Probabilistic foundations	Correction of AI systems by linear discriminants: Probabilistic foundations Artificial Intelligence (AI) systems sometimes make errors and will make errors in the future, from time to time. These errors are usually unexpected, and can lead to dramatic consequences. Intensive development of AI and its practical applica...	https://arxiv.org/abs/1811.05321	10.1016/j.ins.2018.07.040	cs.LG	2018-11-11T00:00:00+00:00	pre2021	2018-11-11	2,018	0.994363	0.850197	0.996355	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2018-11-11, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1811.07825	1811.07825	Forman-Ricci Curvature for Hypergraphs	Forman-Ricci Curvature for Hypergraphs In contrast to graph-based models for complex networks, hypergraphs are more general structures going beyond binary relations of graphs. For graphs, statistics gauging different aspects of their structures have been devised and there is undergoing research for devising them for h...	https://arxiv.org/abs/1811.07825	10.13140/RG.2.2.27347.84001	cs.DM	2018-11-19T00:00:00+00:00	pre2021	2018-11-19	2,018	0.994334	0.850151	0.996349	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2018-11-19, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1811.11270	1811.11270	Challenges of measuring the impact of software: an examination of the lme4 R package	Challenges of measuring the impact of software: an examination of the lme4 R package The rise of software as a research object is mirrored in the increasing interests towards quantitative studies of scientific software. However, due to the inconsistent practice of citing software, most of the existing studies analyzin...	https://arxiv.org/abs/1811.11270	10.1016/j.joi.2019.02.007	cs.DL	2018-11-27T00:00:00+00:00	pre2021	2018-11-27	2,018	0.994304	0.850104	0.996343	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2018-11-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1812.03183	1812.03183	A hybrid machine-learning algorithm for designing quantum experiments	A hybrid machine-learning algorithm for designing quantum experiments We introduce a hybrid machine-learning algorithm for designing quantum optics experiments that produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including SchrÃ¶dinger cat ...	https://arxiv.org/abs/1812.03183	null	quant-ph	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1812.04597	1812.04597	Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport	Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport Classical supervised learning produces unreliable models when training and target distributions differ, with most existing solutions requiring samples from the target domain. We propose a proactive approach which learns a relationship ...	https://arxiv.org/abs/1812.04597	null	stat.ML	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1812.05878	1812.05878	In Praise of Sequence (Co-)Algebra and its implementation in Haskell	In Praise of Sequence (Co-)Algebra and its implementation in Haskell What is Sequence Algebra? This is a question that any teacher or student of mathematics or computer science can engage with. Sequences are in Calculus, Combinatorics, Statistics and Computation. They are foundational, a step up from number arithmetic...	https://arxiv.org/abs/1812.05878	null	math.CO	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1812.09660	1812.09660	Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks	Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance...	https://arxiv.org/abs/1812.09660	null	cs.AI	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1901.05350	1901.05350	TensorFlow.js: Machine Learning for the Web and Beyond	TensorFlow.js: Machine Learning for the Web and Beyond TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible ...	https://arxiv.org/abs/1901.05350	null	cs.LG	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1901.07634	1901.07634	DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD	DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD This paper has some inconsistent results, i.e., we made some failed claims because we did some mistakes for using the test criterion for a series. Precisely, our claims on the convergence rate of $\mathcal{O}(1/t)$ of SGD presented in Theo...	https://arxiv.org/abs/1901.07634	null	cs.LG	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.00062	1902.00062	Contactless Cardiac Arrest Detection Using Smart Devices	Contactless Cardiac Arrest Detection Using Smart Devices Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide. Rapid diagnosis and initiation of cardiopulmonary resuscitation (CPR) is the cornerstone of therapy for victims of cardiac arrest. Yet a significant fraction of cardiac arrest victims h...	https://arxiv.org/abs/1902.00062	null	cs.CY	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.02470	1902.02470	PAI Data, Summary of the Project PAI Data Protocol	PAI Data, Summary of the Project PAI Data Protocol The Project PAI Data Protocol ("PAI Data") is a specification that extends the Project PAI Blockchain Protocol to include a method of securing and provisioning access to arbitrary data. In the context of PAI Coin Development Proposal (PDP) 2, this paper defines two im...	https://arxiv.org/abs/1902.02470	null	cs.DC	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.02605	1902.02605	EMaaS: Energy Measurements as a Service for Mobile Applications	EMaaS: Energy Measurements as a Service for Mobile Applications Measuring energy consumption is a challenging task faced by developers when building mobile apps. This paper presents EMaaS: a system that provides reliable energy measurements for mobile applications, without requiring a complex setup. It combines estima...	