Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling skin and ageing phenotypes using latent variable models in Infer.NET

We demonstrate and compare three unsupervised Bayesian latent variable models implemented in Infer.NET [2] for biomedical data modeling of 42 skin and aging phenotypes measured on the 12,000 female twins in the Twins UK study [7]. We address various data modeling problems include high missingness, heterogeneous data, and repeat observations. We compare the proposed models in terms of their performance at predicting disease labels and symptoms from available explanatory variables, concluding that factor analysis type models have the strongest statistical performance in this setting. We show that such models can be combined with regression components for improved interpretability. This work is being performed in collaboration with the Department of Twin Research and Genetic Epidemiology (DTR) at King’s College London. The DTR manages the largest UK adult twin registry of around 12,000 female monozygotic and dizygotic twins, established in 1992 [7]. The data has characteristics common to many biomedical applications, each of which we our able to address using our modeling framework. 1. High missingness. Many variables have up to 80% missing, and the level of overlap between phenotypes varies considerably. This level of missingness motivates Bayesian methods which are able to naturally deal with missingness, rather than attempting crude imputation procedures. 2. Heterogeneous data. The data contains continuous, categorical (including binary), ordinal and count data. We show in simulation experiments that using appropriate likelihood functions for each of these data types improves statistical power. 3. Multiple observations. Often the same underlying phenotype is recorded as multiple measurements, and the measurements may not be consistent. Allowing the model to combine these measurements into a single phenotype aids interpretability, improves statistical power and helps deal with the missingness problem. 4. High dimensional. The Twins UK database contains over 6000 phenotype and exposure variables, measured at multiple time points. Modern healthcare records are of the same nature. For a subset of 800 individuals we have 10,000 gene expression measurements in three different tissues, and the genotype of 600k Single Nucleotide Polymorphisms (SNPs). Our modeling framework allows these issues to be straightforwardly and rigorously addressed, and provides an efficient inference platform using Variational Message Passing under the Infer.NET framework. Although the models we use all provide some form of dimensionality reduction, which is essential for the high dimensional nature of the data, we currently only analysis around 40 phenotypes of particular relevance to skin and aging. Scaling these models to handle the full dataset, including gene expression and genotype data, is ongoing research. An attribute of the data that we have not fully explored how to model at this stage is that it is time series data. Most individuals in the study group have made multiple visits to be medically
It Matters How You Sell It: Lojack in Mexico

Lojack was shown to have provided large positive externalities to vehicle owners who did not equip their vehicles with this tracking technology in the U.S. (Ayres and Levitt, 1998). This was the result of a large degree of uncertainty about which cars did and which did not have Lojack. The way Lojack was sold to the public was essential in achieving this result. We document how in Mexico the same device was sold in a dierent manner, generating much less ambiguity about which vehicles were equipped with the device. This led to the complete elimination of the positive spillover eect. For cars equipped with Lojack, theft rates fell by over 50%. However, the data also suggest that this deterrence eect could have been obtained with Lojack installed in 40% or fewer of the vehicles in which it was installed. We are able to test for the existence of externalities to vehicles without Lojack, both in states where Lojack operated and where it did not. We do not find much evidence for negative spillovers, although there is some indication that states that were close to those where Lojack operated saw an increase in theft rates coincidental with the introduction of the program.
Suppression and reversal of motion perception around the time of the saccade

