Glimmers: Resolving the Privacy/Trust Quagmire

Many successful services rely on trustworthy contributions from users. To establish that trust, such services often require access to privacy-sensitive information from users, thus creating a conflict between privacy and trust. Although it is likely impractical to expect both absolute privacy and trustworthiness at the same time, we argue that the current state of things, where individual privacy is usually sacrificed at the altar of trustworthy services, can be improved with a pragmatic $Glimmer$ $of$ $Trust$, which allows services to validate user contributions in a trustworthy way without forfeiting user privacy. We describe how trustworthy hardware such as Intel's SGX can be used client-side -- in contrast to much recent work exploring SGX in cloud services -- to realize the Glimmer architecture, and demonstrate how this realization is able to resolve the tension between privacy and trust in a variety of cases

Composable Probabilistic Inference with Blaise

Probabilistic inference provides a unified, systematic framework for specifying and solving these problems. Recent work has demonstrated the great value of probabilistic models defined over complex, structured domains. However, our ability to imagine probabilistic models has far outstripped our ability to programmatically manipulate them and to effectively implement inference, limiting the complexity of the problems that we can solve in practice.This thesis presents Blaise, a novel framework for composable probabilistic modeling and inference, designed to address these limitations. Blaise has three components: * The Blaise State-Density-Kernel (SDK) graphical modeling language that generalizes factor graphs by: (1) explicitly representing inference algorithms (and their locality) using a new type of graph node, (2) representing hierarchical composition and repeated substructures in the state space, the interest distribution, and the inference procedure, and (3) permitting the structure of the model to change during algorithm execution. * A suite of SDK graph transformations that may be used to extend a model (e.g. to construct a mixture model from a model of a mixture component), or to make inference more effective (e.g. by automatically constructing a parallel tempered version of an algorithm or by exploiting conjugacy in a model). * The Blaise Virtual Machine, a runtime environment that can efficiently execute the stochastic automata represented by Blaise SDK graphs. Blaise encourages the construction of sophisticated models by composing simpler models, allowing the designer to implement and verify small portions of the model and inference method, and to reuse model components from one task to another. Blaise decouples the implementation of the inference algorithm from the specification of the interest distribution, even in cases (such as Gibbs sampling) where the shape of the interest distribution guides the inference. This gives modelers the freedom to explore alternate models without slow, error-prone reimplementation. The compositional nature of Blaise enables novel reinterpretations of advanced Monte Carlo inference techniques (such as parallel tempering) as simple transformations of Blaise SDK graphs.In this thesis, I describe each of the components of the Blaise modeling framework, as well as validating the Blaise framework by highlighting a variety of contemporary sophisticated models that have been developed by the Blaise user community. I also present several surprising findings stemming from the Blaise modeling framework, including that an Infinite Relational Model can be built using exactly the same inference methods as a simple mixture model, that constructing a parallel tempered inference algorithm should be a point-and-click/one-line-of-code operation, and that Markov chain Monte Carlo for probabilistic models with complicated long-distance dependencies, such as a stochastic version of Scheme, can be managed using standard Blaise mechanisms

Composable probabilistic inference with BLAISE

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 185-190).If we are to understand human-level cognition, we must understand how the mind finds the patterns that underlie the incomplete, noisy, and ambiguous data from our senses and that allow us to generalize our experiences to new situations. A wide variety of commercial applications face similar issues: industries from health services to business intelligence to oil field exploration critically depend on their ability to find patterns in vast amounts of data and use those patterns to make accurate predictions. Probabilistic inference provides a unified, systematic framework for specifying and solving these problems. Recent work has demonstrated the great value of probabilistic models defined over complex, structured domains. However, our ability to imagine probabilistic models has far outstripped our ability to programmatically manipulate them and to effectively implement inference, limiting the complexity of the problems that we can solve in practice. This thesis presents BLAISE, a novel framework for composable probabilistic modeling and inference, designed to address these limitations. BLAISE has three components: * The BLAISE State-Density-Kernel (SDK) graphical modeling language that generalizes factor graphs by: (1) explicitly representing inference algorithms (and their locality) using a new type of graph node, (2) representing hierarchical composition and repeated substructures in the state space, the interest distribution, and the inference procedure, and (3) permitting the structure of the model to change during algorithm execution. * A suite of SDK graph transformations that may be used to extend a model (e.g. to construct a mixture model from a model of a mixture component), or to make inference more effective (e.g. by automatically constructing a parallel tempered version of an algorithm or by exploiting conjugacy in a model).(cont.) * The BLAISE Virtual Machine, a runtime environment that can efficiently execute the stochastic automata represented by BLAISE SDK graphs. BLAISE encourages the construction of sophisticated models by composing simpler models, allowing the designer to implement and verify small portions of the model and inference method, and to reuse mode components from one task to another. BLAISE decouples the implementation of the inference algorithm from the specification of the interest distribution, even in cases (such as Gibbs sampling) where the shape of the interest distribution guides the inference. This gives modelers the freedom to explore alternate models without slow, error-prone reimplementation. The compositional nature of BLAISE enables novel reinterpretations of advanced Monte Carlo inference techniques (such as parallel tempering) as simple transformations of BLAISE SDK graphs. In this thesis, I describe each of the components of the BLAISE modeling framework, as well as validating the BLAISE framework by highlighting a variety of contemporary sophisticated models that have been developed by the BLAISE user community. I also present several surprising findings stemming from the BLAISE modeling framework, including that an Infinite Relational Model can be built using exactly the same inference methods as a simple mixture model, that constructing a parallel tempered inference algorithm should be a point-and-click/one-line-of-code operation, and that Markov chain Monte Carlo for probabilistic models with complicated long-distance dependencies, such as a stochastic version of Scheme, can be managed using standard BLAISE mechanisms.by Keith Allen Bonawitz.Ph.D

