query: Convolutional neural networks on assembly code for predicting software defects

positive: Predicting defect-prone software modules using support vector machines Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models. 2007 Elsevier Inc. All rights reserved.

positive:
Preliminary comparison of techniques for dealing with imbalance in software defect prediction Imbalanced data is a common problem in data mining when dealing with classification problems, where samples of a class vastly outnumber other classes. In this situation, many data mining algorithms generate poor models as they try to optimize the overall accuracy and perform badly in classes with very few samples. Software Engineering data in general and defect prediction datasets are not an exception and in this paper, we compare different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently. We have used the well-known NASA datasets curated by Shepperd et al. There are differences in the results depending on the characteristics of the dataset and the evaluation metrics, especially if duplicates and inconsistencies are removed as a preprocessing step.
 Further Results and replication package: http://www.cc.uah.es/drg/ease14

positive: Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary data sets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and, finally, limited use of statistical testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository. Overall, an appealing degree of predictive accuracy is observed, which supports the view that metric-based classification is useful. However, our results indicate that the importance of the particular classification algorithm may be less than previously assumed since no significant performance differences could be detected among the top 17 classifiers.

positive: Applying machine learning to software fault-proneness prediction The importance of software testing to quality assurance cannot be overemphasized. The estimation of a module’s fault-proneness is important for minimizing cost and improving the effectiveness of the software testing process. Unfortunately, no general technique for estimating software fault-proneness is available. The observed correlation between some software metrics and fault-proneness has resulted in a variety of predictive models based on multiple metrics. Much work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness. In this paper, we propose the use of machine learning for this purpose. Specifically, given historical data on software metric values and number of reported errors, an Artificial Neural Network (ANN) is trained. Then, in order to determine the importance of each software metric in predicting fault-proneness, a sensitivity analysis is performed on the trained ANN. The software metrics that are deemed to be the most critical are then used as the basis of an ANN-based predictive model of a continuous measure of fault-proneness. We also view fault-proneness prediction as a binary classification task (i.e., a module can either contain errors or be error-free) and use Support Vector Machines (SVM) as a state-of-the-art classification method. We perform a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA’s Metrics Data Program data repository. 2007 Elsevier Inc. All rights reserved.

positive: A systematic analysis of performance measures for classification tasks Article history: Received 14 February 2008 Received in revised form 21 November 2008 Accepted 6 March 2009 Available online 8 May 2009

negative: Using Screen Brightness to Improve Security in Mobile Social Network Access In the today's mobile communications scenario, smartphones offer new capabilities to develop sophisticated applications that seem to make daily life easier and more convenient for users. Such applications, which may involve mobile ticketing, identification, access control operations, etc., are often accessible through social network aggregators, that assume a fundamental role in the federated identity management space. While this makes modern smartphones very powerful devices, it also makes them very attractive targets for spyware injection. This kind of malware is able to bypass classic authentication measures and steal user credentials even when a secure element is used, and can, therefore, perform unauthorized mobile access to social network services without the user's consent. Such an event allows stealing sensitive information or even a full identity theft. In this work, we address this issue by introducing BrightPass, a novel authentication mechanism based on screen brightness. BrightPass allows users to authenticate safely with a PIN-based confirmation in the presence of specific operations on sensitive data. We compare BrightPass with existing schemes, in order to show its usability and security within the social network arena. Furthermore, we empirically assess the security of BrightPass through experimentation. Our tests indicate that BrightPass protects the PIN code against automatic submissions carried out by malware while granting fast authentication phases and reduced error rates.

negative: Using Design Thinking to Differentiate Useful From Misleading Evidence in Observational Research. Few issues can be more important to physicians or patients than that treatment decisions are based on reliable information about benefits and harms. While randomized clinical trials (RCTs) are generally regarded as the most valid source of evidence about benefits and some harms, concerns about their generalizability, costs, and heterogeneity of treatment effects have led to the search for other sources of information to augment or possibly replace trials. This is embodied in the recently passed 21st Century Cures Act, which mandates that the US Food and Drug Administration develop rules for the use of “real world evidence” in drug approval, defined as “data...derived from sources other than randomized clinical trials.”1 A second push toward the use of nontrial evidence is based on the perception that the torrent of electronic health-related data—medical record, genomic, and lifestyle (ie, “Big Data”)—can be transformed into reliable evidence with the use of powerful modern analytic tools. The use of nontrial evidence puts experts who weigh medical evidence, no less a practicing clinician, in a quandary, as the evaluation of such information requires skills well beyond those of basic critical review. The didactic tutorial by Agoritsas et al2 in this issue of JAMA partly addresses that problem, providing a clear, concise, and accessible introduction to several statistical approaches to adjust analyses of observational studies to minimize or eliminate confounding: matching, standard regression, propensity scores, and instrumental variable analyses. While understanding these technical tools is valuable, valid causal inference from nonrandomized studies about treatment effects depends on many factors other than confounding. These include whether the causal question motivating the study is clearly specified, whether the design matches that question and avoids design biases, whether the analysis matches the design, the appropriateness and quality of the data, the fit of adjustment models, and the potential for model searching to find spurious patterns in vast data streams. Some of these issues are recognizable and remediable, whereas others may defy solely analytic solutions, leading to the rejection of some nonrandomized studies to guide treatment. The hallmark of a well-posed causal question is that one can describe an RCT that would answer it. An RCT requires specifying at minimum an eligible population, an intervention, a comparator, and a start time. As posed by Hernán and Taubman,3 an example of a noncausal question is “Does obesity shorten life?” This is not causal because “obesity” is not a randomizable intervention, nor is weight loss. Randomizable interventions—the goal of which might be weight loss—include diet, exercise, gastric bypass surgery, liposuction, or drugs. Once the causal question is specified, the experimental design must mirror it, creating the foundation for a “like vs like” causal contrast on which a valid analysis is based. When observational data not collected for research are analyzed to estimate treatment effects, sometimes they are fed into statistical software without an explicit design and with a causal question that is either imprecise or not stated. This sets the stage for invalid results, even when the statistical methods appear to be properly implemented.4,5 The example of postmenopausal hormone therapy (HT) illustrates the criticality, when analyzing observational data, of posing a precise causal question, with a design and analysis to match. Observational studies, including one from the Nurses’ Health Study, using some of the adjustment techniques described by Agoritsas et al, showed a strong cardiovascular protective association with HT. This result was famously contravened by a subsequent RCT, the Women’s Health Initiative, which showed cardiovascular harm from HT. A subsequent reanalysis of the Nurses’ Health Study data conformed with the RCT results.6 The reanalysis was structured to mirror the clinical question asked in the trial; viz, how does initiating HT in a postmenopausal woman affect her cardiovascular risk? The original observational analysis, using standard regression, effectively excluded women with coronary heart disease events that occurred shortly after initiation of HT, a remediable design bias known in pharmacoepidemiology as “depletion of susceptibles”7 or in other fields as “survivorship bias.” No amount of adjustment for baseline factors between ongoing HT users and nonusers can overcome that bias, but by refining the question, and then using the appropriately tailored design, an appropriate analysis can be crafted. Following the causal thinking reflected in RCT design, most successful examples of observational research that produce findings similar to those from RCTs have compared new drug users with new users of a comparator agent.8,9 The disproven nonrandomized studies referenced at the end of the article by Agoritsas et al all compared users with nonusers. In an RCT, a nonuser is always someone who was eligible and considered for treatment, but that is not guaranteed in an observational study. If instead 2 active treatments indicated for the same condition are compared, it is likely Related article page 748 Opinion

