Query: 4D Generic Video Object Proposals

Relevant passages:
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections <=SEP=> Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.

Watch and learn: Semi-supervised learning of object detectors from videos <=SEP=> We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.

Deep Residual Learning for Image Recognition <=SEP=> Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification <=SEP=> In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.

Mask R-CNN <=SEP=> 

Partial Image Encryption based on Block wise Shuffling using Arnold Map <=SEP=> To prevent image from unauthorized access, Encryption techniques of digital images play a very important role . The Fully image encryption techniques is very complex in nature and takes a lot of computational time. But, certain applications do not require total encryption; it requires a part of the multimedia data to be transparent to all users, like Pay-TV or Payable Internet Imaging Albums which involves encrypting the most meaningful parts of an image. In this paper we focus on a partial image encryption technique based on block wise shuffling with the help of Arnold map . Firstly image is divided into blocks then Blocks within the image are permuted by using Arnold map and final permuted block are combined that yields partial encrypted images . This process is repeated for different block size. The PSNR and NPCR obtained by our technique shows that the proposed technique gives better result than the existing techniques.

Rational Protocol Design: Cryptography against Incentive-Driven Adversaries <=SEP=> Existing work on "rational cryptographic protocols" treats each party (or coalition of parties) running the protocol as a selfish agent trying to maximize its utility. In this work we propose a fundamentally different approach that is better suited to modeling a protocol under attack from an external entity. Specifically, we consider a two-party game between an protocol designer and an external attacker. The goal of the attacker is to break security properties such as correctness or privacy, possibly by corrupting protocol participants; the goal of the protocol designer is to prevent the attacker from succeeding. We lay the theoretical groundwork for a study of cryptographic protocol design in this setting by providing a methodology for defining the problem within the traditional simulation paradigm. Our framework provides ways of reasoning about important cryptographic concepts (e.g., adaptive corruptions or attacks on communication resources) not handled by previous game-theoretic treatments of cryptography. We also prove composition theorems that-for the first time-provide a sound way to design rational protocols assuming "ideal communication resources" (such as broadcast or authenticated channels) and then instantiate these resources using standard cryptographic tools. Finally, we investigate the problem of secure function evaluation in our framework, where the attacker has to pay for each party it corrupts. Our results demonstrate how knowledge of the attacker's incentives can be used to circumvent known impossibility results in this setting.

Malware Classification with Deep Convolutional Neural Networks <=SEP=> In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.

A Computer-Aided Diagnosis System of Nuclear Cataract <=SEP=> Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated in this paper. Nuclear cataract is graded according to the severity of opacity using slit lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.

Ways to React: Comparing Reactive Languages and Complex Event Processing <=SEP=> Reactive applications demand for detecting the changes that occur in a domain of interest and for timely reactions. Examples range from simple interactive applications to complex monitoring tasks involving distributed and heterogeneous systems. Over the last years, different programming paradigms and solutions have been proposed to support such applications. In this paper, we focus on two prominent approaches: event-based programming, specifically Complex Event Processing (CEP), and Reactive Languages (RLs). CEP systems enable the definition of high level situations of interest from low level primitive events detected in the external environment. On the other hand, RLs support time-changing values and their composition as dedicated language abstractions. These research fields have been investigated by different communities, belonging respectively to the database and the distributed systems areas and to the programming language area. It is our belief that a deeper understanding of these research fields, including their benefits and limitations, their similarities and differences, could drive further developments in supporting reactive applications. For this reason, we propose a first comparison of the two fields. Despite huge differences, we believe that such a comparison can trigger an interesting discussion across the communities, favor knowledge sharing, and let new ideas emerge.

Data mining techniques for customer relationship management <=SEP=> Advancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining—the extraction of hidden predictive information from large databases—organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-oriented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm of data mining, the use of some type of data mining is necessary to accomplish the goals of today’s customer relationship management philosophy.  2002 Elsevier Science Ltd. All rights reserved.

Template-Free Construction of Poems with Thematic Cohesion and Enjambment <=SEP=> Existing poetry generation systems usually focus on particular features of poetry (such as rhythm, rhyme, metaphor) and specific techniques to achieve them. They often resort to template-base solutions, in which it is not always clear how many of the alleged features of the outputs were already present in the template employed. The present paper considers two specific features – thematic consistency, and enjambment – and presents an ngram based construction method that achieves these features without the use of templates. The construction procedure is not intended to produce poetry of high quality, only to achieve the features considered specifically in its design. A set of metrics is defined to capture these features in quantitative terms, and the metrics are applied to system outputs and to samples of both human and computer generated poetry. The results of these tests are discussed in terms of the danger of to ignoring certain features when designing construction procedures but valuing them during evaluation even if they arise from hard-wiring in the resources or serendipitous emergence, and the fundamental need for poets to develop a personal voice – fundamental for human poets and inconsistent with the application of Turing tests.