Mind the Gap: Investigating Toddlers’ Sensitivity to Contact Relations in Predictive Events

Toddlers readily learn predictive relations between events (e.g., that event A predicts event B). However, they intervene on A to try to cause B only in a few contexts: When a dispositional agent initiates the event or when the event is described with causal language. The current studies look at whether toddlers’ failures are due merely to the difficulty of initiating interventions or to more general constraints on the kinds of events they represent as causal. Toddlers saw a block slide towards a base, but an occluder prevented them from seeing whether the block contacted the base; after the block disappeared behind the occluder, a toy connected to the base did or did not activate. We hypothesized that if toddlers construed the events as causal, they would be sensitive to the contact relations between the participants in the predictive event. In Experiment 1, the block either moved spontaneously (no dispositional agent) or emerged already in motion (a dispositional agent was potentially present). Toddlers were sensitive to the contact relations only when a dispositional agent was potentially present. Experiment 2 confirmed that toddlers inferred a hidden agent was present when the block emerged in motion. In Experiment 3, the block moved spontaneously, but the events were described either with non-causal (“here’s my block”) or causal (“the block can make it go”) language. Toddlers were sensitive to the contact relations only when given causal language. These findings suggest that dispositional agency and causal language facilitate toddlers’ ability to represent causal relationships.John Templeton Foundation (#12667)James S. McDonnell Foundation (Causal Learning Collaborative Initiative)National Science Foundation (U.S.) (Career Award (# 0744213

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

Repression of Mitochondrial Translation, Respiration and a Metabolic Cycle-Regulated Gene, SLF1, by the Yeast Pumilio-Family Protein Puf3p

Synthesis and assembly of the mitochondrial oxidative phosphorylation (OXPHOS) system requires genes located both in the nuclear and mitochondrial genomes, but how gene expression is coordinated between these two compartments is not fully understood. One level of control is through regulated expression mitochondrial ribosomal proteins and other factors required for mitochondrial translation and OXPHOS assembly, which are all products of nuclear genes that are subsequently imported into mitochondria. Interestingly, this cadre of genes in budding yeast has in common a 3′-UTR element that is bound by the Pumilio family protein, Puf3p, and is coordinately regulated under many conditions, including during the yeast metabolic cycle. Multiple functions have been assigned to Puf3p, including promoting mRNA degradation, localizing nucleus-encoded mitochondrial transcripts to the outer mitochondrial membrane, and facilitating mitochondria-cytoskeletal interactions and motility. Here we show that Puf3p has a general repressive effect on mitochondrial OXPHOS abundance, translation, and respiration that does not involve changes in overall mitochondrial biogenesis and largely independent of TORC1-mitochondrial signaling. We also identified the cytoplasmic translation factor Slf1p as yeast metabolic cycle-regulated gene that is repressed by Puf3p at the post-transcriptional level and promotes respiration and extension of yeast chronological life span when over-expressed. Altogether, these results should facilitate future studies on which of the many functions of Puf3p is most relevant for regulating mitochondrial gene expression and the role of nuclear-mitochondrial communication in aging and longevity

