Query: Propagating Uncertainty through the tanh Function with Application to Reservoir Computing

Relevant passages:
Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting <=SEP=> We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form , the prediction of at time is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.

Minimum Complexity Echo State Network <=SEP=> Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the reservoir) and an adaptable readout form the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping. The field of RC has been growing rapidly with many successful applications. However, RC has been criticized for not being principled enough. Reservoir construction is largely driven by a series of randomized model-building stages, with both researchers and practitioners having to rely on a series of trials and errors. To initialize a systematic study of the field, we concentrate on one of the most popular classes of RC methods, namely echo state network, and ask: What is the minimal complexity of reservoir construction for obtaining competitive models and what is the memory capacity (MC) of such simplified reservoirs? On a number of widely used time series benchmarks of different origin and characteristics, as well as by conducting a theoretical analysis we show that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology. The (short-term) of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value.

The Matrix Cookbook <=SEP=> Acknowledgements: We would like to thank the following for contributions and suggestions: Bill Baxter, Brian Templeton, Christian Rishøj, Christian Schröppel Douglas L. Theobald, Esben Hoegh-Rasmussen, Glynne Casteel, Jan Larsen, Jun Bin Gao, Jürgen Struckmeier, Kamil Dedecius, Korbinian Strimmer, Lars Christiansen, Lars Kai Hansen, Leland Wilkinson, Liguo He, Loic Thibaut, Miguel Barão, Ole Winther, Pavel Sakov, Stephan Hattinger, Vasile Sima, Vincent Rabaud, Zhaoshui He. We would also like thank The Oticon Foundation for funding our PhD studies.

Intriguing properties of neural networks <=SEP=> Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent. We can cause the network to misclassify an image by applying a certain hardly perceptible perturbation, which is found by maximizing the network’s prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.

The''echo state''approach to analysing and training recurrent neural networks <=SEP=> The report introduces a constructive learning algorithm for recurrent neural networks, which modifies only the weights to output units in order to achieve the learning task. key words: recurrent neural networks, supervised learning Zusammenfassung. Der Report führt ein konstruktives Lernverfahren für rekurrente neuronale Netze ein, welches zum Erreichen des Lernzieles lediglich die Gewichte der zu den Ausgabeneuronen führenden Verbindungen modifiziert. Stichwörter: rekurrente neuronale Netze, überwachtes Lernen

Reweighted Wake-Sleep <=SEP=> Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible. We propose a novel interpretation of the wake-sleep algorithm which suggests that better estimators of the gradient can be obtained by sampling latent variables multiple times from the inference network. This view is based on importance sampling as an estimator of the likelihood, with the approximate inference network as a proposal distribution. This interpretation is confirmed experimentally, showing that better likelihood can be achieved with this reweighted wake-sleep procedure. Based on this interpretation, we propose that a sigmoid belief network is not sufficiently powerful for the layers of the inference network, in order to recover a good estimator of the posterior distribution of latent variables. Our experiments show that using a more powerful layer model, such as NADE, yields substantially better generative models.

Design and Synthesis of Synchronization Skeletons Using Branching Time Temporal Logic <=SEP=> We propose a method of constructing concurrent programs in which the synchronization skeleton of the program ~s automatically synthesized from a high-level (branching time) Temporal Logic specification. The synchronization skeleton is an abstraction of the actual program where detail irrelevant to synchronization is suppressed. For example, in the synchronization skeleton for a solution to the critical section problem each process's critical section may be viewed as a single node since the internal structure of the critical section is unimportant. Most solutions to synchronization problems in the literature are in fact given as synchronization skeletons. Because synchronization skeletons are in general finite state, the propositional version of Temporal Logic can be used to specify their properties. Our synthesis method exploits the (bounded) finite model property for an appropriate propositional Temporal Logic which asserts that if a formula of the logic is satisfiable, it is satisfiable in a finite model (of size bounded by a function of the length of the formula). Decision procedures have been devised which, given a formula of Temporal Logic, f, will decide whether f is satisfiable or unsatisfiable. If f is satisfiable, a finite model of f is constructed, in our application, unsatisfiability of f means that the specification is inconsistent (and must be reformulated). If the formula f is satisfiable, then the specification it expresses is consistent. A model for f with a finite number of states is constructed by the decision procedure. The synchronization skeleton of a program meeting the specification can be read from this model. The finite model property ensures that any program whose synchronization properties can be expressed in propositional Temporal Logic can be realized by a system of concurrently running processes, each of which is a finite state machine. initially, the synchronization skeletons we synthesize wi]] be for concurrent programs running in a shared-memory environment and for monitors. However, we believe that it is also possible to extend these techniques to synthesize distributed programs. One such application would be the automatic synthesis of network communication protocols from propositional Temporal Logic specifications. Previous efforts toward parallel program synthesis can be found in the work of [LA78] and iRK80]. [LA78] uses a specification language that is essentially predicate