negative: Classifying Photographic and Photorealistic Computer Graphic Images using Natural Image Statistics As computer graphics (CG) is getting more photorealistic, for the purpose of image authentication, it becomes increasingly important to construct a detector for classifying photographic images (PIM) and photorealistic computer graphics (PRCG). To this end, we propose that photographic images contain natural-imaging quality (NIQ) and natural-scene quality (NSQ). NIQ is due to the imaging process, while NSQ is due to the subtle physical light transport in a real-world scene. We explicitly model NSQ of photographic images using natural image statistics (NIS). NIS has been used as an image prior in applications such as image compression and denoising. However, NIS has not been comprehensively and systematically employed for classifying PIM and PRCG. In this work, we study three types of NIS with different statistical order, i.e., NIS derived from the power spectrum, wavelet transform and local patch of images. The experiment shows that the classification is in line with the statistical order of the NIS. The local patch NIS achieves a classification accuracy of 83% which outperforms the features derived from modeling the characteristics of computer graphics.

negative: Goal-Oriented Requirements Engineering: A Guided Tour Goals capture, at different levels of abstraction, the various objectives the system under consideration should achieve. Goal-oriented requirements engineering is concerned with the use of goals for eliciting, elaborating, structuring, specifying, analyzing, negotiating, documenting, and modifying requirements. This area has received increasing attention over the past few years. The paper reviews various research efforts undertaken along this line of research. The arguments in favor of goal orientation are first briefly discussed. The paper then compares the main approaches to goal modeling, goal specification and goal-based reasoning in the many activities of the requirements engineering process. To make the discussion more concrete, a real case study is used to suggest what a goal-oriented requirements engineering method may look like. Experience with such approaches and tool support are briefly discussed as well.

negative: Do RNNs learn human-like abstract word order preferences? RNN language models have achieved state-ofthe-art results on various tasks, but what exactly they are representing about syntax is as yet unclear. Here we investigate whether RNN language models learn humanlike word order preferences in syntactic alternations. We collect language model surprisal scores for controlled sentence stimuli exhibiting major syntactic alternations in English: heavy NP shift, particle shift, the dative alternation, and the genitive alternation. We show that RNN language models reproduce human preferences in these alternations based on NP length, animacy, and definiteness. We collect human acceptability ratings for our stimuli, in the first acceptability judgment experiment directly manipulating the predictors of syntactic alternations. We show that the RNNs’ performance is similar to the human acceptability ratings and is not matched by an n-gram baseline model. Our results show that RNNs learn the abstract features of weight, animacy, and definiteness which underlie soft constraints on syntactic alternations. The best-performing models for many natural language processing tasks in recent years have been recurrent neural networks (RNNs) (Elman, 1990; Sutskever et al., 2014; Goldberg, 2017), but the black-box nature of these models makes it hard to know exactly what generalizations they have learned about their linguistic input: Have they learned generalizations stated over hierarchical structures, or only dependencies among relatively local groups of words (Linzen et al., 2016; Gulordava et al., 2018; Futrell et al., 2018)? Do they represent structures analogous to syntactic dependency trees (Williams et al., 2018), and can they represent complex relationships such as filler–gap dependencies (Chowdhury and Zamparelli, 2018; Wilcox et al., 2018)? In order to make progress with RNNs, it is crucial to determine what RNNs actually learn given currently standard practices; then we can design network architectures, objective functions, and training practices to build on strengths and alleviate weaknesses (Linzen, 2018). In this work, we investigate whether RNNs trained on a language modeling objective learn certain syntactic preferences exhibited by humans, especially those involving word order. We draw on a rich literature from quantitative linguistics that has investigated these preferences in corpora and experiments (e.g., McDonald et al., 1993; Stallings et al., 1998; Bresnan et al., 2007; Rosenbach, 2008). Word order preferences are a key aspect of human linguistic knowledge. In many cases, they can be captured using local co-occurrence statistics: for example, the preference for subject–verb– object word order in English can often be captured directly in short word strings, as in the dramatic preference for I ate apples over I apples ate. However, some word order preferences are more abstract and can only be stated in terms of higherorder linguistic units and abstract features. For example, humans exhibit a general preference for word orders in which words linked in syntactic dependencies are close to each other: such sentences are produced more frequently and comprehended more easily (Hawkins, 1994; Futrell et al., 2015; Temperley and Gildea, 2018). We are interested in whether RNNs learn abstract word order preferences as a way of probing their syntactic knowledge. If RNNs exhibit these preferences for appropriately controlled stimuli, then on some level they have learned the abstractions required to state them. Knowing whether RNNs show human-like word order preferences also bears on their suitability as language generation systems. White and Rajkumar (2012) have shown that language gener-

