Query: Learning Graphical Model Structure Using L1-Regularization Paths

Relevant passages:
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks <=SEP=> In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent dependency structures using Bayesian network models. To analyze a given data set, Bayesian model selection attempts to find the most likely (MAP) model, and uses its structure to answer these questions. However, when the amount of available data is modest, there might be many models that have non-negligible posterior. Thus, we want compute the Bayesian posterior of a feature, i.e., the total posterior probability of all models that contain it. In this paper, we propose a new approach for this task. We first show how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables. This allows us to compute, for a given order, both the marginal probability of the data and the posterior of a feature. We then use this result as the basis for an algorithm that approximates the Bayesian posterior of a feature. Our approach uses a Markov Chain Monte Carlo (MCMC) method, but over orders rather than over network structures. The space of orders is smaller and more regular than the space of structures, and has much a smoother posterior “landscape”. We present empirical results on synthetic and real-life datasets that compare our approach to full model averaging (when possible), to MCMC over network structures, and to a non-Bayesian bootstrap approach.

Regression Shrinkage and Selection via the Lasso <=SEP=> We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients hat are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.

The max-min hill-climbing Bayesian network structure learning algorithm <=SEP=> We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.

Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks <=SEP=> One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure from data. The BN-learning problem is NPhard, so the standard solution is heuristic search. Many approaches have been proposed for this task, but only a very small number outperform the baseline of greedy hill-climbing with tabu lists; moreover, many of the proposed algorithms are quite complex and hard to implement. In this paper, we propose a very simple and easy-toimplement method for addressing this task. Our approach is based on the well-known fact that the best network (of bounded in-degree) consistent with a given node ordering can be found very efficiently. We therefore propose a search not over the space of structures, but over the space of orderings, selecting for each ordering the best network consistent with it. This search space is much smaller, makes more global search steps, has a lower branching factor, and avoids costly acyclicity checks. We present results for this algorithm on both synthetic and real data sets, evaluating both the score of the network found and in the running time. We show that orderingbased search outperforms the standard baseline, and is competitive with recent algorithms that are much harder to implement.

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data <=SEP=> 

Game private networks performance: analytical models for very-large scale simulation <=SEP=> The WTFast's Gamers Private Network (GPN®) is a client/server solution that makes online games faster. GPN® connects online video-game players with a common game service across a wide-area network. Online games are interactive competitions by individual players who compete in a virtual environment. Response time, latency and its predictability are keys to GPN® success and runs against the vast complexity of internet-wide systems. We have built an experimental network of virtualized GPN® components so as to carefully measure the statistics of latency for distributed Minecraft games and to do so in a controlled laboratory environment. This has led to a better understanding of the coupling between parameters such as: the number of players, the subset of players that are idle or active, the volume of packets exchanged, the size of packets, latency to and from the game servers, and time-series for most of those parameters. In this paper we present a mathematical model of those system game network parameters and show how it leads to: (1) realistic simulation of each of those network or game parameters, without relying on the experimental setup; (2) very large-scale numerical simulation of the game setup so as to explore various internet-wide performance scenarios that: (a) are impossible to isolate from internet “noise” in their real environment and; (b) would require vast supercomputing resources if they were to be simulated exhaustively. We motivate all elements of our mathematical model and estimate the savings in computational costs they will bring for very large-scale simulation of the GPN®. Such simulations will improve quality of service for GPN® systems and their reliability.

Recursive circle packing problems <=SEP=> This paper presents a class of packing problems where circles may be placed either inside or outside other circles, the whole set being packed in a rectangle. This corresponds to a practical problem of packing tubes in a container; before being inserted in the container, tubes may be put inside other tubes in a recursive fashion. A variant of the greedy randomized adaptive search procedure is proposed for tackling this problem, and its performance assessed in a set of benchmark instances.

A Hybrid Fragmentation Approach for Distributed Deductive Database Systems <=SEP=> Fragmentation of base relations in distributed database management systems increases the level of concurrency and therefore system throughput for query processing. Algorithms for horizontal and vertical fragmentation of relations in relational, object-oriented and deductive databases exist; however, hybrid fragmentation techniques based on variable bindings appearing in user queries and query-access-rule dependency are lacking for deductive database systems. In this paper, we propose a hybrid fragmentation approach for distributed deductive database systems. Our approach first considers the horizontal partition of base relations according to the bindings imposed on user queries, and then generates vertical fragments of the horizontally partitioned relations and clusters rules using affinity of attributes and access frequency of queries and rules. The proposed fragmentation technique facilitates the design of distributed deductive database systems.