Cross-Domain Perceptual Reward Functions <=SEP=> In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agent’s environment. When humans are learning to complete tasks, we regularly utilize alternative sources that guide our understanding of the problem. Such task representations allow one to specify goals on their own terms, thus providing specifications that can be appropriately interpreted across various environments. This motivates our own work, in which we represent goals in environments that are different from the agent’s. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned rewards that represent the visual similarity between an agent’s state and a cross-domain goal image. We report results for learning the CDPRs with a deep neural network and using them to solve two tasks with deep reinforcement learning.

Classifying distinct basal cell carcinoma subtype by means of dermatoscopy and reflectance confocal microscopy. <=SEP=> BACKGROUND
The current guidelines for the management of basal cell carcinoma (BCC) suggest a different therapeutic approach according to histopathologic subtype. Although dermatoscopic and confocal criteria of BCC have been investigated, no specific studies were performed to evaluate the distinct reflectance confocal microscopy (RCM) aspects of BCC subtypes.


OBJECTIVES
To define the specific dermatoscopic and confocal criteria for delineating different BCC subtypes.


METHODS
Dermatoscopic and confocal images of histopathologically confirmed BCCs were retrospectively evaluated for the presence of predefined criteria. Frequencies of dermatoscopic and confocal parameters are provided. Univariate and adjusted odds ratios were calculated. Discriminant analyses were performed to define the independent confocal criteria for distinct BCC subtypes.


RESULTS
Eighty-eight BCCs were included. Dermatoscopically, superficial BCCs (n=44) were primarily typified by the presence of fine telangiectasia, multiple erosions, leaf-like structures, and revealed cords connected to the epidermis and epidermal streaming upon RCM. Nodular BCCs (n=22) featured the classic dermatoscopic features and well outlined large basaloid islands upon RCM. Infiltrative BCCs (n=22) featured structureless, shiny red areas, fine telangiectasia, and arborizing vessels on dermatoscopy and dark silhouettes upon RCM.


LIMITATIONS
The retrospective design.


CONCLUSION
Dermatoscopy and confocal microscopy can reliably classify different BCC subtypes.

Height and gradient from shading <=SEP=> The method described here for recovering the shape of a surface from a shaded image can deal with complex, wrinkled surfaces. Integrability can be enforced easily because both surface height and gradient are represented. (A gradient field is integrable if it is the gradient of some surface height function.) The robustness of the method stems in part from linearization of the reflectance map about the current estimate of the surface orientation at each picture cell. (The reflectance map gives the dependence of scene radiance on surface orientation.) The new scheme can find an exact solution of a given shape-from-shading problem even though a regularizing term is included. The reason is that the penalty term is needed only to stabilize the iterative scheme when it is far from the correct solution; it can be turned off as the solution is approached. This is a reflection of the fact that shape-from-shading problems are not ill posed when boundary conditions are available, or when the image contains singular points. This article includes a review of previous work on shape from shading and photoclinometry. Novel features of the new scheme are introduced one at a time to make it easier to see what each contributes. Included is a discussion of implementation details that are important if exact algebraic solutions of synthetic shape-from-shading problems are to be obtained. The hope is that better performance on synthetic data will lead to better performance on real data.

Origami Pop-up Card Generation from 3D Models Using a Directed Acyclic Graph <=SEP=> Origami is a paper art used to create three-dimensional (3D) objects by cutting and folding a single sheet of paper. Origami is labor-intensive and requires a high skill level to generate two-dimensional (2D) objects that pop-up into realistic 3D objects. However, this special feature makes designing an origami architecture procedure challenging. This paper provides a novel algorithm to create an origami paper card from a 3D model with a user-specified folding line. The algorithm segments the 2D shape from a 3D model and creates layers of the pop-up paper card using a directed acyclic graph. After applying layers to the layout, the algorithm creates connections between layers, and then outputs the origami layout. Based on a directed acyclic graph, the algorithm computes a class of paper architectures containing two sets of layers and connections that approximate the input geometry while guaranteeing that a pop up card is physically realizable. The proposed method is demonstrated with a number of paper pop-ups, and physical experimental results are presented.