We make fast, “saccadic” eye movements to capture finely resolved foveal snapshots of the world but these saccades cause motion artefacts. The artefacts go unnoticed, perhaps because the brain suppresses them through subcortical oculomotor signals feeding back into visual cortex. Opposing views, however, claim that passive mechanisms suffice: saccadic shearing forces might render the retina insensitive to the artefacts or post-saccadic snapshots might mask them before they enter consciousness. Crucially, only active suppression could explain perceptual changes that precede saccades but existing evidence for presaccadic misperception are ill-suited for addressing this issue: Previous studies have found misperceptions of space for objects briefly flashed before saccades, but perhaps only because observers confused the timing of flashes and saccades before they could be tested (“postdiction”), and presaccadic motion perception might have appeared to decline because motion stimuli persisted past eye movement onset. Here we addressed these concerns using briefly flashed two-frame animations (50 ms) to probe people’s motion sensitivity during and around saccades. We found that sensitivity declined before saccade onset, even when the probe appeared entirely outside the saccade, and this sensitivity decline was present for motion in every direction relative to saccade, ruling out problems with postdiction. Intriguingly, brief periods during the saccade produced negative sensitivity as if motion was reversed, arguably due to postsaccadic enhancement. These data suggest that motion perception is minimized during saccades through active suppression, complementing neurophysiological findings of colliculo-pulvinar projections that suppress the cortical middle temporal area around the time of the saccade.
Using previous experience for humanoid navigation planning

We propose a humanoid robot navigation planning framework that reuses previous experience to decrease planning time. The framework is intended for navigating complex unstructured environments using both palm and foot contacts. In a complex environment, discrete-search-based contact space planners trade-off between high branching factor and action flexibility. Although approaches such as weighted A*, ARA* and ANA* could speed up the search by compromising on optimality, they can be very slow when the heuristic is inaccurate. In the proposed framework, an experience-retrieval module is added in parallel to ANA*. This module collects previously-generated motion plans and clusters them based on contact pose similarity to form a motion plan library. To retrieve an appropriate plan from the library for a given environment, the framework uses a distance between the contact poses in the plan and environment surfaces. Candidate plans are then modified with local trajectory optimization until a plan fitting the query environment is found. Our experiments show that the proposed framework outperforms planning-from-scratch in success rate in unstructured environments by at least 28% and can navigate difficult environments such as rubble and narrow corridors.
Chronological Scientific Information Recommendation via Supervised Dynamic Topic Modeling

Scientific information recommendation is crucial to assist scholars for their researches. Citation recommendation is an important field of scientific recommendation. Traditional approaches ignore the chronological nature of the citation recommendation task. In this study, I propose the "Chronological Citation Recommendation," which assumes initial user information need could shift while they are looking for the papers in different time slices. Specifically, I employed a supervised dynamic topic model to characterize the content "time-varying" dynamics and constructed a novel heterogeneous graph that contains dynamic topic-based information, time-decay citation information and word-based information. I applied different meta-paths for different ranking hypotheses, which carried different types of information for citation recommendation in different time slices along with information need shifting. I plan to generate the final "Chronological Citation Recommendation" rankings by feature integration using Learning to Rank. "Chronological Citation Recommendation" will recommend time-series ranking lists based on initial user textual information need. Preliminary experiments on the ACM corpus show that chronological citation recommendation will significantly improve the citation recommendation performance.
Understanding Human Activities from Observation via Semantic Reasoning for Humanoid Robots

One of the main purposes of humanoid robots is to improve the quality of life of elderly and/or disabled people by helping them in their everyday activities. In other words, robots need to interact with humans in a meaningful, flexible and adaptable manner, ideally by observing, recognizing and understanding the behavior of the humans. Understanding human activities is a common and difficult problem in the areas of Artificial Intelligence and Robotics. Since this involves the use of our cognitive capabilities, e.g. perception, reasoning, prediction, learning, planning, etc. In other words, we understand, what we are doing. Namely, we extract the semantics of the observed behavior. Then, the ideal goal is to transfer such capabilities to robots so that they can better learn from us. Automatically segmenting and recognizing activities from videos is a challenging task, due to the execution of a similar activity could be performed in many different manners. For example, if I prepare a pancake in my kitchen, then I may follow a predefined pattern. But, if I prepare a pancake in my office’s kitchen under time pressure, then I will follow another pattern even though I execute the same task. These patterns are sometimes defined by different parameters, e.g. different speeds of execution, the height of the pancake mix to pour over the stove, force used to open a bottle, after how much time do I need to flip the dough, etc., which increase the dimensionality of the problem. Our recent work [1] presented a framework for enabling robots on-line segmentation and recognition of human activities from observations. Our framework combines the information from different noisy physical sensors via semantic reasoning to enable robots to segment and recognize human activities by understanding what it sees from videos, i.e. using high-level representations (see Fig. 1). Our framework has been demonstrated to be robust to segment on-line human motions (move, not move and tool use) and object properties (ObjectActedOn and ObjectInHand) from videos.
Federal Funding and the Level of Private Expenditure on Basic Research