Risks and benefits of social media for children and adolescents. <=SEP=> Resources • AAP Internet safety resources site, http:// safetynet.aap.org • CDC Social Media Tools Guidelines & Best Practices site, http://www.cdc.gov/ SocialMedia/Tools/ guidelines/ • Common Sense Media (2009). Is technology changing childhood? A national poll on teens and social networking. R e t r i e v e d f r om http://www.commonsensemedia.org/teensocial-media. FACEBOOK, TWITTER, AND YouTube bring benefits to children and teenagers, including enhancing communication, broadening social connections, and learning technical skills, but can also expose them to risks, such as cyberbullying, “Facebook depression,” and “sexting,” according to a new report by the American Academy of Pediatrics (AAP; O'Keeffe, Clarke-Pearson, & Council on Communications and Media, 2011). The new report outlines the latest research on one of the most common activities among today's children and teenagers and describes how health care providers can help families understand these sites and encourage their healthy use. The report considers any site that allows social interaction as a social media site, including social networking sites such as Facebook, MySpace, and Twitter; gaming sites and virtual worlds such as Club Penguin, Second Life, and the Sims; video sites such as YouTube; and blogs. The abundance of these sites has grown exponentially in recent years. According to a poll cited in the report, more than half of American teenagers log onto their favorite social media site at least once a day, whereas 22% do so at least 10 times a day; 75% of teenagers now own cell phones, with 54% of them using them for texting, 24% for instant messaging, and 25% for social media access. According to the authors of the new report, increase in social media has been so rapid and their presence in children's everyday life is now so pervasive that, for some teens, social media is the primary way they interact socially, and a large part of this generation's social and emotional development is occurring while on the Internet and on cell phones. Because of their limited capacity for self-regulation and susceptibility to peer pressure,

Raspberry Pi based interactive home automation system through E-mail <=SEP=> Home automation is becoming more and more popular day by day due to its numerous advantages. This can be achieved by local networking or by remote control. This paper aims at designing a basic home automation application on Raspberry Pi through reading the subject of E-mail and the algorithm for the same has been developed in python environment which is the default programming environment provided by Raspberry Pi. Results show the efficient implementation of proposed algorithm for home automation. LEDs were used to indicate the switching action.

On the privacy-utility tradeoff in participatory sensing systems <=SEP=> The ubiquity of sensors-equipped mobile devices has enabled citizens to contribute data via participatory sensing systems. This emergent paradigm comes with various applications to improve users' quality of life. However, the data collection process may compromise the participants' privacy when reporting data tagged or correlated with their sensitive information. Therefore, anonymization and location cloaking techniques have been designed to provide privacy protection, yet to some cost of data utility which is a major concern for queriers. Different from past works, we assess simultaneously the two competing goals of ensuring the queriers' required data utility and protecting the participants' privacy. First, we introduce a trust worthy entity to the participatory sensing traditional system. Also, we propose a general privacy-preserving mechanism that runs on this entity to release a distorted version of the sensed data in order to minimize the information leakage with its associated private information. We demonstrate how to identify a near-optimal solution to the privacy-utility tradeoff by maximizing a privacy score while considering a utility metric set by data queriers (service providers). Furthermore, we tackle the challenge of data with large size alphabets by investigating quantization techniques. Finally, we evaluate the proposed model on three different real datasets while varying the prior knowledge and the obfuscation type. The obtained results demonstrate that, for different applications, a limited distortion may ensure the participants' privacy while maintaining about 98% of the required data utility.

On the statistics of natural stochastic textures and their application in image processing <=SEP=> Statistics of natural images has become an important subject of research in recent years. The highly kurtotic, non-Gaussian, statistics known to be characteristic of many natural images are exploited in various image processing tasks. In this paper, we focus on natural stochastic textures (NST) and substantiate our finding that NST have Gaussian statistics. Using the well-known statistical self-similarity property of natural images, exhibited even more profoundly in NST, we exploit a Gaussian self-similar process known as the fractional Brownian motion, to derive a fBm-PDE-based singleimage superresolution scheme for textured images. Using the same process as a prior, we also apply it in denoising of NST.