A Corpus and Model Integrating Multiword Expressions and Supersenses <=SEP=> This paper introduces a task of identifying and semantically classifying lexical expressions in running text. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether singleor multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1.

Misplaced Trust ? Exploring the Structure of the E-Government-Citizen Trust Relationship <=SEP=> A growing body of research focuses on the relationship between e-government, the relatively new mode of citizen-to-government contact founded in information and communications technologies, and citizen trust in government. For many, including both academics and policy makers, e-government is seen as a potentially transformational medium, a mode of contact that could dramatically improve citizen perceptions of government service delivery and possibly reverse the long-running decline in citizen trust in government. To date, however, the literature has left significant gaps in our understanding of the e-government-citizen trust relationship. This study intends to fill some of these gaps. Using a cross-sectional sample of 787 end users of US federal government services, data from the American Customer Satisfaction Index study, and structural equation modeling statistical techniques, this study explores the structure of the e-government-citizen trust relationship. Included in the model are factors influencing the decision to adopt e-government, as well as prior expectations, overall satisfaction, and outcomes including both confidence in the particular agency experienced and trust in the federal government overall. The findings suggest that although e-government The authors would like to thank Professor Claes Fornell of the University ofMichigan for granting access to the ACSI data that made this study possible. Thanks also to Jason Joyner of CFI Group for his extensive and helpful comments on an early version of the article. Forrest V. Morgeson III is a Research Scientist at the ACSI in Ann Arbor, MI. He has a PhD in Political Science from the University of Pittsburgh. His recent research focuses on the financial impact of consumer satisfaction and citizen satisfaction with federal government services—both online and offline—and has recently been published or is forthcoming in journals such as Public Administration Review, Electronic Government: an International Journal, Journal of Marketing, Marketing Science, and the International Journal of Research in Marking. David VanAmburg is Director of the American Customer Satisfaction Index. Mr VanAmburg has acted as Director for the ACSI’s Federal government-wide satisfaction measurement since its inception in 1999 and of the ACSI as a whole since 2001. He has lectured extensively both in the United States and abroad on topics related to customer satisfaction, quality, customer loyalty, and shareholder value, and co-authored The American Customer Satisfaction Index at Ten Years, ACSI 1994–2004: A Summary of Findings: Implications for the Economy, Stock Returns andManagement. Sunil Mithas is an Assistant Professor at Robert H. Smith School of Business at University of Maryland. He has a PhD in Business from the Ross School of Business at the University of Michigan and an Engineering degree from Indian Institute of Technology, Roorkee, India. Prior to pursuing the PhD, he worked for about 10 years with the Tata group. His research focuses on strategic management and the impact of information technology resources and has appeared in journals that includeManagement Science, Information Systems Research, MIS Quarterly,Marketing Science, Journal of Marketing, and Production and Operations Management. His articles have won best article awards and best article nominations and have been featured in practice-oriented publications such as Harvard Business Review, Sloan Management Review, Bloomberg, Computerworld, and InformationWeek. Address correspondence to the author at morgeson@theacsi.org. doi:10.1093/jopart/muq006 a The Author 2010. Published by Oxford University Press on behalf of the Journal of Public Administration Research and Theory, Inc. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org Journal of Public Administration Research and Theory Advance Access published April 26, 2010 by on A ril 7, 2010 http://jpaordjournals.org D ow nladed fom may help improve citizens’ confidence in the future performance of the agency experienced, it does not yet lead to greater satisfaction with an agency interaction nor does it correlate with greater generalized trust in the federal government overall. Explanations for these findings, including an assessment of the potential of e-government to help rebuild trust in government in the future, are offered.

Death Due to Decapitation in Two Motorcyclists: A Description of a Unique Case and a Brief Review of the Literature. <=SEP=> Deaths due to decapitation, especially related to traffic accidents, are rarely found in forensic practice. The present case involves a man and a woman who died from decapitation due to an unusual mechanism while they were riding on a motorbike down a mountain road. The autopsy, which was completed as a physics study, allowed the accident to be reconstructed as follows: A plastic cable that had detached from a timber-transporting machine whipped the road and hit the two motorcyclists. The impact resulted in the complete severing of both riders' heads. Involving different scientists in this accident investigation was crucial to understanding the dynamics of the accident. In fact, in addition to scene inspection and autopsy, a physics study was carried out on the cable and the clamp involved, which led to an explanation for the abrupt movement of the cable and, thus, to a thorough reconstruction of the accident.