An ultra-wideband common gate LNA with gm-boosted and noise cancelling techniques <=SEP=> In this paper, an ultra-wideband (UWB) common gate low-noise amplifier (LNA) with gm-boosted and noise-cancelling techniques is presented In this scheme we utilize gm-boosted stage for cancelling the noise of matching device. The bandwidth extension and flat gain are achieved by using of series and shunt peaking techniques. Simulated in .13 um Cmos technology, the proposed LNA achieved 2.38-3.4dB NF and S11 less than -11dB in the 3.1-10.6 GHz band Maximum power gain(S21) is 11dB and -3dB bandwidth is 1.25-11.33 GHz. The power consumption of LNA is 5.8mW.

SDN-based distributed mobility management for 5G networks <=SEP=> Software-Defined Networking (SDN) is transforming the networking ecosystem. SDN allows network operators to easily and quickly introduce new services and flexibly adapt to their requirements, while simplifying the network management to reduce the cost of operation, maintenance and deployment. On the other hand, mobility is a key aspect for the future mobile networks. In this context, Distributed Mobility Management (DMM) has been recently introduced as a new trend to overcome the limitations of the today's mobility management protocols which are highly centralized and hierarchical. Driven from the fact that DMM and SDN share the same principle in which the data and control plane are decoupled, we propose a DMM solution based on SDN architecture called S-DMM. This solution offers a lot of advantages including no need to deploy any mobility-related component at the access router, independence of the underlying technologies, and per-flow mobility support. On one hand, the numerical results prove that S-DMM is more scalable than the legacy DMM. On the other hand, the experiment results from a real implementation show that S-DMM comes at no performance penalty (in terms of handover latency and end-to-end delay) compared to legacy DMM, yet at a slightly better management cost, which makes S-DMM a promising candidate for a mobility management solution in the context of 5G networks.

Categorization and Machine Learning Methods : Current State of the Art By Durga Bhavani Dasari <=SEP=> In this informative age, we find many documents are available in digital forms which need classification of the text. For solving this major problem present researchers focused on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of pre classified documents, the characteristics of the categories. The main benefit of the present approach is consisting in the manual definition of a classifier by domain experts where effectiveness, less use of expert work and straightforward portability to different domains are possible. The paper examines the main approaches to text categorization comparing the machine learning paradigm and present state of the art. Various issues pertaining to three different text similarity problems, namely, semantic, conceptual and contextual are also discussed.

Video shot boundary detection: Seven years of TRECVid activity <=SEP=> Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other contentbased operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example.

A model of saliency-based auditory attention to environmental sound <=SEP=> A computational model of auditory attention to environmental sound, inspired by the structure of the human auditory system, is presented. The model simulates how listeners switch their attention over time between different auditory streams, based on bottom-up and top-down cues. The bottom-up cues are determined by the time-dependent saliency of each stream. The latter is calculated on the basis of an auditory saliency map, which encodes the intensity and the amount of spectral and temporal irregularities of the sound, and binary spectro-temporal masks for the different streams. The top-down cues are determined by the amount of volitional focusing on particular auditory streams. A competitive winner-takes-all mechanism, which balances bottom-up and top-down attention for each stream, determines which stream is selected for entry into the working memory. Consequently, the model is able to delimit the time periods during which particular streams are paid attention to. Although the main ideas could be applied to all types of sound, the implementation of the model was targeted at environmental sound in particular. As an illustration, the model is used to reproduce results from a detailed field experiment on the perception of transportation noise. Finally, it is shown how this model could be a valuable tool, complementing auralization, in the design of outdoor soundscapes.

On the Existence of a Spectrum of Policies that Subsumes the Least Recently Used (LRU) and Least Frequently Used (LFU) Policies <=SEP=> Sam H. Nohs Sang Lyul Mint t Department of Computer Engineering Seoul National University Seoul 151-742, Korea http://ssrnet.snu.ac.kr http://archi.snu.ac.kr We show that there exists a spectrum of block replacement policies that subsumes both the Least Recently Used (LRU) and the Least Frequently Used (LFU) policies. The spectrum is formed according to how much more weight we give to the recent history than to the older history, and is referred to as the LRFU (Least Recently/Frequently Used) policy. Unlike many previous policies that use limited history to make block replacement decisions, the LRFU policy uses the complete reference history of blocks recorded during their cache residency. Nevertheless, the LRFU requires only a few words for each block to maintain such history. This paper also describes an implementation of the LRFU that again subsumes the LRU and LFU implementations. The LRFU policy is applied to buffer caching, and results from trace-driven simulations show that the LRFU performs better than previously known policies for the workloads we considered. This point is reinforced by results from our integration of the LRFU into the FreeBSD operating system.

HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT <=SEP=> Recent developments in memory-augmented neural networks allowed sequential problems requiring long-term memory to be solved, which were intractable for traditional neural networks. However, current approaches still struggle to scale to large memory sizes and sequence lengths. In this paper we show how access to an external memory component can be encoded geometrically through a novel HyperNEAT-based Neural Turing Machine (HyperNTM). The indirect HyperNEAT encoding allows for training on small memory vectors in a bit vector copy task and then applying the knowledge gained from such training to speed up training on larger size memory vectors. Additionally, we demonstrate that in some instances, networks trained to copy nine bit vectors can be scaled to sizes of 1,000 without further training. While the task in this paper is simple, the HyperNTM approach could now allow memory-augmented neural networks to scale to problems requiring large memory vectors and sequence lengths.

A review: Information extraction techniques from research papers <=SEP=> Text extraction is a crucial stage of analyzing Journal papers. Journal papers generally are in PDF format which is semi structured data. Journal papers are presented into different sections like Introduction, Methodology, Experimental setup, Result and analysis etc. so that it is easy to access information from any section as per the reader's interest. The main importance on section extraction is to find a representative subset of the data, which contains the information of the entire set. Various approaches to extract sections from research papers include stastical methods, NLP, Machine Learning etc. In this paper we present review of various extraction techniques from a PDF document.

Accidental death due to complete autoerotic asphyxia associated with transvestic fetishism and anal self-stimulation - case report. <=SEP=> A case is reported of a 36-year-old male, found dead in his locked room, lying on a bed, dressed in his mother's clothes, with a plastic bag over his head, hands tied and with a barrel wooden cork in his rectum. Two pornographic magazines were found on a chair near the bed, so that the deceased could see them well. Asphyxia was controlled with a complex apparatus which consisted of two elastic luggage rack straps, the first surrounding his waist, perineum, and buttocks, and the second the back of his body, and neck. According to the psychological autopsy based on a structured interview (SCID-I, SCID-II) with his father, the deceased was single, unemployed and with a part college education. He had grown up in a poor family with a reserved father and dominant mother, and was indicative of fulfilling DSM-IV diagnostic criteria for alcohol dependence, paraphilia involving hypoxyphilia with transvestic fetishism and anal masturbation and a borderline personality disorder. There was no evidence of previous psychiatric treatment. The Circumstances subscale of Beck's Suicidal Intent Scale (SIS-CS) pointed at the lack of final acts (thoughts or plans) in anticipation of death, and absence of a suicide note or overt communication of suicidal intent before death. Integration of the crime scene data with those of the forensic medicine and psychological autopsy enabled identification of the event as an accidental death, caused by neck strangulation, suffocation by a plastic bag, and vagal stimulation due to a foreign body in the rectum.

Quadruped Robot Running With a Bounding Gait <=SEP=> Scout II, an autonomous four-legged robot with only one actuator per compliant leg is described. We demonstrate the need to model the actuators and the power source of the robot system carefully in order to obtain experimentally valid models for simulation and analysis. We describe a new, simple running controller that requires minimal task level feedback, yet achieves reliable and fast running up to 1.2 m/s. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. In addition, the simple mechanical design of our robot may be used as a template for the control of higher degree of freedom quadrupeds. An energetics analysis reveals a highly efficient system with a specific resistance of 0.32 when based on mechanical power dissipation and of 1.0 when based on total electrical power dissipation.

Virtual Engineering Factory: Creating Experience Base for Industry 4.0 <=SEP=> In recent times, traditional manufacturing is upgrading and adopting Industry 4.0, which supports computerization of manufacturing by round-the-clock connection and communication of engineering objects. Consequently, Decisional DNAbased knowledge representation of manufacturing objects, processes, and system is achieved by virtual engineering objects (VEO), virtual engineering processes (VEP), and virtual engineering factories (VEF), respectively. In this study, assimilation of VEO-VEP-VEF concept in the Cyber-physical systembased Industry 4.0 is proposed. The planned concept is implemented on a case study. Also, Decisional DNA features such as similarity identification and phenotyping are explored for validation. It is concluded that this approach can support Industry 4.0 and can facilitate in real time critical, creative, and effective decision making.