JSpIRIT: a flexible tool for the analysis of code smells <=SEP=> Code smells are a popular mechanism to identify structural design problems in software systems. Since it is generally not feasible to fix all the smells arising in the code, some of them are often postponed by developers to be resolved in the future. One reason for this decision is that the improvement of the code structure, to achieve modifability goals, requires extra effort from developers. Therefore, they might not always spend this additional effort, particularly when they are focused on delivering customer-visible features. This postponement of code smells are seen as a source of technical debt. Furthermore, not all the code smells may be urgent to fix in the context of the system's modifability and business goals. While there are a number of tools to detect smells, they do not allow developers to discover the most urgent smells according to their goals. In this article, we present a fexible tool to prioritize technical debt in the form of code smells. The tool is flexible to allow developer s to add new smell detection strategies and to prioritize smells, and groups of smells, based on the confguration of their manifold criteria. To illustrate this flexibility, we present an application example of our tool. The results suggest that our tool can be easily extended to be aligned with the developer's goals.

Asymmetrical duty cycle permits zero switching loss in PWM circuits with no conduction loss penalty <=SEP=> Operating a bridge-type PWM (pulse width modulation) switch mode power converter with asymmetrical duty ratios can eliminate switching losses with no increase in conduction loss. This circuit topology combines the best features of resonant (zero switching loss) and switch mode (low conclusion loss) circuits. Design equations are presented for just such a circuit.<<ETX>>

Reproducibility of quantitative tractography methods applied to cerebral white matter <=SEP=> Tractography based on diffusion tensor imaging (DTI) allows visualization of white matter tracts. In this study, protocols to reconstruct eleven major white matter tracts are described. The protocols were refined by several iterations of intra- and inter-rater measurements and identification of sources of variability. Reproducibility of the established protocols was then tested by raters who did not have previous experience in tractography. The protocols were applied to a DTI database of adult normal subjects to study size, fractional anisotropy (FA), and T2 of individual white matter tracts. Distinctive features in FA and T2 were found for the corticospinal tract and callosal fibers. Hemispheric asymmetry was observed for the size of white matter tracts projecting to the temporal lobe. This protocol provides guidelines for reproducible DTI-based tract-specific quantification.

Threshold-Dependent Camouflaged Cells to Secure Circuits Against Reverse Engineering Attacks <=SEP=> With current tools and technology, someone who has physical access to a chip can extract the detailed layout of the integrated circuit (IC). By using advanced visual imaging techniques, reverse engineering can reveal details that are meant to be kept secret, such as a secure protocol or novel implementation that offers a competitive advantage. A promising solution to defend against reverse engineering attacks is IC camouflaging. In this work, we propose a new camouflaging technique based on the threshold voltage of the transistors. We refer to these cells as threshold dependent camouflaged cells. Our work differs from current commercial solutions in that the latter use look-alike cells, with the assumption that it is difficult for the reverse engineer to identify the cell's functionality. Yet, if a structural distinction between cells exists, then these are still vulnerable, especially as reverse engineers use more advanced and precise techniques. On the other hand, the proposed threshold dependent standard cells are structurally identical regardless of the cells' functionality. Detailed circuit simulations of our proposed threshold dependent camouflaged cells demonstrate that they can be used to cost-effectively and robustly camouflage large netlists. Corner analysis of process, temperature, and supply voltage (PVT) variations show that our cells operate as expected over all PVT corners simulated.