Revisiting Requirements Elicitation Techniques <=SEP=> The importance of Requirements Engineering (RE) has been well recognized by the research community in the last decade. There is no doubt to say that requirements phase is the foundation of the entire Software Development Life Cycle (SDLC) on which the entire software is built. With the proper management of various activities falling under requirements phase, a project can deliver the right solution within the time and budget. Requirement elicitation, requirement specification, and requirement validation are the important stages to assure the quality of requirement documentations. Out of these, elicitation is the first major activity, which requires proper attention by Requirement Engineers and other related stakeholders. Literature reveals various elicitation techniques, which can be used for the purpose depending upon the nature of project/s. In this paper, an attempt is made to cover all the major elicitation techniques along with their significant aspects at one place. Such a review would enable the concerned stakeholders to understand and select the most appropriate technique to be used for their project/s.

Game Development for Computer Science Education <=SEP=> Educators have long used digital games as platforms for teaching. Games tend to have several qualities that aren't typically found in homework: they often situate problems within a compelling alternate reality that unfolds through intriguing narrative, they often draw more upon a player's intrinsic motivations than extrinsic ones, they can facilitate deliberate low intensity practice, and they often emphasize a spirit of play instead of work.
 At ITiCSE 2016, this working group convened to survey the landscape of existing digital games that have been used to teach and learn computer science concepts. Our group discovered that these games lacked explicitly defined learning goals and even less evaluation of whether or not the games achieved these goals. As part of this process, we identified and played over 120 games that have been released or described in literature as means for learning computer science concepts. In our report, we classified how these games support the learning objectives outlined in the ACM/IEEE Computer Science Curricula 2013.
 While we found more games than we expected, few games explicitly stated their learning goals and even fewer were evaluated for their capacity to meet these goals. Most of the games we surveyed fell into two categories: short-lived proof-of-concept projects built by academics or closed-source games built by professional developers. Gathering adequate learning data is challenging in either situation. Our original intent for the second year of our working group was to prepare a comprehensive framework for collecting and analyzing learning data from computer science learning games. Upon further discussion, however, we decided that a better next step is to validate the design and development guidelines that we put forth in our final report for ITiCSE 2016.
 We extend this working group to a second year---with a mission to collaboratively develop a game with clearly defined learning objectives and define a methodology for evaluating its capacity to meet its goals.

Video Gaming and Children’s Psychosocial Wellbeing: A Longitudinal Study <=SEP=> The effects of video games on children's psychosocial development remain the focus of debate. At two timepoints, 1 year apart, 194 children (7.27-11.43 years old; male = 98) reported their gaming frequency, and their tendencies to play violent video games, and to game (a) cooperatively and (b) competitively; likewise, parents reported their children's psychosocial health. Gaming at time one was associated with increases in emotion problems. Violent gaming was not associated with psychosocial changes. Cooperative gaming was not associated with changes in prosocial behavior. Finally, competitive gaming was associated with decreases in prosocial behavior, but only among children who played video games with high frequency. Thus, gaming frequency was related to increases in internalizing but not externalizing, attention, or peer problems, violent gaming was not associated with increases in externalizing problems, and for children playing approximately 8 h or more per week, frequent competitive gaming may be a risk factor for decreasing prosocial behavior. We argue that replication is needed and that future research should better distinguish between different forms of gaming for more nuanced and generalizable insight.

Classification of Substitution Ciphers using Neural Networks <=SEP=> Most of the time of a cryptanalyst is spent on finding the cipher technique used for encryption rather than the finding the key/ plaintext of the received cipher text. The Strength of the classical substitution cipher‟s success lie on the variety of characters employed to represent a single character. More, the characters employed more the complexity. Thus, in order to reduce the work of the cryptanalyst, neural network based identification is done based on the features of the cipher methods. In this paper, classical substitution ciphers, namely, Playfair, Vigenère and Hill ciphers are considered. The features of the cipher methods under consideration were extracted and a backpropagation neural network was trained. The network was tested for random texts with random keys of various lengths. The cipher text size was fixed as 1Kb. The results so obtained were encouraging.

