Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Genes Involved in Cyclic Processes by Combining Gene Expression Analysis and Prior Knowledge

Based on time series gene expressions, cyclic genes can be recognized via spectral analysis and statistical periodicity detection tests. These cyclic genes are usually associated with cyclic biological processes, for example, cell cycle and circadian rhythm. The power of a scheme is practically measured by comparing the detected periodically expressed genes with experimentally verified genes participating in a cyclic process. However, in the above mentioned procedure the valuable prior knowledge only serves as an evaluation benchmark, and it is not fully exploited in the implementation of the algorithm. In addition, partial data sets are also disregarded due to their nonstationarity. This paper proposes a novel algorithm to identify cyclic-process-involved genes by integrating the prior knowledge with the gene expression analysis. The proposed algorithm is applied on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, respectively. Biological evidences are found to validate the roles of the discovered genes in cell cycle and circadian rhythm. Dendrograms are presented to cluster the identified genes and to reveal expression patterns. It is corroborated that the proposed novel identification scheme provides a valuable technique for unveiling pathways related to cyclic processes.
Faerieplay on Tiny Trusted Third Parties ( Work in Progress ) ∗

Many security protocols refer to a trusted third party (TTP) as an ideal way of handling computation and data with conflicting stakeholders. Subsequent discussion usually dismisses a TTP as hypothetical or impractical. However, the last decade has seen the emergence of hardware-based devices like the IBM 4758 that, to high assurance, can carry out computation unmolested; TPM-based systems like Intel’s Lagrande also provide secure platforms; emerging research in trusted computing promises more. In theory, such devices can perform the role of a TTP in real-world problems. In practice, all existing devices have problems. TPM-based systems are not secure against physical attack. The 4758 aspires to be general-purpose but is too small to accommodate real-world problem sizes. The small size forces programmers to hand-tune each algorithm anew, to fit inside the small space without losing security. This tuning heavily uses operations that general-purpose processors do not perform well. Furthermore, current devices are too expensive to deploy widely. Our current research attempts to overcome these barriers, by focusing on the effective use of tiny TTPs (T3Ps). To eliminate the programming obstacle, we designed and prototyped an efficient system, called Faerieplay, to execute arbitrary programs on T3Ps while preserving critical trust properties. To eliminate the performance and cost obstacles, we are currently examining the potential hardware design for a T3P optimized for bottleneck operations. We estimate that such a T3P could outperform the 4758 by several orders of magnitude, while also having a gate-count of only 30K-60K, one to three orders of magnitude smaller than the 4758 or hardened CPU systems like AEGIS. We are currently proceeding with a proof-of-concept prototype on a Xilinx FPGA.
Experience of risk-reducing salpingo-oophorectomy for a BRCA1 mutation carrier and establishment of a system performing a preventive surgery for hereditary breast and ovarian cancer syndrome in Japan: our challenges for the future.

BACKGROUND Risk-reducing salpingo-oophorectomy is currently regarded as the most certain primary method for preventing ovarian cancer among BRCA1/2 mutation carriers with hereditary breast and ovarian cancer syndrome. However, risk-reducing salpingo-oophorectomy has rarely been performed in Japan. METHODS We developed the first system in Japan for performing risk-reducing salpingo-oophorectomy for BRCA1/2 mutation carriers at our university hospital in 2008. RESULTS The indication for risk-reducing salpingo-oophorectomy for patients with hereditary breast/ovarian cancer syndrome is currently limited in Japan. This situation may be because of the limited number of genetic counseling units, the limited number of facilities that can perform BRCA1/2 genetic testing and the fact that prophylactic surgery is not covered by health insurance in Japan. CONCLUSIONS Recent treatment guidelines for breast cancer in Japan recommended risk-reducing salpingo-oophorectomy for BRCA1/2 mutation carriers. Risk-reducing salpingo-oophorectomy should be performed in the framework of the standard therapeutic modality for BRCA1/2 mutation carriers in the near future.
Embedded Neural Recording With TinyOS-Based Wireless-Enabled Processor Modules