Yacc is dead <=SEP=> We present two novel approaches to parsing context-free languages. The first approach is based on an extension of Brzozowski’s derivative from regular expressions to context-free grammars. The second approach is based on a generalization of the derivative to parser combinators. The payoff of these techniques is a small (less than 250 lines of code), easy-to-implement parsing library capable of parsing arbitrary context-free grammars into lazy parse forests. Implementations for both Scala and Haskell are provided. Preliminary experiments with S-Expressions parsed millions of tokens per second, which suggests this technique is efficient enough for use in practice. 1 Top-down motivation: End cargo cult parsing “Cargo cult parsing” is a plague upon computing. Cargo cult parsing refers to the use of “magic” regular expressions—often cut and pasted directly from Google search results—to parse languages which ought to be parsed with contextfree grammars. Such parsing has two outcomes. In the first case, the programmer produces a parser that works “most of the time” because the underlying language is fundamentally irregular. In the second case, some domain-specific language ends up with a mulish syntax, because the programmer has squeezed the language through the regular bottleneck. There are two reasons why regular expressions are so abused while context-free languages remain foresaken: (1) regular expression libraries are available in almost every language, while parsing libraries and toolkits are not, and (2) regular expressions are “WYSIWYG”—the language described is the language that gets matched—whereas parser-generators are WYSIWYGIYULR(k)—“what you see is what you get if you understand LR(k).” To end cargo-cult parsing, we need a new approach to parsing that: 1. handles arbitrary context-free grammars; 2. parses efficiently on average; and 3. can be implemented as a library with little effort. The end goals of the three conditions are simplicity, feasibility and ubiquity. The “arbitrary context-free grammar” condition is necessary because programmers will be less inclined to use a tool that forces them to learn or think about LL/LR arcana. It is hard for compiler experts to imagine, but the constraints on LL/LR 1 The term “cargo cult parsing” is due to Larry Wall, 19 June 2008, Google Tech Talk. grammars are (far) beyond the grasp of the average programmer. [Of course, arbitrary context-free grammars bring ambiguity, which means the parser must be prepared to return a parse forest rather than a parse tree.] The “efficient parsing” condition is necessary because programmers will avoid tools branded as inefficient (however justly or unjustly this label has been applied). Specifically, a parser needs to have roughly linear behavior on average. Because ambiguous grammars may yield an exponential number of parse trees, parse trees must be produced lazily, and each parse tree should be paid for only if it is actually produced. The “easily implemented” condition is perhaps most critical. It must be the case that a programmer could construct a general parsing toolkit if their language of choice doesn’t yet have one. If this condition is met, it is reasonable to expect that proper parsing toolkits will eventually be available for every language.When proper parsing toolkits and libraries remain unavailable for a language, cargo cult parsing prevails. This work introduces parsers based on the derivative of context-free languages and upon the derivative of parser combinators. Parsers based on derivatives meet all of the aforementioned requirements: they accept arbitrary grammars, they produce parse forests efficiently (and lazily), and they are easy to implement (less than 250 lines of Scala code for the complete library). Derivative-based parsers also avoid the precompilation overhead of traditional parser generators; this cost is amortized (and memoised) across the parse itself. In addition, derivative-based parsers can be modified mid-parse, which makes it conceivable that a language could to modify its own syntax at compileor run-time. 2 Bottom-up motivation: Generalizing the derivative Brzozowski defined the derivative of regular expressions in 1964 [1]. This technique was lost to the “sands of time” until Owens, Reppy and Turon recently revived it to show that derivative-based lexer-generators were easier to write, more efficient and more flexible than the classical regex-to-NFA-to-DFA generators [15]. (Derivative-based lexers allow, for instance, both complement and intersection.) Given the payoff for regular languages, it is natural to ask whether the derivative, and its benefits, extend to context-free languages, and transitively, to parsing. As it turns out, they do. We will show that context-free languages are closed under the derivative—they critical property needed for parsing. We will then show that context-free parser combinators are also closed under a generalization of the derivative. The net impact is that we will be able to write a derivative-based parser combinator library in under 250 lines of Scala, capable of producing a lazy parse forest for any context-free grammar. 2 The second author on this paper, an undergraduate student, completed the implementation for Haskell in less than a week.

Sketches by Paul the Robot <=SEP=> In this paper we describe Paul, a robotic installation that produces observational sketches of people. The sketches produced have been considered of interest by fine art professionals in recent art fairs and exhibitions, as well as by the public at large. We identify factors that may account for the perceived qualities of the produced sketches. A technical overview of the system is also presented.

A Hierarchical Neural Model for Learning Sequences of Dialogue Acts <=SEP=> We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs). The input in this task is a sequence of utterances (i.e., conversational contributions) comprising a sequence of tokens, and the output is a sequence of DA labels (one label per utterance). Our model leverages the hierarchical nature of dialogue data by using two nested RNNs that capture long-range dependencies at the dialogue level and the utterance level. This model is combined with an attention mechanism that focuses on salient tokens in utterances. Our experimental results show that our model outperforms strong baselines on two popular datasets, Switchboard and MapTask; and our detailed empirical analysis highlights the impact of each aspect of our model.