Efficient Verification of Holograms Using Mobile Augmented Reality <=SEP=> Paper documents such as passports, visas and banknotes are frequently checked by inspection of security elements. In particular, optically variable devices such as holograms are important, but difficult to inspect. Augmented Reality can provide all relevant information on standard mobile devices. However, hologram verification on mobiles still takes long and provides lower accuracy than inspection by human individuals using appropriate reference information. We aim to address these drawbacks by automatic matching combined with a special parametrization of an efficient goal-oriented user interface which supports constrained navigation. We first evaluate a series of similarity measures for matching hologram patches to provide a sound basis for automatic decisions. Then a re-parametrized user interface is proposed based on observations of typical user behavior during document capture. These measures help to reduce capture time to approximately 15 s with better decisions regarding the evaluated samples than what can be achieved by untrained users.

Web Spam Taxonomy <=SEP=> Web spamming refers to actions intended to mislead search engines into ranking some pages higher than they deserve. Recently, the amount of web spam has increased dramatically, leading to a degradation of search results. This paper presents a comprehensive taxonomy of current spamming techniques, which we believe can help in developing appropriate countermeasures.

Deep code comment generation <=SEP=> During software maintenance, code comments help developers comprehend programs and reduce additional time spent on reading and navigating source code. Unfortunately, these comments are often mismatched, missing or outdated in the software projects. Developers have to infer the functionality from the source code. This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generated comments aim to help developers understand the functionality of Java methods. DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features. We use a deep neural network that analyzes structural information of Java methods for better comments generation. We conduct experiments on a large-scale Java corpus built from 9,714 open source projects from GitHub. We evaluate the experimental results on a machine translation metric. Experimental results demonstrate that our method DeepCom outperforms the state-of-the-art by a substantial margin.

Rethinking Feelings: An fMRI Study of the Cognitive Regulation of Emotion <=SEP=> The ability to cognitively regulate emotional responses to aversive events is important for mental and physical health. Little is known, however, about neural bases of the cognitive control of emotion. The present study employed functional magnetic resonance imaging to examine the neural systems used to reappraise highly negative scenes in unemotional terms. Reappraisal of highly negative scenes reduced subjective experience of negative affect. Neural correlates of reappraisal were increased activation of the lateral and medial prefrontal regions and decreased activation of the amygdala and medial orbito-frontal cortex. These findings support the hypothesis that prefrontal cortex is involved in constructing reappraisal strategies that can modulate activity in multiple emotion-processing systems.

