Query: Design of a UHF RFID metal tag for long reading range using a cavity structure

Relevant passages:
Design criteria for the RF section of UHF and microwave passive RFID transponders <=SEP=> A set of design criteria for the radio-frequency (RF) section of long-range passive RF identification (RFID) transponders operating in the 2.45-GHz or 868-MHz industrial, scientific, and medical (ISM) frequency ranges is derived in this paper, focusing in particular on the voltage multiplier, the power-matching network, and the backscatter modulation. The paper discusses the design tradeoffs between the error probability at the reader receiver and the converted RF-dc power at the transponder, determining the regions of the design space that allow optimization of the operating range and the data rate of the RFID system.

Low-Profile RFID Tag Antenna Using Compact AMC Substrate for Metallic Objects <=SEP=> A low-profile (aplambda/100) passive radio frequency identification (RFID) tag antenna incorporated with a compact artificial magnetic conductor (AMC) substrate is proposed. A novel modified rectangular patch type AMC substrate with offset vias is employed to significantly reduce the planar dimensions of a tag antenna, which is about 0.1lambda times 0.2lambda. The effect of a ground plane size and the number of AMC unit cells on the antenna performance such as return loss and gain is investigated with a view of tag installation. It is demonstrated through experiments that the proposed tag antenna shows relatively tolerant reading distances from 2.4 to 4.8 m under various platform environments such as a conductive plate or a plastic water container.

Power reflection coefficient analysis for complex impedances in RFID tag design <=SEP=> Kurokawa's method of calculating the power reflection coefficient from the Smith chart in the situation when one complex impedance is directly connected to another is applied to passive RFID tag design, where power reflection is important, as it determines the tag characteristics. The performance analysis of a specific RFID tag is presented together with experimental data, which is in close agreement with the theory.

High-Impedance Electromagnetic Surfaces with a Forbidden Frequency Band <=SEP=> A new type of metallic electromagnetic structure has been developed that is characterized by having high surface impedance. Although it is made of continuous metal, and conducts dc currents, it does not conduct ac currents within a forbidden frequency band. Unlike normal conductors, this new surface does not support propagating surface waves, and its image currents are not phase reversed. The geometry is analogous to a corrugated metal surface in which the corrugations have been folded up into lumped-circuit elements, and distributed in a two-dimensional lattice. The surface can be described using solid-state band theory concepts, even though the periodicity is much less than the free-space wavelength. This unique material is applicable to a variety of electromagnetic problems, including new kinds of low-profile antennas.

Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. <=SEP=> Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create "cultural bridges," or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research.

On the momentum term in gradient descent learning algorithms <=SEP=> A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.

A sparse-response deep belief network based on rate distortion theory <=SEP=> Deep belief networks (DBNs) are currently the dominant technique for modeling the architectural depth of brain, and can be trained efficiently in a greedy layer-wise unsupervised learning manner. However, DBNs without a narrow hidden bottleneck typically produce redundant, continuous-valued codes and unstructured weight patterns. Taking inspiration from rate distortion (RD) theory, which encodes original data using as few bits as possible, we introduce in this paper a variant of DBN, referred to as sparse-response DBN (SR-DBN). In this approach, Kullback–Leibler divergence between the distribution of data and the equilibrium distribution defined by the building block of DBN is considered as a distortion function, and the sparse response regularization induced by L1-norm of codes is used to achieve a small code rate. Several experiments by extracting features from different scale image datasets show that our approach SR-DBN learns codes with small rate, extracts features at multiple levels of abstraction mimicking computations in the cortical hierarchy, and obtains more discriminative representation than PCA and several basic algorithms of DBNs. & 2014 Elsevier Ltd. All rights reserved.

Pl@ntNet mobile app <=SEP=> Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.

Resilient Cell-Based Architecture for Time-to-Digital Converter <=SEP=> This paper proposes a resilient Time-to-Digital Converter (TDC) that lends itself to cell-based design automation. We adopt a shrinking-based architecture with a number of distinctive techniques. First of all, a specialized on-chip re-calibration scheme is developed so that the real-time transfer function of the TDC in silicon (which maps an input pulse-width to its corresponding output code) can be derived on the chip and thereby the absolute value (instead of just a relative code) of an input pulse-width under measurement can be reported. Secondly, the sampling errors stemming from the jitters of training clocks used in the calibration scheme are mitigated by the principle of multi sampling. Thirdly, a flexible coarse-shrinking block is adopted and an automatic adjustment scheme is employed so that the coarse-shrinking block can adjust itself when operated under different input pulse-width ranges.