https://arxiv.org/abs/1902.02605	null	cs.SE	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.04491	1902.04491	SDN Controllers: Benchmarking & Performance Evaluation	SDN Controllers: Benchmarking & Performance Evaluation Software Defined Networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing flow paths to switches and opt...	https://arxiv.org/abs/1902.04491	null	cs.NI	2019-02-12T00:00:00+00:00	pre2021	2019-02-12	2,019	0.993966	0.849568	0.996274	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-12, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.05146	1902.05146	On the CVP for the root lattices via folding with deep ReLU neural networks	On the CVP for the root lattices via folding with deep ReLU neural networks Point lattices and their decoding via neural networks are considered in this paper. Lattice decoding in Rn, known as the closest vector problem (CVP), becomes a classification problem in the fundamental parallelotope with a piecewise linear fu...	https://arxiv.org/abs/1902.05146	null	cs.LG	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.05396	1902.05396	Semi-Supervised and Task-Driven Data Augmentation	Semi-Supervised and Task-Driven Data Augmentation Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overfitting, not generalizing well to unseen images. In practice, obtaining a large number of annotations from clinical experts is expensi...	https://arxiv.org/abs/1902.05396	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.06865	1902.06865	Hyperbolic Discounting and Learning over Multiple Horizons	Hyperbolic Discounting and Learning over Multiple Horizons Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation. However, eviden...	https://arxiv.org/abs/1902.06865	null	stat.ML	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.06942	1902.06942	Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning	Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning Environmental air quality affects people's life, obtaining real-time and accurate environmental air quality has a profound guiding significance for the development of social activities. At present, environmental air quality ...	https://arxiv.org/abs/1902.06942	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.07249	1902.07249	Discovery of Natural Language Concepts in Individual Units of CNNs	Discovery of Natural Language Concepts in Individual Units of CNNs Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in thei...	https://arxiv.org/abs/1902.07249	null	cs.CL	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.09852	1902.09852	Associatively Segmenting Instances and Semantics in Point Clouds	Associatively Segmenting Instances and Semantics in Point Clouds A 3D point cloud describes the real scene precisely and intuitively.To date how to segment diversified elements in such an informative 3D scene is rarely discussed. In this paper, we first introduce a simple and flexible framework to segment instances an...	https://arxiv.org/abs/1902.09852	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10699	1902.10699	Validation of smartphone based pavement roughness measures	Validation of smartphone based pavement roughness measures Smartphones are equipped with sensors such as accelerometers, gyroscope, and GPS in one cost-effective device with an acceptable level of accuracy. There have been some research studies carried out in terms of using smartphones to measure the pavement roughnes...	https://arxiv.org/abs/1902.10699	null	cs.HC	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10700	1902.10700	A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers	A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers We propose a hybrid sequential deep learning model to predict the risk of AMD progression in non-exudative AMD eyes at multiple timepoints, starting from short-term progression (3-months) up to long-term ...	https://arxiv.org/abs/1902.10700	null	q-bio.QM	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10703	1902.10703	Decentralized Evolution and Consolidation of RDF Graphs	Decentralized Evolution and Consolidation of RDF Graphs The World Wide Web and the Semantic Web are designed as a network of distributed services and datasets. In this network and its genesis, collaboration played and still plays a crucial role. But currently we only have central collaboration solutions for RDF data, ...	https://arxiv.org/abs/1902.10703	10.1007/978-3-319-60131-1_2	cs.DB	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10704	1902.10704	Adaptive Gaussian Copula ABC	Adaptive Gaussian Copula ABC Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents a simple yet effective ABC algorithm based on the combination of two classical ABC approaches --- regression A...	https://arxiv.org/abs/1902.10704	null	stat.CO	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10707	1902.10707	Neural Imaging Pipelines - the Scourge or Hope of Forensics?	