To create a wireless neural recording system that can benefit from the continuous advancements being made in embedded microcontroller and communications technologies, an embedded-system-based architecture for wireless neural recording has been designed, fabricated, and tested. The system consists of commercial-off-the-shelf wireless-enabled processor modules (motes) for communicating the neural signals, and a back-end database server and client application for archiving and browsing the neural signals. A neural-signal-acquisition application has been developed to enable the mote to either acquire neural signals at a rate of 4000 12-bit samples per second, or detect and transmit spike heights and widths sampled at a rate of 16670 12-bit samples per second on a single channel. The motes acquire neural signals via a custom low-noise neural-signal amplifier with adjustable gain and high-pass corner frequency that has been designed, and fabricated in a 1.5-¿m CMOS process. In addition to browsing acquired neural data, the client application enables the user to remotely toggle modes of operation (real-time or spike-only), as well as amplifier gain and high-pass corner frequency.
Control of reaction-diffusion equations on time-evolving manifolds

Among the main actors of organism development there are morphogens, which are signaling molecules diffusing in the developing organism and acting on cells to produce local responses. Growth is thus determined by the distribution of such signal. Meanwhile, the diffusion of the signal is itself affected by the changes in shape and size of the organism. In other words, there is a complete coupling between the diffusion of the signal and the change of the shapes. In this paper, we introduce a mathematical model to investigate such coupling. The shape is given by a manifold, that varies in time as the result of a deformation given by a transport equation. The signal is represented by a density, diffusing on the manifold via a diffusion equation. We show the non-commutativity of the transport and diffusion evolution by introducing a new concept of Lie bracket between the diffusion and the transport operator. We also provide numerical simulations showing this phenomenon.
Progression of DNA Marker and the Next Generation of Crop Development

Advancement in genomic technology has been the main thrust for the progression of DNA markers that is now approaching a critical point in providing a platform for the next generation of varietal development. Improving total yield production to meet the increasing need to feed the world population remains the major goal. However, achieving more sophistication in providing high quality crop products to meet the emerging demand for better nutritional values and food functionalities will increasingly become important goals. Progression in high throughput marker analyses, significant reduction in the cost per data point, sophistication in computational tools, and creation of customized sets of markers for specific breeding applications are continuing and expected to have direct implications for highly efficient crop development in the near future. An advanced DNA marker system can be used to accomplish breeding goals, as well as achieve various scientific goals. The goals encompass a wide array of targets from understanding the function of specific genes so detailed that the quality of gene output or products can be controlled to attaining a global view of genomic utility to improve crop development efficiency. The combination of molecular understanding at the individual gene levels and genetic manipulation at the genome levels may lead to a significant yield leap to meet global food challenges.
Using LTL rewriting to improve the performance of model-checker based test-case generation

Model-checkers have recently been suggested for automated software test-case generation. Several works have presented methods that create efficient test-suites using model-checkers. Ease of use and complete automation are major advantages of such approaches. However, the use of a model-checker comes at the price of potential performance problems. If the model used for test-case generation is complex, then model-checker based approaches can be very slow, or even not applicable at all. In this paper, we identify that unnecessary, redundant calls to the model-checker are one of the causes of bad performance. To overcome this problem, we suggest the use of temporal logic rewriting techniques, which originate from runtime verification research. This achieves a significant increase in the performance, and improves the applicability of model-checker based test-case generation approaches in general. At the same time, the suggested techniques achieve a reduction of the resulting test-suite sizes without degradation of the fault sensitivity. This helps to reduce the costs of the test-case execution.
Semi-Automatic Classification of Skeletal Morphology in Genetically Altered Mice Using Flat-Panel Volume Computed Tomography