Metacognitive Control of the Spacing of Study Repetitions <=SEP=> Rememberers play an active role in learning, not only by committing material more or less faithfully to memory, but also by selecting judicious study strategies (or not). In three experiments, subjects chose whether to mass or space the second presentation of to-be-learned paired-associate terms that were either normatively difficult or easy to remember, under the constraint that subjects needed to space exactly half of the items (and mass the other half). In contrast with recent findings that implemented no such constraint (Son, 2004), subjects chose to space more of the difficult pairs (in Experiments 1 and 2). Reduction in exposure time eliminated but did not reverse this effect (Experiment 3). Subjects who spaced more of the difficult pairs were more likely to exhibit superior memory performance, but, because subjects who made spacing selections that had no effect on the actual scheduling of items also showed this effect (Experiment 2), that enhancement in performance is more likely to reflect subject selection than strategy efficacy. Overall, these results suggest that choice constraints strongly elicit a discrepancy-reduction approach (Dunlosky & Hertzog, 1998) to strategic decisionmaking, but that reduced study time can eliminate this effect. Metacognitive control of spacing 3 The question of how people use their metacognitive knowledge to regulate their behaviors has been of much interest in recent years, particularly with regard to the implementation of study strategies. Metacognition plays an integral role in tasks such as selfdirected learning (Koriat & Goldsmith, 1996; Nelson & Narens, 1990), and understanding the means by which metacognitions guide learning processes is essential to facilitate and optimize the learning process itself (see Bjork, 1994). This article pursues that question by examining the strategies that subjects employ in the scheduling of learning events. Recent evidence revealed conditions under which subjects prefer to space easier materials and mass more difficult ones (Son, 2004). That result is fascinating because it either reveals that subjects choose to apply more effective study conditions to easier materials—a result in conflict with the vast majority of findings from study-time allocation experiments—or it reveals a fundamental misappreciation of the greater effectiveness of spacing in promoting learning (e.g., Baddeley & Longman, 1978). However, the present experiments reveal the opposite effect—subjects choose to space difficult and mass easy items. These results thus suggest that, under some conditions, subjects do understand the beneficial effects of spacing and also choose to selectively utilize them with difficult materials. Self-regulation of learning Theories of self-regulated study claim that active learners use assessments of item difficulty and their own degree of learning in deciding whether to allocate further cognitive resources toward study of that item or to move on to other items (e.g. Dunlosky & Hertzog, 1998; Mazzoni, Cornoldi, & Marchitelli, 1990; Metcalfe, 2002; Nelson & Leonesio, 1988; Nelson & Narens, 1990; Thiede & Dunlosky, 1999). There is some debate, however, with regard to how difficulty and resource allocation – specifically, study time allotment – are related. Metacognitive control of spacing 4 Discrepancy reduction. One theory emphasizes a discrepancy-reduction mechanism (Dunlosky & Hertzog, 1998). According to this theory, the learner compares their perceived degree of learning of a to-be-learned item to the their desired level of mastery for that item, also known as the norm of study (Le Ny, Denhiere, & Le Taillanter, 1972; see also Nelson & Narens, 1990), and if the degree of learning does not reach that criterion, additional study of that item ensues. Therefore, in reducing the discrepancy between an item’s current and desired degree of learning, the model predicts an inverse relationship between the perceived (prior) degree of learning and study time, and hence suggests that people will allot more study time to judgeddifficult than judged-easy items (Dunlosky & Hertzog, 1998; see also Thiede & Dunlosky, 1999). Indeed, a multitude of experiments have shown that people tend to study more difficult items for longer than they study easier items. In a comprehensive review, Son and Metcalfe (2000) reported that, of 46 treatment combinations in 19 published experiments in which subjects controlled the allocation of study time, 35 revealed a strategy of devoting more time to difficult items, and none showed the opposite strategy of devoting more time to easy items. These studies included subjective as well as objective measures of difficulty, and the results were consistently found across age groups and study materials. Proximal learning. However, Son and Metcalfe (2000) showed that total time constraints caused subjects to apportion more study time to judged-easy items than to judged-difficult items. That is, when the total study time allotted was likely insufficient to master all items, subjects chose to allocate their limited time to items that individually take less time to master, rather than slowly learn fewer difficult items. Similarly, Thiede and Dunlosky (1999) found that if their task Metacognitive control of spacing 5 was to remember only a small portion of the to-be-learned items, rather than the full set, subjects devoted more study time to easy items. Metcalfe (2002) surmised that the discrepancy-reduction model adequately accounted for subjects’ allocation strategies only under certain conditions, and forwarded a more comprehensive model to incorporate the newer data. She argued that study time should be devoted to those items that are just beyond the current grasp of the individual, in the region of proximal learning (Metcalfe, 2002; see also Metcalfe & Kornell, 2003). In the case that those just-unlearned items are the most difficult to-be-learned items, the discrepancy-reduction and region of proximal learning models agree on what the appropriate strategy should be. However, in cases where easy items are still unlearned, the predictions of the two theories are in opposition. Whereas the discrepancy-reduction model suggests that learners will always devote more time to the difficult items, the proximal learning hypothesis implies that individual differences in expertise within a domain should influence study-time allocation. Metcalfe (2002) demonstrated this effect using English-Spanish vocabulary pairs. To monolingual speakers, even relatively easy items can be difficult to learn, and thus those speakers allocated more study time to those easy pairs accordingly. Experts, on the other hand, spent more time studying the difficult word pairs, and Metcalfe (2002) attributed the group differences in item selection to the difference between the two groups’ regions of proximal learning. Novices chose to spend more time studying the easy, yet still unlearned, items before moving on to more difficult items, a result that is not predicted by the discrepancy-reduction model. Effects of strategy choice. In addition to identifying the strategies used in allocating study time, determining which strategy ultimately leads to superior performance on subsequent Metacognitive control of spacing 6 recall tests is of importance as well. The actual quality of any study strategy, after all, can only be evaluated by the outcome it produces on the subsequent test. As Son and Metcalfe (2000) pointed out, even though much of the previous literature suggests a tendency for subjects to study the difficult items for longer than the easy items, there are no data showing that subjects who employ such a strategy outperform subjects who spend equal amounts of time on easy and difficult items. While it is intuitive that increased duration of study should lead to higher recall performance for any given item, previous findings have suggested that once study time for an item has been sufficient for initial acquisition, continued immediate study of that item leads to little or no increase in the probability of future recall. This null increase in performance despite substantial increases in study time has been termed the “labor-in-vain effect” (Nelson & Leonesio, 1988, p. 680). Metcalfe and Kornell (2003) systematically investigated the effects of allocating study time to a particular subset of items on later recall performance. Their results showed that allocating study time to items of medium difficulty was more helpful than was allocating study time to easy or difficult items, presumably because learning of the easy items had already plateaued and would not benefit from additional study, whereas learning of the difficult items would require even larger amounts of additional study time to reach a level sufficient to improve later recall. The plateau occurs sooner for easy items than for medium items, and likewise sooner for medium items than for difficult items. If an optimal strategy of study-time allocation is one in which study of a particular item should be discontinued at the point when further study is not as advantageous as study of another item, a critical question for the metacognizer is where (or when) that point is. If information uptake has slowed sufficiently for an item, immediate further study of that item at the cost of a Metacognitive control of spacing 7 different item may be detrimental to overall recall performance, whereas further study at some later time may boost one’s memory for that item and may benefit future recall. Such a strategy has already been shown in the context of semantic retrieval tasks in which subjects tried to name as many items as possible from two semantic categories in a short time. When retrieval for items in one category slowed, and the other category became relatively more appealing, subjects switched to the second category (Young, 2004). To test whether subjects employ this type of strategy in a learning task, a pa