Neural Imaging Pipelines - the Scourge or Hope of Forensics? Forensic analysis of digital photographs relies on intrinsic statistical traces introduced at the time of their acquisition or subsequent editing. Such traces are often removed by post-processing (e.g., down-sampling and re-compression applied upon distribut...	https://arxiv.org/abs/1902.10707	null	cs.CV	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10731	1902.10731	Private Center Points and Learning of Halfspaces	Private Center Points and Learning of Halfspaces We present a private learner for halfspaces over an arbitrary finite domain $X\subset \mathbb{R}^d$ with sample complexity $mathrm{poly}(d,2^{\log^*\|X\|})$. The building block for this learner is a differentially private algorithm for locating an approximate center point...	https://arxiv.org/abs/1902.10731	null	cs.LG	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10739	1902.10739	A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images	A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images Recent research used machine learning methods to predict a person's sexual orientation from their photograph (Wang and Kosinski, 2017). To verify this result, two of these models are replicated, one based on a ...	https://arxiv.org/abs/1902.10739	null	cs.CV	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10740	1902.10740	Object-driven Text-to-Image Synthesis via Adversarial Training	Object-driven Text-to-Image Synthesis via Adversarial Training In this paper, we propose Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for complex scenes. Following the two-step (layout-image) generation process, a novel object-driven attentive im...	https://arxiv.org/abs/1902.10740	null	cs.CV	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10741	1902.10741	Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping	Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDA...	https://arxiv.org/abs/1902.10741	null	cs.RO	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10744	1902.10744	Joint Face Detection and Facial Motion Retargeting for Multiple Faces	Joint Face Detection and Facial Motion Retargeting for Multiple Faces Facial motion retargeting is an important problem in both computer graphics and vision, which involves capturing the performance of a human face and transferring it to another 3D character. Learning 3D morphable model (3DMM) parameters from 2D face ...	https://arxiv.org/abs/1902.10744	null	cs.CV	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10754	1902.10754	Introspection Learning	Introspection Learning Traditional reinforcement learning agents learn from experience, past or present, gained through interaction with their environment. Our approach synthesizes experience, without requiring an agent to interact with their environment, by asking the policy directly "Are there situations X, Y, and Z...	https://arxiv.org/abs/1902.10754	null	cs.LG	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10759	1902.10759	Phase-field modeling of fracture for quasi-brittle materials	Phase-field modeling of fracture for quasi-brittle materials This paper addresses the modeling of fracture in quasi-brittle materials using a phase-field approach to the description of crack topology. Within the computational mechanics community, several studies have treated the issue of modeling fracture using phase ...	https://arxiv.org/abs/1902.10759	10.1016/j.undsp.2018.08.002	cs.CE	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10773	1902.10773	An exponential lower bound for the degrees of invariants of cubic forms and tensor actions	An exponential lower bound for the degrees of invariants of cubic forms and tensor actions Using the Grosshans Principle, we develop a method for proving lower bounds for the maximal degree of a system of generators of an invariant ring. This method also gives lower bounds for the maximal degree of a set of invariants...	https://arxiv.org/abs/1902.10773	null	math.RT	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10810	1902.10810	Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation	Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation The convergence of HPC and data-intensive methodologies provide a promising approach to major performance improvements. This paper provides a general description of the interaction between traditional HPC and ML approaches and m...	https://arxiv.org/abs/1902.10810	null	cs.DC	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10812	1902.10812	Padovan heaps	Padovan heaps We analyze priority queues of Fibonacci family. The paper is inspired by Violation heap [1], where A. Elmasry saves one pointer in representation of Fibonacci heap nodes while achieving the same amortized bounds as Fibonacci heaps [2] of M. L. Fredman and R. E. Tarjan. Unfortunately author forces the hea...	https://arxiv.org/abs/1902.10812	null	cs.DS	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10814	1902.10814	Graph-RISE: Graph-Regularized Image Semantic Embedding	Graph-RISE: Graph-Regularized Image Semantic Embedding Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this paper, we present Graph-Regularized Image Semantic Embedding (Graph-RISE), a large-sc...	https://arxiv.org/abs/1902.10814	null	cs.CV	2019-02-14T00:00:00+00:00	pre2021	2019-02-14	2,019	0.993956	0.849552	0.996272	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-14, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10816	1902.10816	Automated Detection of Pre-Disaster Building Images from Google Street View	Automated Detection of Pre-Disaster Building Images from Google Street View After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain lessons and gain knowledge from the event. Images of damaged buildings and components provide valuable evidence to understand the c...	https://arxiv.org/abs/1902.10816	null	cs.CV	2019-02-13T00:00:00+00:00	pre2021	2019-02-13	2,019	0.993961	0.84956	0.996273	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-13, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10828	1902.10828	The VOiCES from a Distance Challenge 2019 Evaluation Plan	The VOiCES from a Distance Challenge 2019 Evaluation Plan The "VOiCES from a Distance Challenge 2019" is designed to foster research in the area of speaker recognition and automatic speech recognition (ASR) with the special focus on single channel distant/far-field audio, under noisy conditions. The main objectives of...	https://arxiv.org/abs/1902.10828	null	eess.AS	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10832	1902.10832	Capacity Results for the Noisy Shuffling Channel	Capacity Results for the Noisy Shuffling Channel Motivated by DNA-based storage, we study the noisy shuffling channel, which can be seen as the concatenation of a standard noisy channel (such as the BSC) and a shuffling channel, which breaks the data block into small pieces and shuffles them. This channel models a DNA...	https://arxiv.org/abs/1902.10832	null	cs.IT	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10838	1902.10838	Data-centric online ecosystem for digital materials science	Data-centric online ecosystem for digital materials science Materials science is becoming increasingly more reliant on digital data to facilitate progress in the field. Due to a large diversity in its scope, breadth, and depth, organizing the data in a standard way to optimize the speed and creative breadth of the res...	https://arxiv.org/abs/1902.10838	null	cond-mat.mtrl-sci	2019-02-27T00:00:00+00:00	pre2021	2019-02-27	2,019	0.993889	0.849445	0.996258	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-27, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10840	1902.10840	Deep Interpretable Non-Rigid Structure from Motion	Deep Interpretable Non-Rigid Structure from Motion All current non-rigid structure from motion (NRSfM) algorithms are limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle. This has hampered the practical utility of NRSfM for many applications within vision. In this ...	https://arxiv.org/abs/1902.10840	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10847	1902.10847	Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings	Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings Visual identification of individual animals that bear unique natural body markings is an important task in wildlife conservation. The photo databases of animal markings grow larger and each new observation has to be matc...	https://arxiv.org/abs/1902.10847	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10848	1902.10848	Machine-assisted annotation of forensic imagery	Machine-assisted annotation of forensic imagery Image collections, if critical aspects of image content are exposed, can spur research and practical applications in many domains. Supervised machine learning may be the only feasible way to annotate very large collections, but leading approaches rely on large samples of...	https://arxiv.org/abs/1902.10848	null	cs.CV	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10849	1902.10849	A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing	A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors. While they p...	https://arxiv.org/abs/1902.10849	null	cs.CE	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true
arxiv:1902.10851	1902.10851	Analysis of Quantum Multi-Prover Zero-Knowledge Systems: Elimination of the Honest Condition and Computational Zero-Knowledge Systems for QMIP*	Analysis of Quantum Multi-Prover Zero-Knowledge Systems: Elimination of the Honest Condition and Computational Zero-Knowledge Systems for QMIP* Zero-knowledge and multi-prover systems are both central notions in classical and quantum complexity theory. There is, however, little research in quantum multi-prover zero-kn...	https://arxiv.org/abs/1902.10851	null	quant-ph	2019-02-28T00:00:00+00:00	pre2021	2019-02-28	2,019	0.993883	0.849436	0.996257	false	1	6.5026	[ "single_operator" ]	null	Existence bound: content existed on or before 2019-02-28, witnessed by 1 independent operator(s) (arxiv-cornell). Flag [low] single_operator: bound rests on a single operator (arxiv-cornell); compromising it would be sufficient to move the date Under the configured prevalence curve, machine-generated text was ~0.5% of ...	true