negative: Sparse concept coding for visual analysis We consider the problem of image representation for visual analysis. When representing images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. To tackle this problem, matrix factorization techniques, such as Singular Vector Decomposition (SVD) and Non-negative Matrix Factorization (NMF), received an increasing amount of interest in recent years. Matrix factorization is an unsupervised learning technique, which finds a basis set capturing high-level semantics in the data and learns coordinates in terms of the basis set. However, the representations obtained by them are highly dense and can not capture the intrinsic geometric structure in the data. In this paper, we propose a novel method, called Sparse Concept Coding (SCC), for image representation and analysis. Inspired from the recent developments on manifold learning and sparse coding, SCC provides a sparse representation which can capture the intrinsic geometric structure of the image space. Extensive experimental results on image clustering have shown that the proposed approach provides a better representation with respect to the semantic structure.

negative: Design and Modeling of A Crowdsource-Enabled System for Urban Parcel Relay and Delivery This paper proposes a crowdsource-enabled system for urban parcel relay and delivery. We consider cyclists and pedestrians as crowdsources who are close to customers and interested in relaying parcels with a truck carrier and undertaking jobs for the last-leg parcel delivery and the first-leg parcel pickup. The crowdsources express their interests in doing so by submitting bids to the truck carrier. The truck carrier then selects bids and coordinates crowdsources’ last-leg delivery (first-leg pickup) with its truck operations. The truck carrier’s problem is formulated as a mixed integer non-linear program which simultaneously i) selects crowdsources to complete the last-leg delivery (first-leg pickup) between customers and selected points for crowdsource-truck relay; and ii) determines the relay points and truck routes and schedule. To solve the truck carrier problem, we first decompose the problem into a winner determination problem and a simultaneous pickup and delivery problem with soft time windows, and propose a Tabu Search based algorithm to iteratively solve the two subproblems. Numerical results show that this solution approach is able to yield close-to-optimum solutions with much less time than using offthe-shelf solvers. By adopting this new system, truck vehicle miles traveled (VMT) and total cost can be reduced compared to pure-truck delivery. The advantage of the system over pure-truck delivery is sensitive to factors such as penalty for servicing outside customers’ desired time windows, truck unit operating cost, time value of crowdsources, and the crowdsource mode.

negative: Survey on data science with population-based algorithms This paper discusses the relationship between data science and population-based algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two categories of literature, which include population-based algorithms solving data analysis problem and utilizing data analysis methods in population-based algorithms. With the exponential increment of data, the data science, or more specifically, the big data analytics has gained increasing attention among researchers. New and more efficient algorithms should be designed to handle this massive data problem. Based on the combination of population-based algorithms and data mining techniques, we understand better the insights of data analytics, and design more efficient algorithms to solve real-world big data analytics problems. Also, the weakness and strength of population-based algorithms could be analyzed via the data analytics along the optimization process, a crucial entity in population-based algorithms.

negative: Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remains largely mysterious. For example in the context of artificial perception, this issue is usually circumvented by having engineers pre-define the spatial structure of the problem the agent has to face. We here show that the structure of space can be autonomously discovered by a naive agent in the form of sensorimotor regularities, that correspond to so called compensable sensory experiences: these are experiences that can be generated either by the agent or its environment. By detecting such compensable experiences the agent can infer the topological and metric structure of the external space in which its body is moving. We propose a theoretical description of the nature of these regularities and illustrate the approach on a simulated robotic arm equipped with an eye-like sensor, and which interacts with an object. Finally we show how these regularities can be used to build an internal representation of the sensor's external spatial configuration.

negative: Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence. Both physiological and behavioral studies have suggested that stimulus-driven neural activity in the sensory pathways can be modulated in amplitude during selective attention. Recordings of event-related brain potentials indicate that such sensory gain control or amplification processes play an important role in visual-spatial attention. Combined event-related brain potential and neuroimaging experiments provide strong evidence that attentional gain control operates at an early stage of visual processing in extrastriate cortical areas. These data support early selection theories of attention and provide a basis for distinguishing between separate mechanisms of attentional suppression (of unattended inputs) and attentional facilitation (of attended inputs).

negative: Advanced Data Mining Techniques The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. I dedicate this book to my grandchildren. Preface The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve uncertain description or other complexities that cause difficulty for the conventional approaches of logistic regression, neural network models, and decision trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Our intent is to cover the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided. Chapter 2 presents the data mining process in more detail. It demonstrates this process with a typical set of data. Visualization of data through data mining software is addressed. Chapter 3 presents memory-based reasoning methods of data mining. Major real applications are described. Algorithms are demonstrated with prototypical data based on real applications. Chapter 4 discusses association rule methods. Application in the form of market basket analysis is discussed. A real data set is described, and a simplified version used to demonstrate association rule methods. Chapter 5 presents fuzzy data mining approaches. Fuzzy decision tree approaches are described, as well as fuzzy association rule applications. Real data mining applications are described and demonstrated Chapter 6 presents Rough …

negative: A cost analysis of RDL-first and mold-first fan-out wafer level packaging Industry interest in fan-out wafer level packaging has been increasing steadily. This is partially because of the potential behind panel-based fan-out packaging, and partially because wafer level packaging may be fulfilling needs not met by interposer-based and 3D technologies. As is the case with any technology, there are variations within the fan-out wafer level packaging category. This paper will focus on two of the primary processes: RDL-first and mold-first (also called chip-first). While these process flows have many of the same activities, those activities are carried out in a different order, and there are a few key steps that will differ. Each process has unique challenges and benefits, and these will be explored and analyzed.