TEFM (c17orf42) is necessary for transcription of human mtDNA

Here we show that c17orf42, hereafter TEFM (transcription elongation factor of mitochondria), makes a critical contribution to mitochondrial transcription. Inactivation of TEFM in cells by RNA interference results in respiratory incompetence owing to decreased levels of H- and L-strand promoter-distal mitochondrial transcripts. Affinity purification of TEFM from human mitochondria yielded a complex comprising mitochondrial transcripts, mitochondrial RNA polymerase (POLRMT), pentatricopeptide repeat domain 3 protein (PTCD3), and a putative DEAD-box RNA helicase, DHX30. After RNase treatment only POLRMT remained associated with TEFM, and in human cultured cells TEFM formed foci coincident with newly synthesized mitochondrial RNA. Based on deletion mutants, TEFM interacts with the catalytic region of POLRMT, and in vitro TEFM enhanced POLRMT processivity on ss- and dsDNA templates. TEFM contains two HhH motifs and a Ribonuclease H fold, similar to the nuclear transcription elongation regulator Spt6. These findings lead us to propose that TEFM is a mitochondrial transcription elongation factor

Mitochondria of the Yeasts Saccharomyces cerevisiae and Kluyveromyces lactis Contain Nuclear rDNA-Encoded Proteins

In eukaryotes, the nuclear ribosomal DNA (rDNA) is the source of the structural 18S, 5.8S and 25S rRNAs. In hemiascomycetous yeasts, the 25S rDNA sequence was described to lodge an antisense open reading frame (ORF) named TAR1 for Transcript Antisense to Ribosomal RNA. Here, we present the first immuno-detection and sub-cellular localization of the authentic product of this atypical yeast gene. Using specific antibodies against the predicted amino-acid sequence of the Saccharomyces cerevisiae TAR1 product, we detected the endogenous Tar1p polypeptides in S. cerevisiae (Sc) and Kluyveromyces lactis (Kl) species and found that both proteins localize to mitochondria. Protease and carbonate treatments of purified mitochondria further revealed that endogenous Sc Tar1p protein sub-localizes in the inner membrane in a Nin-Cout topology. Plasmid-versions of 5′ end or 3′ end truncated TAR1 ORF were used to demonstrate that neither the N-terminus nor the C-terminus of Sc Tar1p were required for its localization. Also, Tar1p is a presequence-less protein. Endogenous Sc Tar1p was found to be a low abundant protein, which is expressed in fermentable and non-fermentable growth conditions. Endogenous Sc TAR1 transcripts were also found low abundant and consistently 5′ flanking regions of TAR1 ORF exhibit modest promoter activity when assayed in a luciferase-reporter system. Using rapid amplification of cDNA ends (RACE) PCR, we also determined that endogenous Sc TAR1 transcripts possess heterogeneous 5′ and 3′ ends probably reflecting the complex expression of a gene embedded in actively transcribed rDNA sequence. Altogether, our results definitively ascertain that the antisense yeast gene TAR1 constitutes a functional transcription unit within the nuclear rDNA repeats

Structural analysis and DNA binding of the HMG domains of the human mitochondrial transcription factor A

The mitochondrial transcription factor A (mtTFA) is central to assembly and initiation of the mitochondrial transcription complex. Human mtTFA (h-mtTFA) is a dual high mobility group box (HMGB) protein that binds site-specifically to the mitochondrial genome and demarcates the promoters for recruitment of h-mtTFB1, h-mtTFB2 and the mitochondrial RNA polymerase. The stoichiometry of h-mtTFA was found to be a monomer in the absence of DNA, whereas it formed a dimer in the complex with the light strand promoter (LSP) DNA. Each of the HMG boxes and the C-terminal tail were evaluated for their ability to bind to the LSP DNA. Removal of the C-terminal tail only slightly decreased nonsequence specific DNA binding, and box A, but not box B, was capable of binding to the LSP DNA. The X-ray crystal structure of h-mtTFA box B, at 1.35 Å resolution, revealed the features of a noncanonical HMG box. Interactions of box B with other regions of h-mtTFA were observed. Together, these results provide an explanation for the unusual DNA-binding properties of box B and suggest possible roles for this domain in transcription complex assembly

Effects of explaining on children's preference for simpler hypotheses

Research suggests that the process of explaining influences causal reasoning by prompting learners to favor hypotheses that offer "good" explanations. One feature of a good explanation is its simplicity. Here, we investigate whether prompting children to generate explanations for observed effects increases the extent to which they favor causal hypotheses that offer simpler explanations, and whether this changes over the course of development. Children aged 4, 5, and 6 years observed several outcomes that could be explained by appeal to a common cause (the simple hypothesis) or two independent causes (the complex hypothesis). We varied whether children were prompted to explain each observation or, in a control condition, to report it. Children were then asked to make additional inferences for which the competing hypotheses generated different predictions. The results revealed developmental differences in the extent to which children favored simpler hypotheses as a basis for further inference in this task: 4-year-olds did not favor the simpler hypothesis in either condition; 5-year-olds favored the simpler hypothesis only when prompted to explain; and 6-year-olds favored the simpler hypothesis whether or not they explained