Unsupervised Training Set Generation for Automatic Acquisition of Technical Terminology in Patents <=SEP=> NLP methods for automatic information access to rich technological knowledge sources like patents are of great value. One important resource for accessing this knowledge is the technical terminology of the patent domain. In this paper, we address the problem of automatic terminology acquisition (ATA), i.e., the problem of automatically identifying all technical terms in a document. We analyze technical terminology in patents and define the concept of technical term based on the analysis. We present a novel method for labeling large amounts of high-quality training data for ATA in an unsupervised fashion. We train two ATA methods on this training data, a term candidate classifier and a conditional random field (CRF), and investigate the utility of different types of features. Finally, we show that our method of automatically generating training data is effective and the two ATA methods successfully generalize, considerably increasing recall while preserving high precision relative to a state-of-the-art baseline.

Linear Model Selection by Cross-Validation <=SEP=> JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association. We consider the problem of selecting a model having the best predictive ability among a class of linear models. The popular leave-one-out cross-validation method, which is asymptotically equivalent to many other model selection methods such as the Akaike information criterion (AIC), the Cp, and the bootstrap, is asymptotically inconsistent in the sense that the probability of selecting the model with the best predictive ability does not converge to 1 as the total number of observations n-s o. We show that the inconsistency of the leave-one-out cross-validation can be rectified by using a leave-n,-out cross-validation with nv, the number of observations reserved for validation, satisfying no/n-1 I as n s* xoo. This is a somewhat shocking discovery, because ne/n-* 1 is totally opposite to the popular leave-one-out recipe in cross-validation. Motivations, justifications, and discussions of some practical aspects of the use of the leave-n,-out cross-validation method are provided, and results from a simulation study are presented.

An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records <=SEP=> Detecting database records that are approximate duplicates, but not exact duplicates, is an important task. Databases may contain duplicate records concerning the same real-world entity because of data entry errors, because of un-standardized abbreviations, or because of diierences in the detailed schemas of records from multiple databases, among other reasons. In this paper, we present an eecient algorithm for recognizing clusters of approximately duplicate records. Three key ideas distinguish the algorithm presented. First, a version of the Smith-Waterman algorithm for computing minimum edit-distance is used as a domain-independent method to recognize pairs of approximately duplicate records. Second, the union//nd algorithm is used to keep track of clusters of duplicate records incrementally, as pairwise duplicate relationships are discovered. Third, the algorithm uses a priority queue of cluster subsets to respond adaptively to the size and homogeneity of the clusters discovered as the database is scanned. This typically reduces by over 75% the number of times that the expensive pair-wise record matching (Smith-Waterman or other) is applied, without impairing accuracy. Comprehensive experiments on synthetic databases and on a real database of bibliographic records connrm the eeectiveness of the new algorithm.

Agent based urban growth modeling framework on Apache Spark <=SEP=> The simulation of urban growth is an important part of urban planning and development. Due to large data and computational challenges, urban growth simulation models demand efficient data analytic frameworks for scaling them to large geographic regions. Agent-based models are widely used to observe and analyze the urban growth simulation at various scales. The incorporation of the agent-based model makes the scaling task even harder due to communication and coordination among agents. Many existing agent-based model frameworks were implemented using traditional shared and distributed memory programming models. On the other hand, Apache Spark is becoming a popular platform for distributed big data in-memory analytics. This paper presents an implementation of agent-based sub-model in Apache Spark framework. With the in-memory computation, Spark implementation outperforms the traditional distributed memory implementation using MPI. This paper provides (i) an overview of our framework capable of running urban growth simulations at a fine resolution of 30 meter grid cells, (ii) a scalable approach using Apache Spark to implement an agent-based model for simulating human decisions, and (iii) the comparative analysis of performance of Apache Spark and MPI based implementations.

Psychopathy and the DSM-IV criteria for antisocial personality disorder. <=SEP=> The Axis II Work Group of the Task Force on DSM-IV has expressed concern that antisocial personality disorder (APD) criteria are too long and cumbersome and that they focus on antisocial behaviors rather than personality traits central to traditional conceptions of psychopathy and to international criteria. We describe an alternative to the approach taken in the rev. 3rd ed. of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R; American Psychiatric Association, 1987), namely, the revised Psychopathy Checklist. We also discuss the multisite APD field trials designed to evaluate and compare four criteria sets: the DSM-III-R criteria, a shortened list of these criteria, the criteria for dyssocial personality disorder from the 10th ed. of the International Classification of Diseases (World Health Organization, 1990), and a 10-item criteria set for psychopathic personality disorder derived from the revised Psychopathy Checklist.

Genetic Algorithms + Data Structures = Evolution Programs <=SEP=> If you are looking for Genetic Algorithms Data Structures Evolution Programs in pdf file you can find it here. This is the best place for you where you can find the genetic algorithms data structures evolution programs document. Sign up for free to get the download links. There are 3 sources of download links that you can download and save it in your desktop.

Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching <=SEP=> End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be trained for a given disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel crossentropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.