Measuring User Productivity in Machine Translation Enhanced Computer Assisted Translation <=SEP=> This paper addresses the problem of reliably measuring productivity gains by professional translators working with a machine translation enhanced computer assisted translation tool. In particular, we report on a field test we carried out with a commercial CAT tool in which translation memory matches were supplemented with suggestions from a commercial machine translation engine. The field test was conducted with 12 professional translators working on real translation projects. Productivity of translators were measured with two indicators, post-editing speed and post-editing effort, on two translation directions, English–Italian and English–German, and two linguistic domains, legal and information technology. Besides a detailed statistical analysis of the experimental results, we also discuss issues encountered in running the test.

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection <=SEP=> This paper presents and evaluates a deep learning architecture for automated basal cell carcinoma cancer detection that integrates (1) image representation learning, (2) image classification and (3) result interpretability. A novel characteristic of this approach is that it extends the deep learning architecture to also include an interpretable layer that highlights the visual patterns that contribute to discriminate between cancerous and normal tissues patterns, working akin to a digital staining which spotlights image regions important for diagnostic decisions. Experimental evaluation was performed on set of 1,417 images from 308 regions of interest of skin histopathology slides, where the presence of absence of basal cell carcinoma needs to be determined. Different image representation strategies, including bag of features (BOF), canonical (discrete cosine transform (DCT) and Haar-based wavelet transform (Haar)) and proposed learned-from-data representations, were evaluated for comparison. Experimental results show that the representation learned from a large histology image data set has the best overall performance (89.4% in F-measure and 91.4% in balanced accuracy), which represents an improvement of around 7% over canonical representations and 3% over the best equivalent BOF representation.

Defining Media Enjoyment as the Satisfaction of Intrinsic Needs <=SEP=> This article presents a model of enjoyment rooted in self-determination theory (Deci & Ryan, 1985) that includes the satisfaction of three needs related to psychological wellbeing: autonomy, competence, and relatedness. In an experiment designed to validate this conceptualization of enjoyment, we manipulate video game characteristics related to the satisfaction of these needs and examine their relative effects on enjoyment. The validated model explains 51% of the variance in enjoyment, even without including needs usually studied in relation to enjoyment such as pleasure seeking. Results indicate the utility of defining enjoyment as need satisfaction. These results are discussed in terms of a broader conceptualization of enjoyment represented as the satisfaction of a comprehensive set of functional needs.

A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR <=SEP=> Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. To get a higher precision with less learning time, this paper presents a Hybrid PSO-SVR forecasting method, which uses particle swarm optimization (PSO) to search optimal SVR parameters. In order to find a PSO that is more proper for SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flying out of the searching space Through the comparison of three strategies, we find one of the strategies can make PSO get the optimal parameters more quickly. The PSO using this strategy is called fast PSO. Furthermore, aiming at the problem about the decrease of prediction accuracy caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The results of extensive comparison experiments indicate that the proposed model can get more accurate forecasting results than other state-of-the-art algorithms, and when the data contain noises, the method with historical momentum still gets accurate forecasting results.

Fully Parallel Stochastic LDPC Decoders <=SEP=> Stochastic decoding is a new approach to iterative decoding on graphs. This paper presents a hardware architecture for fully parallel stochastic low-density parity-check (LDPC) decoders. To obtain the characteristics of the proposed architecture, we apply this architecture to decode an irregular state-of-the-art (1056,528) LDPC code on a Xilinx Virtex-4 LX200 field-programmable gate-array (FPGA) device. The implemented decoder achieves a clock frequency of 222 MHz and a throughput of about 1.66 Gb/s at Eb/N0=4.25 dB (a bit error rate of 10-8). It provides decoding performance within 0.5 and 0.25 dB of the floating-point sum-product algorithm with 32 and 16 iterations, respectively, and similar error-floor behavior. The decoder uses less than 40% of the lookup tables, flip-flops, and IO ports available on the FPGA device. The results provided in this paper validate the potential of stochastic LDPC decoding as a practical and competitive fully parallel decoding approach.