negative: Mostly Exploration-Free Algorithms for Contextual Bandits The contextual bandit literature has traditionally focused on algorithms that address the explorationexploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in practical settings where exploration may be costly or unethical (e.g., clinical trials). Surprisingly, we find that a simple greedy algorithm can be rate-optimal (achieves asymptotically optimal regret) if there is sufficient randomness in the observed contexts (covariates). We prove that this is always the case for a two-armed bandit under a general class of context distributions that satisfy a condition we term covariate diversity. Furthermore, even absent this condition, we show that a greedy algorithm can be rate-optimal with nonzero probability. Thus, standard bandit algorithms may unnecessarily explore. Motivated by these results, we introduce Greedy-First, a new algorithm that uses only observed contexts and rewards to determine whether to follow a greedy algorithm or to explore. We prove that this algorithm is rate-optimal without any additional assumptions on the context distribution or the number of arms. Extensive simulations demonstrate that Greedy-First successfully reduces experimentation and outperforms existing (exploration-based) contextual bandit algorithms such as Thompson sampling or UCB.

negative: Unveiling patterns of international communities in a global city using mobile phone data We analyse a large mobile phone activity dataset provided by Telecom Italia for the Telecom Big Data Challenge contest. The dataset reports the international country codes of every call/SMS made and received by mobile phone users in Milan, Italy, between November and December 2013, with a spatial resolution of about 200 meters. We first show that the observed spatial distribution of international codes well matches the distribution of international communities reported by official statistics, confirming the value of mobile phone data for demographic research. Next, we define an entropy function to measure the heterogeneity of the international phone activity in space and time. By comparing the entropy function to empirical data, we show that it can be used to identify the city’s hotspots, defined by the presence of points of interests. Eventually, we use the entropy function to characterize the spatial distribution of international communities in the city. Adopting a topological data analysis approach, we find that international mobile phone users exhibit some robust clustering patterns that correlate with basic socio-economic variables. Our results suggest that mobile phone records can be used in conjunction with topological data analysis tools to study the geography of migrant communities in a global city.

negative: Automatic Radial Distortion Estimation from a Single Image Many computer vision algorithms rely on the assumptions of the pinhole camera model, but lens distortion with off-the-shelf cameras is usually significant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for many applications, particularly those in human-made environments containing abundant lines. For example, it could be used in place of an extensive calibration procedure to get a mobile robot or quadrotor experiment up and running quickly in an indoor environment. We propose a new method for automatic radial distortion estimation based on the plumb-line approach. The method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon’s division model, robust estimation of circular arcs, and robust estimation of distortion parameters. We perform an extensive empirical study of the method on synthetic images. We include a comparative statistical analysis of how different circle fitting methods contribute to accurate distortion parameter estimation. We finally provide qualitative results on a wide variety of challenging real images. The experiments demonstrate the method’s ability to accurately identify distortion parameters and remove distortion from images.

negative: Millimeter-Wave Substrate Integrated Waveguide Multibeam Antenna Based on the Parabolic Reflector Principle A novel substrate integrated waveguide (SIW) multibeam antenna based on the parabolic reflector principle is proposed and implemented at 37.5 GHz, which takes the advantages of low loss, low profile, high gain, and mass producible. A prototype of the proposed antenna with seven input ports generating a corresponding number of output beams is fabricated in a standard PCB process. The measurements are in good agreement with the simulations, and the results demonstrate that this type of printed multibeam antenna is a good choice for communications applications where mobility and high gain are simultaneously required.

negative: Fashioning with Networks: Neural Style Transfer to Design Clothes Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how the style of a painter can be extracted from an image of the painting and applied to another normal photograph, thus recreating the photo in the style of the painter. The method has been successfully applied to a wide range of images and has since spawned multiple applications and mobile apps. In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes. We construct an approach to personalize and generate new custom clothes based on a user’s preference and by learning the user’s fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how well they align with the user’s fashion style.

negative: Affective Deprivation Disorder : Does it Constitute a Relational Disorder ? Harriet Simons is Adjunct Associate Professor at Smith College School for Social Work and runs a private social work practice for individuals and couples specializing in infertility and Asperger’s relationships. Among her published works are Wanting Another Child: Coping with Secondary Infertility. Jossey-Bass, 1998; (Contributor), Infertility Counseling: A Handbook for Clinicians. Parthenon Press, 1997; and (Co-editor and Contributor) Infertility: Medical, Emotional and Social Considerations. Human Sciences Press, 1984. Jason Thompson is Community Support Officer at SPARAL Disability Services specializing in rehabilitation and lifestyle support for individuals with intellectual and physical disabilities. He has completed certified studies in Applied Behaviour Analysis and Normative Male Alexithymia. He recently published an essay on the subject of alexithymia Alexithymia: An Imaginative Approach, Psychotherapy Australia Journal, vol 14, No 4, Aug. 2008

negative: Persistent Homology: An Introduction and a New Text Representation for Natural Language Processing Persistent homology is a mathematical tool from topological data analysis. It performs multi-scale analysis on a set of points and identifies clusters, holes, and voids therein. These latter topological structures complement standard feature representations, making persistent homology an attractive feature extractor for artificial intelligence. Research on persistent homology for AI is in its infancy, and is currently hindered by two issues: the lack of an accessible introduction to AI researchers, and the paucity of applications. In response, the first part of this paper presents a tutorial on persistent homology specifically aimed at a broader audience without sacrificing mathematical rigor. The second part contains one of the first applications of persistent homology to natural language processing. Specifically, our Similarity Filtration with Time Skeleton (SIFTS) algorithm identifies holes that can be interpreted as semantic “tie-backs” in a text document, providing a new document structure representation. We illustrate our algorithm on documents ranging from nursery rhymes to novels, and on a corpus with child and adolescent writings.