Economists have long been interested in questions concerning the role of government in stimulating private-sector innovation. In the late 1950s, Blank and Stigler [2] asked, in particular, whether government-funded industrial research and development (R & D) activity was likely to stimulate or substitute for private-sector expenditure on R & D. A number of studies have since addressed this issue and most conclude in favor of the existence of a positive relationship between federal R & D expenditures and the level of private R & D investment. ' Leyden and Link [8] have provided a theoretical rationale for this observed complementarity. They suggest that federallyfunded industrial R & D activity may generate knowledge spillovers and other infratechnology that may raise the productivity of inputs into private R & D. R & D is a heterogeneous activity, and a question which has received relatively little attention in the literature is the extent to which the apparent complementarity between federal and private industrial R & D spending holds for the different components of firms' R & D budgets. Cross-section evidence on this issue, produced by Link [13], suggests that:
Infarct-Derived Chondroitin Sulfate Proteoglycans Prevent Sympathetic Reinnervation after Cardiac Ischemia-Reperfusion Injury

Sympathetic nerves can regenerate after injury to reinnervate target tissues. Sympathetic regeneration is well documented after chronic cardiac ischemia, so we were surprised that the cardiac infarct remained denervated following ischemia-reperfusion (I-R). We used mice to ask if the lack of sympathetic regeneration into the scar was due to blockade by inhibitory extracellular matrix within the infarct. We found that chondroitin sulfate proteoglycans (CSPGs) were present in the infarct after I-R, but not after chronic ischemia, and that CSPGs caused inhibition of sympathetic axon outgrowth in vitro. Ventricle explants after I-R and chronic ischemia stimulated sympathetic axon outgrowth that was blocked by nerve growth factor antibodies. However, growth in I-R cocultures was asymmetrical, with axons growing toward the heart tissue consistently shorter than axons growing in other directions. Growth toward I-R explants was rescued by adding chondroitinase ABC to the cocultures, suggesting that I-R infarct-derived CSPGs prevented axon extension. Sympathetic ganglia lacking protein tyrosine phosphatase sigma (PTPRS) were not inhibited by CSPGs or I-R explants in vitro, suggesting PTPRS is the major CSPG receptor in sympathetic neurons. To test directly if infarct-derived CSPGs prevented cardiac reinnervation, we performed I-R in ptprs−/− and ptprs+/− mice. Cardiac infarcts in ptprs−/− mice were hyperinnervated, while infarcts in ptprs+/− littermates were denervated, confirming that CSPGs prevent sympathetic reinnervation of the cardiac scar after I-R. This is the first example of CSPGs preventing sympathetic reinnervation of an autonomic target following injury, and may have important consequences for cardiac function and arrhythmia susceptibility after myocardial infarction.
Phosphorylation regulates the interaction and complex formation between wt p53 protein and PARP‐1