Undefined By Data: A Survey of Big Data Definitions <=SEP=> The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term.

Explaining the Poor Performance of Consumption-Based Asset Pricing Models <=SEP=> The poor performance of consumption-based asset pricing models relative to traditional portfoliobased asset pricing models is one of the great disappointments of the empirical asset pricing literature. We show that the external habit-formation model economy of Campbell and Cochrane (1999) can explain this puzzle. Though artificial data from that economy conform to a consumption-based model by construction, the CAPM and its extensions are much better approximate models than is the standard power utility specification of the consumption-based model. Conditioning information is the central reason for this result. The model economy has one shock, so when returns are measured at sufficiently high frequency the consumption-based model and the CAPM are equivalent and perfect conditional asset pricing models. However, the model economy also produces time-varying expected returns, tracked by the dividend-price ratio. Portfolio-based models capture some of this variation in state variables, which a state-independent function of consumption cannot capture, and so portfolio-based models are better approximate unconditional asset pricing models. John Y. Campbell John H. Cochrane Department of Economics Graduate School of Business Harvard University University of Chicago Littauer Center 213 1101 E. 58th. St. Cambridge, MA 02138 Chicago, IL 60637 and NBER and NBER john_campbell@harvard.edu john.cochrane@gsb.uchicago.edu

Assessing Organizational Capabilities: Reviewing and Guiding the Development of Maturity Grids <=SEP=> Managing and improving organizational capabilities is a significant and complex issue for many companies. To support management and enable improvement, performance assessments are commonly used. One way of assessing organizational capabilities is by means of maturity grids. While maturity grids may share a common structure, their content differs and very often they are developed anew. This paper presents both a reference point and guidance for developing maturity grids. This is achieved by reviewing 24 existing maturity grids and by suggesting a roadmap for their development. The review places particular emphasis on embedded assumptions about organizational change in the formulation of the maturity ratings. The suggested roadmap encompasses four phases: planning, development, evaluation, and maintenance. Each phase discusses a number of decision points for development, such as the selection of process areas, maturity levels, and the delivery mechanism. An example demonstrating the roadmap's utility in industrial practice is provided. The roadmap can also be used to evaluate existing approaches. In concluding the paper, implications for management practice and research are presented.