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Dendro low-background corpus

5034 arXiv records annotated with archival evidence of when they existed, produced by Dendro v0.1.0.

2500 of them (49.7%) are low-background: an independent registration record places them before 2021-01-01, i.e. before large-scale text generation. The name is from metallurgy — low-background steel is steel smelted before the 1945 atmospheric tests: not special steel, just ordinary steel that happens to predate the contamination, and valuable because no amount of later care can reproduce it.

Read this before you use it

This is not an AI-writing detector, and these columns must not be used as one. They report evidence that content existed before a date. Nothing here supports a conclusion that a particular person did or did not write something.
Absence of evidence is not evidence. Most text that has ever existed was never archived. dendro_abstained marks rows where the evidence is too thin to act on — those are unknown, not generated.
Every row is witnessed by arXiv and arXiv alone. dendro_operators is 1 throughout, and dendro_flags contains single_operator. Compromising one organisation would be sufficient to move every date in this dataset. Dendro's whole argument is that confidence should come from independent operators; this corpus has one, and the column says so rather than hiding it.
The recent split is "human-attributed", not "verified human". By 2025 an unknown share of abstracts have been through a model. There is no clean recent human corpus to be had — which is the argument for dating by archive rather than by inspection.
Selection is not neutral. Archived text over-represents what crawlers reached: English, institutional, indexed, long-lived. A low-background subset concentrates every one of those biases.

Columns

column	meaning
`text`	title + abstract, as fingerprinted
`title`, `doi`	the record's own metadata
`url`, `arxiv_id`, `category`, `era`	identifiers and provenance of the record
`published`	arXiv v1 submission date — the witness
`dendro_not_after`	date the content is proven to have existed by
`dendro_not_after_year`	integer year, convenient for filtering
`dendro_human_origin_p`	calibrated probability — not a label
`dendro_ci_low` / `dendro_ci_high`	90% credible interval, the honest width of the claim
`dendro_abstained`	true when the evidence is too thin to act on
`dendro_operators`	count of independent operators behind the bound
`dendro_evidence_logodds`	how hard the bound would be to forge
`dendro_flags`	detected inconsistencies
`dendro_explanation`	human-readable receipt naming the witness sources
`low_background`	proven pre-2021 existence and `dendro_ci_low` ≥ 0.8

Why the threshold is 0.8 and not 0.95

Because that is what the evidence in this corpus can actually support, and moving the threshold to flatter the number would be the whole problem with provenance tooling.

Every row here rests on one operator. Under Dendro's model a single arXiv registration gives an evidence log-odds of ~6.5, which puts the lower end of the 90% credible interval at about 0.85 no matter how trustworthy arXiv is — the interval is wide because corroboration is absent, not because the record is doubtful. At ci_low ≥ 0.90 this dataset would contain zero low-background rows, which is a true statement about single-operator evidence and a useless artefact.

So the flag is set where single-operator registration evidence genuinely reaches, and the number that would tighten it is dendro_operators — not the threshold. A document witnessed by arXiv and the Internet Archive and Common Crawl clears 0.95 comfortably; filter on dendro_ci_low yourself if you want a different line.

There is deliberately no is_synthetic column and no boolean verdict. Code that wants a filter must pick a threshold explicitly, and should prefer dendro_ci_low so the choice is conservative.

Three lines to use it

from datasets import load_dataset
ds = load_dataset("NagaYu/dendro-lowbackground", split="train")
clean = ds.filter(lambda r: r["low_background"])          # 2500 rows

Purity / retention trade-off

Selecting to bound the expected synthetic fraction (provably maximum-cardinality — see dendro.corpus_report.build_low_background_subset):

max expected synthetic	rows kept	retention
≤ 1%	0	0.0%
≤ 2%	0	0.0%
≤ 5%	0	0.0%
≤ 10%	0	0.0%
≤ 20%	2790	55.4%

Method, in one paragraph

Dendro asks independent archives — the Internet Archive, Common Crawl, arXiv, Crossref, public posting archives — what they observed and when, groups witnesses by operator rather than by count (twenty captures from one archive are one archive), and reports the earliest time whose surviving evidence clears a failure-probability budget. It never reads the prose, which is why its accuracy does not move when a new generator ships. Probabilities are produced by a mixture that collapses to the base rate when evidence is absent, so "no evidence" cannot be reported as "synthetic".

The calibrator is left unfitted here on purpose: a fitted map encodes the base rate of the corpus it was fitted on, and baking that into a published artefact would make every row depend on a split nobody downstream can inspect. These are the raw numbers.

Provenance of the data itself

arXiv metadata, including abstracts, is offered under CC0 1.0 through the public API and OAI-PMH, which is how it was harvested. Thank you to arXiv for use of its open access interoperability.

Generated 2026-08-10 by scripts/publish_dataset.py · Apache-2.0 code, CC0 data.

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