negative: Warpage Tuning Study for Multi-chip Last Fan Out Wafer Level Package In recent years, the IoT popularity pushes the package development of 3C products into a more functional and thinner target. For high I/O density and low cost considered package, the promising Fan-out Wafer Level Packaging (FOWLP) provides a solution to match OSAT existing capability, besides, the chip last process in FOWLP can further enhance the total yield by selectable known-good dies (KGDs). However, under processing, the large portion of molding compound induces high warpage to challenge fabrication limitation. The additional leveling process is usually applied to lower the warpage that caused by the mismatch of coefficient of thermal expansion and Young's modulus from carriers, dies, and molding compound. This process results in the increase of package cost and even induce internal damages that affect device reliability. In order to avoid leveling process and improve warpage trend, in this paper, we simulated several models with different design of molding compound and dies, and then developed a multi-chip last FOWLP test vehicle by package dimension of 12x15 mm2 with 8x9 and 4x9 mm2 multiple dies, respectively. The test vehicle performed three redistribution layers (RDLs) including one fine pitch RDL of line width/line spacing 2um/2um, which is also the advantage of multi-chip last FOWLP, and also exhibited ball on trace structure for another low cost option. For the wafer warpage discussion, the results showed that tuning the thickness of molding compound can improve warpage trend, especially in the application of high modulus carrier, which improved wafer warpage within 1mm, for package warpage discussion, the thinner die can lower the warpage of package. Through well warpage controlling, the multi-chip last FOWLP package with ball on trace design was successfully presented in this paper, and also passed the package level reliability of TCB 1000 cycles, HTSL 1000 hrs, and uHAST 96 hrs, and drop test by board level reliability.

negative: Eccentric training in chronic painful impingement syndrome of the shoulder: results of a pilot study Treatment with painful eccentric muscle training has been demonstrated to give good clinical results in patients with chronic Achilles tendinosis. The pain mechanisms in chronic painful shoulder impingement syndrome have not been scientifically clarified, but the histological changes found in the supraspinatus tendon have similarities with the findings in Achilles tendinosis. In this pilot study, nine patients (five females and four males, mean age 54 years) with a long duration of shoulder pain (mean 41 months), diagnosed as having shoulder impingement syndrome and on the waiting list for surgical treatment (mean 13 months), were included. Patients with arthrosis in the acromio-clavicular joint or with large calcifications causing mechanical impingement during horizontal shoulder abduction were not included. We prospectively studied the effects of a specially designed painful eccentric training programme for the supraspintus and deltoideus muscles (3×15 reps, 2 times/day, 7 days/week, for 12 weeks). The patients evaluated the amount of shoulder pain during horizontal shoulder activity on a visual analogue scale (VAS), and satisfaction with treatment. Constant score was assessed. After 12 weeks of treatment, five patients were satisfied with treatment, their mean VAS had decreased (62–18, P<0.05), and their mean Constant score had increased (65–80, P<0.05). At 52-week follow-up, the same five patients were still satisfied (had withdrawn from the waiting list for surgery), and their mean VAS and Constant score were 31 and 81, respectively. Among the satisfied patients, two had a partial suprasinatus tendon rupture, and three had a Type 3 shaped acromion. In conclusion, the material in this study is small and the follow-up is short, but it seems that although there is a long duration of pain, together with bone and tendon abnormalities, painful eccentric supraspinatus and deltoideus training might be effective. The findings motivate further studies.

negative: A Proximity-Aware Hierarchical Clustering of Faces In this paper, we propose an unsupervised face clustering algorithm called “Proximity-Aware Hierarchical Clustering” (PAHC) that exploits the local structure of deep representations. In the proposed method, a similarity measure between deep features is computed by evaluating linear SVM margins. SVMs are trained using nearest neighbors of sample data, and thus do not require any external training data. Clus- ters are then formed by thresholding the similarity scores. We evaluate the clustering performance using three challenging un- constrained face datasets, including Celebrity in Frontal-Profile (CFP), IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3) datasets. Experimental results demonstrate that the proposed approach can achieve significant improvements over state-of-the-art methods. Moreover, we also show that the proposed clustering algorithm can be applied to curate a set of large-scale and noisy training dataset while maintaining sufficient amount of images and their variations due to nuisance factors. The face verification performance on JANUS CS3 improves significantly by finetuning a DCNN model with the curated MS-Celeb-1M dataset which contains over three million face images.

negative: Touch, Taste, & Smell User Interfaces: The Future of Multisensory HCI The senses we call upon when interacting with technology are very restricted. We mostly rely on vision and audition, increasingly harnessing touch, whilst taste and smell remain largely underexploited. In spite of our current knowledge about sensory systems and sensory devices, the biggest stumbling block for progress concerns the need for a deeper understanding of people's multisensory experiences in HCI. It is essential to determine what tactile, gustatory, and olfactory experiences we can design for, and how we can meaningfully stimulate such experiences when interacting with technology. Importantly, we need to determine the contribution of the different senses along with their interactions in order to design more effective and engaging digital multisensory experiences. Finally, it is vital to understand what the limitations are that come into play when users need to monitor more than one sense at a time. The aim of this workshop is to deepen and expand the discussion on touch, taste, and smell within the CHI community and promote the relevance of multisensory experience design and research in HCI.

negative: Minimum Text Corpus Selection for Limited Domain Speech Synthesis This paper concerns limited domain TTS system based on the concatenative method, and presents an algorithm capable to extract the minimal domainoriented text corpus from the real data of the given domain, while still reaching the maximum coverage of the domain. The proposed approach ensures that the least amount of texts are extracted, containing the most common phrases and (possibly) all the words from the domain. At the same time, it ensures that appropriate phrase overlapping is kept, allowing to find smooth concatenation in the overlapped regions to reach high quality synthesized speech. In addition, several recommendations allowing a speaker to record the corpus more fluently and comfortably are presented and discussed. The corpus building is tested and evaluated on several domains differing in size and nature, and the authors present the results of the algorithm and demonstrate the advantages of using the domain oriented corpus for speech synthesis.