Epigenetics and the embodiment of race: developmental origins of US racial disparities in cardiovascular health. <=SEP=> The relative contribution of genetic and environmental influences to the US black-white disparity in cardiovascular disease (CVD) is hotly debated within the public health, anthropology, and medical communities. In this article, we review evidence for developmental and epigenetic pathways linking early life environments with CVD, and critically evaluate their possible role in the origins of these racial health disparities. African Americans not only suffer from a disproportionate burden of CVD relative to whites, but also have higher rates of the perinatal health disparities now known to be the antecedents of these conditions. There is extensive evidence for a social origin to prematurity and low birth weight in African Americans, reflecting pathways such as the effects of discrimination on maternal stress physiology. In light of the inverse relationship between birth weight and adult CVD, there is now a strong rationale to consider developmental and epigenetic mechanisms as links between early life environmental factors like maternal stress during pregnancy and adult race-based health disparities in diseases like hypertension, diabetes, stroke, and coronary heart disease. The model outlined here builds upon social constructivist perspectives to highlight an important set of mechanisms by which social influences can become embodied, having durable and even transgenerational influences on the most pressing US health disparities. We conclude that environmentally responsive phenotypic plasticity, in combination with the better-studied acute and chronic effects of social-environmental exposures, provides a more parsimonious explanation than genetics for the persistence of CVD disparities between members of socially imposed racial categories.

Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease <=SEP=> Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD.

Multidisciplinary Instruction with the Natural Language Toolkit <=SEP=> The Natural Language Toolkit ( NLTK ) is widely used for teaching natural language processing to students majoring in linguistics or computer science. This paper describes the design ofNLTK , and reports on how it has been used effectively in classes that involve different mixes of linguistics and computer science students. We focus on three key issues: getting started with a course, delivering interactive demonstrations in the classroom, and organizing assignments and projects. In each case, we report on practical experience and make recommendations on how to useNLTK to maximum effect.

A 2.4 GHz Interferer-Resilient Wake-Up Receiver Using A Dual-IF Multi-Stage N-Path Architecture <=SEP=> A 2.4 GHz interferer-resilient wake-up receiver for ultra-low power wireless sensor nodes uses an uncertain-IF dualconversion topology, combining a distributed multi-stage N-path filtering technique with an unlocked low-Q resonator-referred local oscillator. This structure provides narrow-band selectivity and strong immunity against interferers, while avoiding expensive external resonant components such as BAW resonators or crystals. The 65 nm CMOS receiver prototype provides a sensitivity of -97 dBm and a carrier-to-interferer ratio better than -27 dB at 5 MHz offset, for a data rate of 10 kb/s at a 10-3 bit error rate, while consuming 99 μW from a 0.5 V voltage supply under continuous operation.

Understanding choice overload in recommender systems <=SEP=> Even though people are attracted by large, high quality recommendation sets, psychological research on choice overload shows that choosing an item from recommendation sets containing many attractive items can be a very difficult task. A web-based user experiment using a matrix factorization algorithm applied to the MovieLens dataset was used to investigate the effect of recommendation set size (5 or 20 items) and set quality (low or high) on perceived variety, recommendation set attractiveness, choice difficulty and satisfaction with the chosen item. The results show that larger sets containing only good items do not necessarily result in higher choice satisfaction compared to smaller sets, as the increased recommendation set attractiveness is counteracted by the increased difficulty of choosing from these sets. These findings were supported by behavioral measurements revealing intensified information search and increased acquisition times for these large attractive sets. Important implications of these findings for the design of recommender system user interfaces will be discussed.

Deep Rewiring: Training very sparse deep networks <=SEP=> Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently on sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

Stability Analysis and Stabilization methods of DC Microgrid with Multiple Parallel-Connected DC-DC Converters loaded <=SEP=> -Constant power loads (CPLs) may yield instability due to the well-known negative impedance characteristic. This study analyzes the factors which cause instability of a DC microgrid with multiple DC-DC converters. Two stabilization methods are presented for two operation modes: constant voltage source mode and droop mode. And sufficient conditions for the stability of the DC microgrid are obtained by identifying the eigenvalues of the Jacobian matrix. The key is to transform the eigenvalue problem to a quadratic eigenvalue problem (QEP). When applying the methods in practical engineering, the salient feature is that the stability parameter domains can be estimated by the available constraints, such as the values of capacities, inductances, maximum load power and distances of the cables. Compared with some classical methods, the proposed methods have wider stability region. The simulation results based on MATLAB /simulink platform verify the feasibility of the methods.