Delta-Sigma FDC Based Fractional-N PLLs <=SEP=> Fractional-N phase-locked loop frequency synthesizers based on time-to-digital converters (TDC-PLLs) have been proposed to reduce the area and linearity requirements of conventional PLLs based on delta-sigma modulation and charge pumps (ΔΣ-PLLs). Although TDC-PLLs with good performance have been demonstrated, TDC quantization noise has so far kept their phase noise and spurious tone performance below that of the best comparable ΔΣ-PLLs. An alternative approach is to use a delta-sigma frequency-to-digital converter (ΔΣ FDC) in place of a TDC to retain the benefits of TDC-PLLs and ΔΣ-PLLs. This paper proposes a practical ΔΣ FDC based PLL in which the quantization noise is equivalent to that of a ΔΣ-PLL. It presents a linearized model of the PLL, design criteria to avoid spurious tones in the ΔΣFDC quantization noise, and a design methodology for choosing the loop parameters in terms of standard PLL target specifications.

Distributed generation intelligent islanding detection using governor signal clustering <=SEP=> One of the major protection concerns with distribution networks comprising distributed generation is unintentional islanding phenomenon. Expert diagnosis system is needed to distinguish network cut off from normal occurrences. An important part of synchronous generator is automatic load-frequency controller (ALFC). In this paper, a new approach based on clustering of input signal to governor is introduced. Self-organizing map (SOM) neural network is used to identify and classify islanding and non-islanding phenomena. Simulation results show that input signal to governor has different characteristics concern with islanding conditions and other disturbances. In addition, the SOM is able to identify and classify phenomena satisfactorily. Using proposed method, islanding can be detected after 200 ms.

Function-Hiding Inner Product Encryption Is Practical <=SEP=> In a functional encryption scheme, secret keys are associated with functions and ciphertexts are associated with messages. Given a secret key for a function f , and a ciphertext for a message x, a decryptor learns f(x) and nothing else about x. Inner product encryption is a special case of functional encryption where both secret keys and ciphertext are associated with vectors. The combination of a secret key for a vector x and a ciphertext for a vector y reveal 〈x,y〉 and nothing more about y. An inner product encryption scheme is function-hiding if the keys and ciphertexts reveal no additional information about both x and y beyond their inner product. In the last few years, there has been a flurry of works on the construction of function-hiding inner product encryption, starting with the work of Bishop, Jain, and Kowalczyk (Asiacrypt 2015) to the more recent work of Tomida, Abe, and Okamoto (ISC 2016). In this work, we focus on the practical applications of this primitive. First, we show that the parameter sizes and the run-time complexity of the state-of-the-art construction can be further reduced by another factor of 2, though we compromise by proving security in the generic group model. We then show that function privacy enables a number of applications in biometric authentication, nearest-neighbor search on encrypted data, and single-key two-input functional encryption for functions over small message spaces. Finally, we evaluate the practicality of our encryption scheme by implementing our function-hiding inner product encryption scheme. Using our construction, encryption and decryption operations for vectors of length 50 complete in a tenth of a second in a standard desktop environment.

Characterising the Behaviour of IEEE 802.11 Broadcast Transmissions in Ad Hoc Wireless LANs <=SEP=> This paper evaluates the performance of the IEEE 802.11 broadcast traffic under both saturation and nonsaturation conditions. The evaluation highlights some important characteristics of IEEE 802.11 broadcast traffic as compared to corresponding unicast traffic. Moreover, it underlines the inaccuracy of the broadcast saturation model proposed by Ma and Chen due to the absence of backoff counter freeze process when channel is busy. Computer simulations are used to validate the accuracy of the new model and demonstrate the importance of capturing the freezing of backoff counter in the analytical study of IEEE 802.11 broadcast.

Brain development during childhood and adolescence: a longitudinal MRI study <=SEP=> Pediatric neuroimaging studies, up to now exclusively cross sectional, identify linear decreases in cortical gray matter and increases in white matter across ages 4 to 20. In this large-scale longitudinal pediatric neuroimaging study, we confirmed linear increases in white matter, but demonstrated nonlinear changes in cortical gray matter, with a preadolescent increase followed by a postadolescent decrease. These changes in cortical gray matter were regionally specific, with developmental curves for the frontal and parietal lobe peaking at about age 12 and for the temporal lobe at about age 16, whereas cortical gray matter continued to increase in the occipital lobe through age 20.