We recently characterized the interaction between poly(ADP‐ribose) polymerase‐1 (PARP‐1) and the product of the tumor suppressor gene p53. We investigated which domains of human PARP‐1 and of human wild‐type (wt) p53 were involved in this protein–protein interaction. We generated baculoviral constructs encoding full length or distinct functional domains of both proteins. Full length PARP‐1 was simultaneously coexpressed in insect cells with full length wt p53 protein or its distinct truncated fragments and vice versa. Reciprocal immunoprecipitation of Sf9 cell lysates revealed that the central and carboxy‐terminal fragments of p53 were sufficient to confer binding to PARP‐1, whereas the amino‐terminal part harboring the transactivation functional domain was dispensable. On the other hand, the amino‐terminal and central fragments of PARP‐1 were necessary for complex formation with p53 protein. As the most important features of p53 protein are regulated by phosphorylation, we addressed the question of whether its phosphorylation is essential for binding between the two proteins. Baculovirally expressed wt p53 was post‐translationally modified. At least six distinct p53 isomeres were resolved by immunoblotting following two‐dimensional separation of baculovirally expressed wt p53 protein. Using specific phospho‐serine antibodies, we identified phosphorylation of baculovirally expressed p53 protein at five distinct sites. To define the role of p53 phosphorylation, pull‐down assays using untreated and dephosphorylated p53 protein were performed. Dephosphorylated p53 failed to bind PARP‐1 indicating that complex formation between both proteins is regulated by phosphorylation of p53. The marked phosphorylation of p53 at Ser392 observed in unstressed cells suggests that the phosphorylated carboxy‐terminal part of p53 undergoes complex formation with PARP‐1 resulting in masking of the NES and thereby preventing its export. The functional significance of the interaction between both proteins was investigated at two different conditions: inactivation of PARP‐1 and overexpression of PARP‐1. Our results unequivocally show that the presence of PARP‐1 regulates the basal expression of wt p53 in unstressed cells. © 2003 Wiley‐Liss, Inc.
Extending P450 site-of-metabolism models with region-resolution data

MOTIVATION Cytochrome P450s are a family of enzymes responsible for the metabolism of approximately 90% of FDA-approved drugs. Medicinal chemists often want to know which atoms of a molecule-its metabolized sites-are oxidized by Cytochrome P450s in order to modify their metabolism. Consequently, there are several methods that use literature-derived, atom-resolution data to train models that can predict a molecule's sites of metabolism. There is, however, much more data available at a lower resolution, where the exact site of metabolism is not known, but the region of the molecule that is oxidized is known. Until now, no site-of-metabolism models made use of region-resolution data. RESULTS Here, we describe XenoSite-Region, the first reported method for training site-of-metabolism models with region-resolution data. Our approach uses the Expectation Maximization algorithm to train a site-of-metabolism model. Region-resolution metabolism data was simulated from a large site-of-metabolism dataset, containing 2000 molecules with 3400 metabolized and 30 000 un-metabolized sites and covering nine Cytochrome P450 isozymes. When training on the same molecules (but with only region-level information), we find that this approach yields models almost as accurate as models trained with atom-resolution data. Moreover, we find that atom-resolution trained models are more accurate when also trained with region-resolution data from additional molecules. Our approach, therefore, opens up a way to extend the applicable domain of site-of-metabolism models into larger regions of chemical space. This meets a critical need in drug development by tapping into underutilized data commonly available in most large drug companies. AVAILABILITY AND IMPLEMENTATION The algorithm, data and a web server are available at http://swami.wustl. edu/xregion.
Melt Treatment of Copper and Aluminium – The Complex Step Before Casting

Aluminium and Copper are second and third in metal production world wide after iron and steel. But in melt treatment technologies they are probably the number one. Although Copper is the metal that is longest used technological by mankind and Aluminium one of the shortest, in both industries a variety of melt treatments and sometimes similar technologies were investigated over time. But as for most industries a look into the technology of a non competing neighbour often is missing, because contact is lost due to a lack of connection points. This plenary paper tries to bridge this gap, here the most important melt treatment techniques are presented and compared between these two metals. It is accentuated what the industries can learn from each other. To see the differences for Aluminium and Copper the most important properties of Copper are summarised in Table 1. Table 1. Properties of Copper and Aluminium [2]
Recursively enumerable sets and degrees