negative: BreakDancer: An algorithm for high resolution mapping of genomic structural variation Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods with which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including insertion-deletions (indels), inversions and translocations. We examined BreakDancer's performance in simulation, in comparison with other methods and in analyses of a sample from an individual with acute myeloid leukemia and of samples from the 1,000 Genomes trio individuals. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.

negative: The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models Defect prediction models help software organizations to anticipate where defects will appear in the future. When training a defect prediction model, historical defect data is often mined from a Version Control System (VCS, e.g., Subversion), which records software changes at the file-level. Software metrics, on the other hand, are often calculated at the class- or method-level (e.g., McCabe's Cyclomatic Complexity). To address the disagreement in granularity, the class- and method-level software metrics are aggregated to file-level, often using summation (i.e., McCabe of a file is the sum of the McCabe of all methods within the file). A recent study shows that summation significantly inflates the correlation between lines of code (Sloc) and cyclomatic complexity (Cc) in Java projects. While there are many other aggregation schemes (e.g., central tendency, dispersion), they have remained unexplored in the scope of defect prediction. In this study, we set out to investigate how different aggregation schemes impact defect prediction models. Through an analysis of 11 aggregation schemes using data collected from 255 open source projects, we find that: (1) aggregation schemes can significantly alter correlations among metrics, as well as the correlations between metrics and the defect count; (2) when constructing models to predict defect proneness, applying only the summation scheme (i.e., the most commonly used aggregation scheme in the literature) only achieves the best performance (the best among the 12 studied configurations) in 11 percent of the studied projects, while applying all of the studied aggregation schemes achieves the best performance in 40 percent of the studied projects; (3) when constructing models to predict defect rank or count, either applying only the summation or applying all of the studied aggregation schemes achieves similar performance, with both achieving the closest to the best performance more often than the other studied aggregation schemes; and (4) when constructing models for effort-aware defect prediction, the mean or median aggregation schemes yield performance values that are significantly closer to the best performance than any of the other studied aggregation schemes. Broadly speaking, the performance of defect prediction models are often underestimated due to our community's tendency to only use the summation aggregation scheme. Given the potential benefit of applying additional aggregation schemes, we advise that future defect prediction models should explore a variety of aggregation schemes.

negative: Software metrics : usability and evaluation of software quality It is difficult to understand, let alone improve, the quality of software without the knowledge of its software development process and software products. There must be some measurement process to predict the software development, and to evaluate the software products. This thesis provides a brief view on Software Quality, Software Metrics, and Software Metrics methods that will predict and measure the specified quality factors of software. It further discusses about the Quality as given by the standards such as ISO, principal elements required for the Software Quality and Software Metrics as the measurement technique to predict the Software Quality. This thesis was performed by evaluating a source code developed in Java, using Software Metrics, such as Size Metrics, Complexity Metrics, and Defect Metrics. Results show that, the quality of software can be analyzed, studied and improved by the usage of software metrics.

negative: Improving code review effectiveness through reviewer recommendations Effectively performing code review increases the quality of software and reduces occurrence of defects. However, this requires reviewers with experiences and deep understandings of system code. Manual selection of such reviewers can be a costly and time-consuming task. To reduce this cost, we propose a reviewer recommendation algorithm determining file path similarity called FPS algorithm. Using three OSS projects as case studies, FPS algorithm was accurate up to 77.97%, which significantly outperformed the previous approach.

negative: An experimental comparison of reading techniques for defect detection in UML design documents The basic motivation for software inspections is to detect and remove defects before they propagate to subsequent development phases where their detection and removal becomes more expensive. To attain this potential, the examination of the artefact under inspection must be as thorough and detailed as possible. This implies the need for systematic reading techniques that tell inspection participants what to look for and, more importantly, how to scrutinise a software document. Recent research efforts investigated the benefits of scenario-based reading techniques for defect detection in functional requirements and functional code documents. A major finding has been that these techniques help inspection teams find more defects than existing state-of-the-practice approaches, such as, ad-hoc or checklist-based reading (CBR). In this paper we describe and experimentally compare one scenariobased reading technique, namely perspective-based reading (PBR), for defect detection in objectoriented design documents using the notation of the Unified Modelling Language (UML) with the more traditional CBR approach. The comparison was performed in a controlled experiment with 18 practitioners as subjects. Our results indicate that PBR is more effective than CBR (i.e., it resulted in inspection teams detecting on average 41% more unique defects than CBR). Moreover the cost of defect detection using PBR is significantly lower than CBR (i.e., PBR exhibits on average a 58% cost per defect improvement over CBR). This study therefore provides evidence demonstrating the efficacy of PBR scenarios for defect detection in UML design documents. In addition, it demonstrates that a PBR inspection is a promising approach for improving the quality of models developed using the UML notation.