Building Enterprise Systems Infrastructure Flexibility as Enabler of Organisational Agility: Empirical Evidence <=SEP=> Enterprise systems (ES) that capture the most advanced developments of information technology are becoming common fixtures in most organisations. However, how ES affect organizational agility (OA) has been less researched and the existing research remains equivocal. From the perspective that ES can positively contribute to OA, this research via theory-based model development and rigorous empirical investigation of the proposed model, has bridged significant research gaps and provided empirical evidence for, and insights into, the effect of ES on OA. The empirical results based on data collected from 179 large organizations in Australia and New Zealand which have implemented and used ES for at least one year show that organisations can achieve agility out of their ES in two ways: by developing ES technical competences to build ES-enabled capabilities that digitise their key sensing and responding processes; and when ES-enabled sensing and responding capabilities are aligned in relatively turbulent environment.

Power grids reliability appraisal with intrusion tolerant capability in SCADA systems <=SEP=> By intruding into the substations and control center of the supervisory control and data acquisition (SCADA) system, trip commands can be sent to intelligent electronic devices (IEDs) which control the system breakers. Reliability of the power system can be impacted through the cyber attacks. In this paper, a modified semi-Markov process (SMP) model is used to describe the procedures of normal and penetration attacks against the intrusion tolerant system. By modeling the transition probabilities between the SMP states and the sojourn time of each SMP state, the mean times to compromise (MTTCs) of the normal and penetration attacks are calculated. With increased probabilities of breaker trips resulted from the cyber attacks, the loss of load probabilities (LOLP) are evaluated based on IEEE reliability test system 79 (RTS79). When the level of attack increases or the level of defense in the system decreases, the simulation results demonstrate that the power system becomes less reliable.

FiDO: A Community-based Web Browsing Agent and CDN for Challenged Network Environments <=SEP=> Homes located on tribal lands, particularly in rural areas of the United States, continue to lack access to broadband Internet and cellular connectivity [19]. Inspired by previous observations of community content similarity in tribal networks, we propose FiDO, a community-based Web browsing and content delivery system that takes advantage of user mobility, opportunistic connectivity, and collaborative filtering to provide relevant Web content to members of disconnected households via opportunistic contact with cellular base stations during a daily commute. We evaluate FiDO using trace-driven simulations with network usage data collected from a tribal-operated ISP that serves the Coeur d’Alene Indian Reservation in Western Idaho. By collecting data about household Web preferences and applying a collaborative filtering technique based on the Web usage patterns of the surrounding reservation community, we are able to opportunistically browse the Web on behalf of members of disconnected households, providing an average of 69.4 Web pages (all content from a specific URL, e.g., “http://gis.cdatribe-nsn.gov/LandBuyBack/”) crawled from 73% of their top 10 most visited Web domains (e.g., “cdatribe-nsn.gov” or “cnn.com/”) per day. Moreover, this content is able to be fetched and pushed to users even when the opportunistic data rate is limited to an average of only 0.99 Mbps (σ = 0.24 Mbps) and the daily opportunistic connection time is an average of 45.9 minutes (σ = 2.3 minutes). Additionally, we demonstrate a hybrid “search and browse” approach that allocates a percentage of opportunistic resources to the download of user-specified content. By dedicating only 10% of opportunistic windows of connectivity to the download of social media content, 51% of households were able to receive all of their daily expected social media content in addition to an average of 55.3 Web pages browsed on their behalf from an average of 4 different Web domains. Critically, we demonstrate the feasibility of a collaborative and community-based Web browsing model that extends access to Web content across the last mile(s) using existing infrastructure and rural patterns of mobility.