TABLE OF CONTENTS Introduction Chapter I. The relation of the structure of an r.e. set to its degree. 1. Post's program and simple sets. 2. Dominating functions and quotient lattices. 3. Maximal sets and high degrees. 4. Low degrees, atomless sets, and invariant degree classes. 5. Incompleteness and completeness for noninvariant properties. Chapter II. The structure, automorphisms, and elementary theory of the r.e. sets. 6. Basic facts and splitting theorems. 7. Hh-simple sets. 8. Major subsets and r-maximal sets. 9. Automorphisms of &. 10. The elementary theory of S. Chapter III. The structure of the r.e. degrees. 11. Basic facts. 12. The finite injury priority method. 13. The infinite injury priority method. 14. The minimal pair method and lattice embeddings in R. 15. Cupping and splitting r.e. degrees. 16. Automorphisms and decidability of R.
Resource allocation and bionomics of indigenous and exotic Cotesia (Hymenoptera: Braconidae) species reared on Sesamia calamistis

Abstract The braconid larval parasitoids Cotesia chilonis (Matsumura), C. flavipes Cameron and a strain of Cotesia sesamiae (Cameron) from coastal Kenya, reared at the International Centre of Insect Ecology and Physiology, were introduced at the International Institute of Tropical Agriculture in the Republic of Benin for suitability testing on West African stemborers prior to release. C. chilonis was originally collected in Japan while C. flavipes was imported into Kenya from Pakistan. The host species used was the noctuid Sesamia calamistis (Hampson), the most important noctuid maize pest in the region. All three Cotesia species attacked and successfully developed in 2nd to 6th larval instar of S. calamistis but parasitoid-induced mortality was highest on second instars. On most instars, C. sesamiae and C. flavipes produced larger broods than C. chilonis. Larvae parasitized by C. sesamiae developed to the 6th instar and attained an average larval weight of 353 mg, while larvae parasitized by C. chilonis only molted to the 4th instar and attained a maximum weight of 107 mg. The lower developmental threshold estimated from the non-linear regression of temperature on developmental rate was 15.9, 15.9 and 14.9°C for C. chilonis, C. sesamiae and C. flavipes, respectively, while the maximum temperature was 34.2, 35.2 and 33.8°C, respectively. A maximum of four ovipositions were observed per female during a life span ranging from 1.3 days for C. chilonis and C. flavipes to 1.6 days for C. sesamiae. The largest adult progeny, intrinsic rate of increase and net reproductive rates were recorded at 28°C for all species. However, across temperatures, C. flavipes yielded the highest number of offspring, followed by C. sesamiae and C. chilonis. The sex ratios did not vary significantly with species and temperature. Thus, the reproductive potentials of C. sesamiae and C. flavipes were greater than that of C. chilonis.
The digital currency challenge : shaping online payment systems through US financial regulations

Introduction 1. What Is Digital Currency? 2. Who Uses Digital Currency? 3. Digital Gold Currency 4. E-gold 5. Digital Currency Growth 6. Regulatory History 7. Follow the Money 8. Money Service Business 9. Prepaid Access 10. WebMoney Transfer 11. Loom 12. Bitcoin Decentralized Virtual Currency 13. Early Bitcoin 14. Bitcoin Mining 15. Bitcoin Differences 16. Benefits and Advantages 17. Disadvantages and Barriers 18. FIN-2013-G001 19. Global Bitcoin 20. Bitcoin Challenges 21. Bitcoin Merchant Services 22. Bitcoin Opportunity 23. The Future
Dysfunctional anticipatory thoughts and the self‐handicapping strategy.

Self-handicapping is an anticipatory self-protective strategy in which individuals create or claim obstacles to success prior to an important performance to excuse potential failure. The present research sought in four studies to document the anticipatory nature of self-handicapping, examining the role of prefactual (“what if …? ”) thoughts in this strategy. Individuals prone to self-handicap were more likely to generate prefactuals, identifying ways to undermine their performance. Moreover, inducing individuals to consider these thoughts increased self-handicapping behavior, whereas focusing individuals on ways to improve their performance actually reduced self-handicapping behavior. Implications of this work for understanding the cognitive processes underlying self-handicapping behavior and for interventions that seek to minimize this self-defeating behavior are discussed. Copyright © 2011 John Wiley & Sons, Ltd.