negative: Texture Segmentation Based Video Compression Using Convolutional Neural Networks There has been a growing interest in using different approaches to improve the coding efficiency of modern video codec in recent years as demand for web-based video consumption increases. In this paper, we propose a model-based approach that uses texture analysis/synthesis to reconstruct blocks in texture regions of a video to achieve potential coding gains using the AV1 codec developed by the Alliance for Open Media (AOM). The proposed method uses convolutional neural networks to extract texture regions in a frame, which are then reconstructed using a global motion model. Our preliminary results show an increase in coding efficiency while maintaining satisfactory visual quality. Introduction With the increasing amount of videos being created and consumed, better video compression tools are needed to provide fast transmission and high visual quality. Modern video coding standards utilize spatial and temporal redundancy in the videos to achieve high coding efficiency and high visual quality with motion compensation techniques and 2-D orthogonal transforms. However, efficient exploitation of statistical dependencies measured by a mean squared error (MSE) does not always produce the best psychovisual result, and may require higher data rate to preserve detail information in the video. Recent advancement in GPU computing has enabled the analysis of large scale data using deep learning method. Deep learning techniques have shown promising performance in many applications such as object detection, natural language process, and synthetic images generation [1, 2, 3, 4]. Several methods have been developed for video applications to improve coding efficiency using deep learning. In [5], sample adaptive offset (SAO) is replaced by a CNN-based in-loop filter (IFCNN) to improve the coding efficiency in HEVC. By learning the predicted residue between the quantized reconstructed frames obtained after deblocking filter (DF) and the original frames, IFCNN is able to reconstruct video frames with higher quality without requiring any bit transmission during coding process. Similar to [5], [6] proposes a Variable-filter-size Residue-learning CNN (VRCNN) to improving coding efficiency by replacing DF and SAO in HEVC. VRCNN is based on the concept of ARCNN [7] which is originally designed for JPEG applications. Instead of only using spatial information to train a CNN to reduce the coding artifacts in HEVC, [8] proposed a spatial temporal residue network (STResNet) as an additional in-loop filter after SAO. A rate-distortion optimization strategy is used to control the on/off switch of the proposed in-loop filter. There are also some works that have been done in the decoder of HEVC to improve the coding efficiency. In [9], a deep CNN-based auto decoder (DCAD) is implemented in the decoder of HEVC to improve the video quality of decoded video. DCAD is trained to learn the predict residue between decoded video frames and original video frames. By adding the predicted residual generated from DCAD to the compressed video frames, this method enhances the compressed video frames to higher quality. In summary, the above methods improve the coding efficiency by enhancing the quality of reconstructed video frames. However, they require different trained models for video reconstruction at different quantization levels. We are interested in developing deep learning approaches to only encode visually relevant information and use a different coding method for “perceptually insignificant” regions in a frame, which can lead to substantial data rate reductions while maintaining visual quality. In particular, we have developed a model based approach that can be used to improve the coding efficiency by identifying texture areas in a video frame that contain detail irrelevant information, which the viewer does not perceive specific details and can be skipped or encoded at a much lower data rate. The task is then to divide a frame into “perceptually insignificant” texture region and then use a texture model for the pixels in that region. In 1959, Schreiber and colleagues proposed a coding method that divides an image into textures and edges and used it in image coding [10]. This work was later extended by using the human visual system and statistical model to determine the texture region [11, 12, 13]. More recently, several groups have focused on adapting perceptual based approaches to the video coding framework [14]. In our previous work [15], we introduced a texture analyzer before encoding the input sequences to identify detail irrelevant regions in the frame which are classified into different texture classes. At the encoder, no inter-frame prediction is performed for these regions. Instead, displacement of the entire texture region is modeled by just one set of motion parameters. Therefore, only the model parameters are transmitted to the decoder for reconstructing the texture regions using a texture synthesizer. Non-texture regions in the frame are coded conventionally. Since this method uses feature extraction based on texture segmentation technique, a proper set of parameters are required to achieve accurate texture segmentation for different videos. Deep learning methods usually do not require such parameter tuning for inference. As a result, deep learning techniques can be developed to perform texture segmentation and classification for the proposed model-based video coding. A Fisher vector convolution neural networks (FVCNN) that can produce segmentation labels for different texture classes was proposed in [16]. One of the advantage of FV-CNN is that the image input size is flexible and is not limited by the network architecture. Instead of doing pixel-wise classification on texture regions, a texture classification CNN network was described in [17]. To reduce computational expenses, [17] uses a small classification network to classify image patches with size of 227 × 227. A smaller network is needed to classify smaller image patches in our case. In this paper, we propose a block-based texture segmentation method to extract texture region in a video frame using convolutional neural networks. The block-based segmentation network classifies each 16 × 16 block in a frame as texture or non-texture. The identified texture region is then synthesized using the temporal correlations among the frames. Our method was implemented using the AOM/AV1 codec. Preliminary results show significant bitrate savings while maintaining a satisfactory visual quality.

negative: A study of the uniqueness of source code This paper presents the results of the first study of the uniqueness of source code. We define the uniqueness of a unit of source code with respect to the entire body of written software, which we approximate with a corpus of 420 million lines of source code. Our high-level methodology consists of examining a collection of 6,000 software projects and measuring the degree to which each project can be `assembled' solely from portions of this corpus, thus providing a precise measure of `uniqueness' that we call syntactic redundancy. We parameterized our study over a variety of variables, the most important of which being the level of granularity at which we view source code. Our suite of experiments together consumed approximately four months of CPU time, providing quantitative answers to the following questions: at what levels of granularity is software unique, and at a given level of granularity, how unique is software? While we believe these questions to be of intrinsic interest, we discuss possible applications to genetic programming and developer productivity tools.