Actions and Attributes from Wholes and Parts <=SEP=> We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired by recent advances in computer vision. Our part detectors are a deep version of poselets and capture parts of the human body under a distinct set of poses. For the tasks of action and attribute classification, we train holistic convolutional neural networks and show that adding parts leads to top-performing results for both tasks. We observe that for deeper networks parts are less significant. In addition, we demonstrate the effectiveness of our approach when we replace an oracle person detector, as is the default in the current evaluation protocol for both tasks, with a state-of-the-art person detection system.

基於對照表以及語言模型之簡繁字體轉換 (Chinese Characters Conversion System based on Lookup Table and Language Model) [In Chinese] <=SEP=> The character sets used in China and Taiwan are both Chinese, but they are divided into simplified and traditional Chinese characters. There are large amount of information exchange between China and Taiwan through books and Internet. To provide readers a convenient reading environment, the character conversion between simplified and traditional Chinese is necessary. The conversion between simplified and traditional Chinese characters has two problems: one-to-many ambiguity and term usage problems. Since there are many traditional Chinese characters that have only one corresponding simplified character, when converting simplified Chinese into traditional Chinese, the system will face the one-to-many ambiguity. Also, there are many terms that have different usages between the two Chinese societies. This paper focus on designing an extensible conversion system, that can take the advantage of community knowledge by accumulating lookup tables through Wikipedia to tackle the term usage problem and can integrate language model to disambiguate the one-to-many ambiguity. The system can reduce the cost of proofreading of character conversion for books, e-books, or online publications. The extensible architecture makes it easy to improve the system with new training data.

Evaluating abdominal core muscle fatigue: Assessment of the validity and reliability of the prone bridging test. <=SEP=> The aims of this study were to research the amplitude and median frequency characteristics of selected abdominal, back, and hip muscles of healthy subjects during a prone bridging endurance test, based on surface electromyography (sEMG), (a) to determine if the prone bridging test is a valid field test to measure abdominal muscle fatigue, and (b) to evaluate if the current method of administrating the prone bridging test is reliable. Thirty healthy subjects participated in this experiment. The sEMG activity of seven abdominal, back, and hip muscles was bilaterally measured. Normalized median frequencies were computed from the EMG power spectra. The prone bridging tests were repeated on separate days to evaluate inter and intratester reliability. Significant differences in normalized median frequency slope (NMFslope ) values between several abdominal, back, and hip muscles could be demonstrated. Moderate-to-high correlation coefficients were shown between NMFslope values and endurance time. Multiple backward linear regression revealed that the test endurance time could only be significantly predicted by the NMFslope of the rectus abdominis. Statistical analysis showed excellent reliability (ICC=0.87-0.89). The findings of this study support the validity and reliability of the prone bridging test for evaluating abdominal muscle fatigue.

ProfileDroid: multi-layer profiling of android applications <=SEP=> The Android platform lacks tools for assessing and monitoring apps in a systematic way. This lack of tools is particularly problematic when combined with the open nature of Google Play, the main app distribution channel. As our key contribution, we design and implement ProfileDroid, a comprehensive, multi-layer system for monitoring and profiling apps. Our approach is arguably the first to profile apps at four layers: (a) static, or app specification, (b) user interaction, (c) operating system, and (d) network. We evaluate 27 free and paid Android apps and make several observations: (a) we identify discrepancies between the app specification and app execution, (b) free versions of apps could end up costing more than their paid counterparts, due to an order of magnitude increase in traffic, (c) most network traffic is not encrypted, (d) apps communicate with many more sources than users might expect---as many as 13, and (e) we find that 22 out of 27 apps communicate with Google during execution. ProfileDroid is the first step towards a systematic approach for (a) generating cost-effective but comprehensive app profiles, and (b) identifying inconsistencies and surprising behaviors.

Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay <=SEP=> This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, called human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This is meant to compensate for the difficulties of current exploration strategies, such as ε-greedy, to find successful control policies in games with sparse rewards. Like other deep reinforcement learning architectures, our model uses a convolutional neural network that receives only raw pixel inputs to estimate the state value function. We tested our method on Montezuma’s Revenge and Private Eye, two of the most challenging games from the Atari platform. The results we obtained show a substantial improvement compared to previous learning approaches, as well as over a random player. We also propose a method for training deep reinforcement learning agents using human gameplay experience, which we call human experience replay.