Modelling out-of-vocabulary words for robust speech recognition <=SEP=> This thesis concerns the problem of unknown or out-of-vocabulary (OOV) words in continuous speech recognition. Most of today's state-of-the-art speech recognition systems can recognize only words that belong to some predefined finite word vocabulary. When encountering an OOV word, a speech recognizer erroneously substitutes the OOV word with a similarly sounding word from its vocabulary. Furthermore, a recognition error due to an OOV word tends to spread errors into neighboring words; dramatically degrading overall recognition performance. In this thesis we propose a novel approach for handling OOV words within a single-stage recognition framework. To achieve this goal, an explicit and detailed model of OOV words is constructed and then used to augment the closed-vocabulary search space of a standard speech recognizer. This OOV model achieves open-vocabulary recognition through the use of more flexible subword units that can be concatenated during recognition to form new phone sequences corresponding to potential new words. Examples of such subword units are phones, syllables, or some automatically-learned multi-phone sequences. Subword units have the attractive property of being a closed set, and thus are able to cover any new words, and can conceivably cover most utterances with partially spoken words as well. The main challenge with such an approach is ensuring that the OOV model does not absorb portions of the speech signal corresponding to in-vocabulary (IV) words. In dealing with this challenge, we explore several research issues related to designing the subword lexicon, language model, and topology of the OOV model. We present a dictionary-based approach for estimating subword language models. Such language models are utilized within the subword search space to help recognize the underlying phonetic transcription of OOV words. We also propose a data-driven iterative bottom-up procedure for automatically creating a multi-phone subword inventory. Starting with individual phones, this procedure uses the maximum mutual information principle to successively merge phones to obtain longer subword units. The thesis also extends this OOV approach to modelling multiple classes of OOV words. Instead of augmenting the word search space with a single model, we add several models, one for each class of words. We present two approaches for designing the OOV word classes. The first approach relies on using common part-of-speech tags. The second approach is a data-driven two-step clustering procedure, where the first step uses agglomerative clustering to derive an initial class assignment, while the second step uses iterative clustering to move words from one class to another in order to reduce the model perplexity.

A Digital Input Controller for Audio Class-D Amplifiers with 100W 0.004% THD+N and 113dB DR <=SEP=> A digital input controller for audio class-D amplifiers is presented. The controller utilizes specially configured integrated DAC and power stage feedback loop to suppress distortion components coming from power-stage switching with digital input capability. The class-D amplifier system with the controller and an existing power stage achieves 113dB DR, 0.0018% THD+N with 10W output power, and 0.004% THD+N with 100W output power into 4Omega load

Understanding Latency Variation in Modern DRAM Chips: Experimental Characterization, Analysis, and Optimization <=SEP=> Long DRAM latency is a critical performance bottleneck in current systems. DRAM access latency is defined by three fundamental operations that take place within the DRAM cell array: (i) activation of a memory row, which opens the row to perform accesses; (ii) precharge, which prepares the cell array for the next memory access; and (iii) restoration of the row, which restores the values of cells in the row that were destroyed due to activation. There is significant latency variation for each of these operations across the cells of a single DRAM chip due to irregularity in the manufacturing process. As a result, some cells are inherently faster to access, while others are inherently slower. Unfortunately, existing systems do not exploit this variation. 
 The goal of this work is to (i) experimentally characterize and understand the latency variation across cells within a DRAM chip for these three fundamental DRAM operations, and (ii) develop new mechanisms that exploit our understanding of the latency variation to reliably improve performance. To this end, we comprehensively characterize 240 DRAM chips from three major vendors, and make several new observations about latency variation within DRAM. We find that (i) there is large latency variation across the cells for each of the three operations; (ii) variation characteristics exhibit significant spatial locality: slower cells are clustered in certain regions of a DRAM chip; and (iii) the three fundamental operations exhibit different reliability characteristics when the latency of each operation is reduced. 
 Based on our observations, we propose Flexible-LatencY DRAM (FLY-DRAM), a mechanism that exploits latency variation across DRAM cells within a DRAM chip to improve system performance. The key idea of FLY-DRAM is to exploit the spatial locality of slower cells within DRAM, and access the faster DRAM regions with reduced latencies for the fundamental operations. Our evaluations show that FLY-DRAM improves the performance of a wide range of applications by 13.3%, 17.6%, and 19.5%, on average, for each of the three different vendors' real DRAM chips, in a simulated 8-core system. We conclude that the experimental characterization and analysis of latency variation within modern DRAM, provided by this work, can lead to new techniques that improve DRAM and system performance.

Autoimmune disorders: nail signs and therapeutic approaches. <=SEP=> Systemic sclerosis (scleroderma, SSc) is an autoimmune disease that targets small and medium-sized arteries and arterioles in the involved tissues, resulting in a fibrotic vasculopathy and tissue fibrosis. Several prominent nail and periungual changes are apparent in scleroderma. Examination of the nail fold capillaries can reveal the nature and extent of microvascular pathology in patients with collagen vascular disease and Raynaud's phenomenon. Among the complications stemming from Raynaud's phenomenon can be painful ischemic digital ulcers. This can be managed, and potentially prevented, through pharmacologic and nonpharmacologic means. Whereas oral calcium channel blockers remain the most convenient therapy, oral endothelin receptor antagonists and intravenous prostaglandins may be important therapeutic advances for ischemic digital vascular lesions.