negative: Self-organized computation with unreliable , memristive nanodevices Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network—a self-organized spatial filter array—that demonstrate its performance as a function of defects and device variation. (Some figures in this article are in colour only in the electronic version) 1. Nanoelectronics and computing paradigms Nanodevices are crummy1. High defect rates, high device variability, device ageing, and limitations on device complexity (e.g., two-terminal devices are much easier to build) are to be expected if we intend to mass produce nanoelectronic systems economically. Not only that, it is almost axiomatic among many researchers that such systems will be built from simple structures, such as crossbars, composed of nanodevices that must be configured to implement the desired functionality (Heath et al 1998, Williams and Kuekes 2000, Kuekes and Williams 2002, DeHon 2003, Stan et al 2003, Ziegler and Stan 2003, Snider et al 2004, DeHon 2005, Ma et al 2005, Snider 2005, Snider et al 2005, Strukov and Likharev 2005). So we are faced with the challenge of computing with devices that are not only crummy, but dynamical as well. Can reliable Boolean logic systems be built from such crummy devices? Yes, but we argue that at some point as 1 ‘Crummy’ was introduced into the technical lexicon by Moore and Shannon (1954). device dimensions scale down, the overhead and complexity become so costly that performance and density improvements will hit a barrier. In the next section we discuss two frequently proposed strategies for implementing Boolean logic with crummy, dynamical devices—reconfiguration and coding theory—and argue that each has severe scalability problems. This is not suggesting that logic at the nanoscale is not worth pursuing. It clearly is, and semiconductor manufacturers have the economic motivation to continue scaling down as aggressively as their profits permit. Rather we are suggesting that the ‘killer app’ for nanoelectronics lies elsewhere. An alternative computational paradigm—adaptive, recurrent networks—is computationally powerful and requires only two types of components, which we call ‘edges’ and ‘nodes’. Edge to node ratios are typically high, hundreds to thousands, and edges are, unfortunately, difficult to implement efficiently. This difficulty has made these networks extremely unpopular; software implementations are impossibly slow, and hardware implementations require far too much area. 0957-4484/07/365202+13$30.00 1 © 2007 IOP Publishing Ltd Printed in the UK Nanotechnology 18 (2007) 365202 G S Snider In this paper we propose using memristive nanodevices to implement edges, conventional analog and digital electronics to implement nodes, and pairs of bipolar pulses, called ‘spikes’, to implement communication. The tiny size of the nanodevices implementing edges would allow, for the first time, a practical hardware implementation of adaptive, recurrent networks. We suggest that such networks are a better architectural fit to nanoelectronics than Boolean logic circuits. They are robust in the presence of device variations and defects; they degrade gracefully as devices become defective over time, and can even ‘self-heal’ in response to internal change; they can be implemented with simple, crossbar-like structures. Just as importantly, they can be structured to self-organize their computations, sidestepping scalability problems with device characterization, compilation and configuration. Such systems can contain large numbers of defective components, but we will not need to discover where they are—in fact, we will not care where they are. The system can adapt and ‘rewire’ itself around them. 2. Boolean logic is hard with crummy, dynamical devices 2.1. Reconfiguration to the rescue? Several researchers have proposed using a combination of device characterization, defect avoidance, and configuration to handle initial static defects (Stan et al 2003, DeHon 2003, Snider et al 2004, Strukov and Likharev 2005, Snider and Williams 2007). The strategy is a three-pass algorithm: (1) Characterization. Analyze every nanowire and nanodevice in the system and compile a list of resources which are defective (stuck open or stuck closed, shorted, broken, out-of-spec, etc). Such analysis algorithms were used in the Teramac system (Culbertson et al 1997). (2) Defect avoidance. Give the list of defects from pass 1 to a compiler which maps a desired circuit onto the defective fabric, routing around defective components (Culbertson et al 1997, Snider et al 2004, Strukov and Likharev 2005, Snider and Williams 2007). (3) Configuration. Give the mapping determined in pass 2 to a controller that electrically configures each of the mapped components. Since every chip will have a unique set of defects, the above process must be applied to each and every chip. This presents some interesting challenges for manufacturing, since the time required to perform the above steps will contribute to production cost. Characterization (pass 1) is problematic due to device variability—the functional state of a device (or wire) is not necessarily discrete (working versus nonworking) but can lie on a continuum. And characterizing, say, 1012 nanodevices (DeHon et al 2005) in a reasonable amount of time is not likely to be trivial, especially given the bottleneck of pins on the chip. It is not clear if existing characterization algorithms could be applied to systems like this, or how well they would scale. Compilation (pass 2) also presents considerable risk. Compiling circuits onto configurable chips (such as FPGAs) today is a time-consuming process, due to the NP-hard placement and routing problems that lie at the compiler’s core. Even circuits comprising only a few tens of thousands of gates can require several minutes to compile, depending on the degree of optimization needed—and that’s assuming a defect-free target, where a single compilation can be used to manufacture thousands of parts. One proposal for minimizing this problem requires an initial ‘ideal’ compilation onto a hypothetical defect-free fabric, laying out components a little more sparsely than optimal. This would be done only once for each (circuit type, chip type) combination, so one could afford to spend enormous amounts of compute time on this to arrive at this ideal configuration. The configuration of an individual chip on the production line would then be viewed as a ‘perturbation’ of the ideal configuration, with resource allocation shifted as necessary to avoid defects. One might even combine the characterization pass with this pass for further speed improvements. This strategy might be workable. But it is not clear how well this would scale, or how robust this would be in the presence of defect clustering. Configuration (pass 3) is the most straightforward, with the most significant risk being configuration time restrictions due to the pin bottleneck and power dissipation. Note that the above approach of characterize, compile, configure does not handle device death. What happens when a nanodevice stops working and the chip starts computing nonsense? If the nanodevices are reconfigurable, the system can be stopped and reconfigured to work around the newly formed defects. But that assumes additional circuitry to detect the malfunction (e.g. self-checking circuits (Wakerly 1978)), and a companion host processor capable of implementing the three passes in order to reconfigure the chip. Such a processor would have to be fast, reliable (which probably means it would not be implemented with nanodevices), and likely would require a significant amount of memory. Implementing such a coprocessor would seem to negate the benefits that one was presumably getting from the nanoscale circuit in the first place. 2.2. Coding theory to the rescue? Coding theory has been used for decades to robustly transmit information over noisy channels by adding a small amount of redundant information. Can coding theory do the same for logic circuits by adding a small number of redundant gates or components in order to achieve reliable operation? von Neumann (1956), looking forward to nanoscale computation, was perhaps the first to address this question. His approach used a code that replicated each logic gate, and combined the replicated gate outputs in a clever way so that the entire system achieved a desired level of reliability. Although his primary concern was correcting errors induced by transient faults, the approach could also compensate for initially defective devices or ‘device deaths’ as long as the failing devices were randomly distributed and the number did not exceed a threshold (the trade-off being that device deaths would reduce the system’s tolerance of transient faults). The overhead for his scheme could be enormous, though. Replication factors for Boolean logic went to infinity as fault rates approached about 1%. This bound has been improved by later researchers, with