Risk Factors in Adolescence: The Case of Gambling, Videogame Playing, and the Internet <=SEP=> It has been noted that adolescents may be more susceptible to pathological gambling. Not only is it usually illegal, but it appears to be related to high levels of problem gambling and other delinquent activities such as illicit drug taking and alcohol abuse. This paper examines risk factors not only in adolescent gambling but also in videogame playing (which shares many similarities with gambling). There appear to be three main forms of adolescent gambling that have been widely researched. Adolescent gambling activities and general risk factors in adolescent gambling are provided. As well, the influence of technology on adolescents in the form of both videogames and the Internet are examined. It is argued that technologically advanced forms of gambling may be highly appealing to adolescents.

Automated Data Structure Generation: Refuting Common Wisdom <=SEP=> Common wisdom in the automated data structure generation community states that declarative techniques have better usability than imperative techniques, while imperative techniques have better performance. We show that this reasoning is fundamentally flawed: if we go to the declarative limit and employ constraint logic programming (CLP), the CLP data structure generation has orders of magnitude better performance than comparable imperative techniques. Conversely, we observe and argue that when it comes to realistically complex data structures and properties, the CLP specifications become more obscure, indirect, and difficult to implement and understand than their imperative counterparts. We empirically evaluate three competing generation techniques, CLP, Korat, and UDITA, to validate these observations on more complex and interesting data structures than any prior work in this area. We explain why these observations are true, and discuss possible techniques for attaining the best of both worlds.

Simple Bayesian Algorithms for Best Arm Identification <=SEP=> This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure and observes noisy signals of their quality with the goal of confidently identifying the best design after a small number of measurements. I propose three simple Bayesian algorithms for adaptively allocating measurement effort. One is Top-Two Probability sampling, which computes the two designs with the highest posterior probability of being optimal, and then randomizes to select among these two. One is a variant a top-two sampling which considers not only the probability a design is optimal, but the expected amount by which its quality exceeds that of other designs. The final algorithm is a modified version of Thompson sampling that is tailored for identifying the best design. I prove that these simple algorithms satisfy a strong optimality property. In a frequestist setting where the true quality of the designs is fixed, one hopes the posterior definitively identifies the optimal design, in the sense that that the posterior probability assigned to the event that some other design is optimal converges to zero as measurements are collected. I show that under the proposed algorithms this convergence occurs at an exponential rate, and the corresponding exponent is the best possible among all allocation rules.

Cloudlet-based just-in-time indexing of IoT video <=SEP=> As video cameras proliferate, the ability to scalably capture and search their data becomes important. Scalability is improved by performing video analytics on cloudlets at the edge of the Internet, and only shipping extracted index information and meta-data to the cloud. In this setting, we describe interactive data exploration (IDE), which refers to human-in-the-loop content-based retrospective search using predicates that may not have been part of any prior indexing. We also describe a new technique called just-in-time indexing (JITI) that improves response times in IDE.

Intrinsically Motivated Affordance Discovery and Modeling <=SEP=> In this chapter, we argue that a single intrinsic motivation function for affordance discovery can guide long-term learning in robot systems. To these ends, we provide a novel definition of “affordance” as the latent potential for the closed-loop control of environmental stimuli perceived by sensors. Specifically, the proposed intrinsic motivation function rewards the discovery of such control affordances. We will demonstrate how this function has been used by a humanoid robot to learn a number of general purpose control skills that address many different tasks. These skills, for example, include strategies for finding, grasping, and placing simple objects. We further show how this same intrinsic reward function is used to direct the robot to build stable models of when the environment affords these skills.

An affective computational model for machine consciousness <=SEP=> In the past, several models of consciousness have become popular and have led to the development of models for machine consciousness with varying degrees of success and challenges for simulation and implementations. Moreover, affective computing attributes that involve emotions, behavior and personality have not been the focus of models of consciousness as they lacked motivation for deployment in software applications and robots. The affective attributes are important factors for the future of machine consciousness with the rise of technologies that can assist humans. Personality and affection hence can give an additional flavor for the computational model of consciousness in humanoid robotics. Recent advances in areas of machine learning with a focus on deep learning can further help in developing aspects of machine consciousness in areas that can better replicate human sensory perceptions such as speech recognition and vision. With such advancements, one encounters further challenges in developing models that can synchronize different aspects of affective computing. In this paper, we review some existing models of consciousnesses and present an affective computational model that would enable the human touch and feel for robotic systems.