Rapid progress in exploring the human and mouse genome has resulted in the generation of a multitude of mouse models to study gene functions in their biological context. However, effective screening methods that allow rapid noninvasive phenotyping of transgenic and knockout mice are still lacking. To identify murine models with bone alterations in vivo, we used flat-panel volume computed tomography (fpVCT) for high-resolution 3-D imaging and developed an algorithm with a computational intelligence system. First, we tested the accuracy and reliability of this approach by imaging discoidin domain receptor 2- (DDR2-) deficient mice, which display distinct skull abnormalities as shown by comparative landmark-based analysis. High-contrast fpVCT data of the skull with 200 μm isotropic resolution and 8-s scan time allowed segmentation and computation of significant shape features as well as visualization of morphological differences. The application of a trained artificial neuronal network to these datasets permitted a semi-automatic and highly accurate phenotype classification of DDR2-deficient compared to C57BL/6 wild-type mice. Even heterozygous DDR2 mice with only subtle phenotypic alterations were correctly determined by fpVCT imaging and identified as a new class. In addition, we successfully applied the algorithm to classify knockout mice lacking the DDR1 gene with no apparent skull deformities. Thus, this new method seems to be a potential tool to identify novel mouse phenotypes with skull changes from transgenic and knockout mice on the basis of random mutagenesis as well as from genetic models. However for this purpose, new neuronal networks have to be created and trained. In summary, the combination of fpVCT images with artificial neuronal networks provides a reliable, novel method for rapid, cost-effective, and noninvasive primary screening tool to detect skeletal phenotypes in mice.
A likelihood approach to analysis of network data

Biological, sociological, and technological network data are often analyzed by using simple summary statistics, such as the observed degree distribution, and nonparametric bootstrap procedures to provide an adequate null distribution for testing hypotheses about the network. In this article we present a full-likelihood approach that allows us to estimate parameters for general models of network growth that can be expressed in terms of recursion relations. To handle larger networks we have developed an importance sampling scheme that allows us to approximate the likelihood and draw inference about the network and how it has been generated, estimate the parameters in the model, and perform parametric bootstrap analysis of network data. We illustrate the power of this approach by estimating growth parameters for the Caenorhabditis elegans protein interaction network.
Modeling the Trust with ECT in E-auction Loyalty

The increasing popularity in the new digital economy, and the Internet is considered a technology asset because its efficiency to quickly break geographical barriers. The purpose of this study is to explore from customerspsila perspective what attracts them to online auction sites and keeps them coming back. This article provides an understanding on online auction customer with loyalty of online customer behaviors. The structural equation modeling (SEM) was used. Discussions of how consumerspsila satisfaction interacts with auction Website loyalty which is an essence of the business and consumer relationship. Proposals are made of how online auction Website managers can use these findings to make marketing strategies.
An algorithm for embedding graphs in the torus ∗

An efficient algorithm for embedding graphs in the torus is presented. Given a graph G, the algorithm either returns an embedding ofG in the torus or a subgraph ofG which is a subdivision of a minimal nontoroidal graph. The algorithm based on [13] avoids the most complicated step of [13] by applying a recent result of Fiedler, Huneke, Richter, and Robertson [5] about the genus of graphs in the projective plane, and simplifies other steps on the expense of losing linear time complexity.
VUMeF: Extending the French Involvement in the UMLS metathesaurus

A considerable number of robust vocabularies and thesauri have been developed for the healthcare and biomedical domain. No single vocabulary, however; provides complete coverage of the information needs from a public health perspective. The results of an investigation of vocabulary sources for the development of a comprehensive controlled vocabulary for the public health domain at the Centers for Disease Control and Prevention (CDC) is presented.
Simultaneous Active Learning of Classifiers &amp; Attributes via Relative Feedback

Active learning provides useful tools to reduce annotation costs without compromising classifier performance. However it traditionally views the supervisor simply as a labeling machine. Recently a new interactive learning paradigm was introduced that allows the supervisor to additionally convey useful domain knowledge using attributes. The learner first conveys its belief about an actively chosen image e.g. "I think this is a forest, what do you think? ". If the learner is wrong, the supervisor provides an explanation e.g. "No, this is too open to be a forest". With access to a pre-trained set of relative attribute predictors, the learner fetches all unlabeled images more open than the query image, and uses them as negative examples of forests to update its classifier. This rich human-machine communication leads to better classification performance. In this work, we propose three improvements over this set-up. First, we incorporate a weighting scheme that instead of making a hard decision reasons about the likelihood of an image being a negative example. Second, we do away with pre-trained attributes and instead learn the attribute models on the fly, alleviating overhead and restrictions of a pre-determined attribute vocabulary. Finally, we propose an active learning framework that accounts for not just the label-but also the attributes-based feedback while selecting the next query image. We demonstrate significant improvement in classification accuracy on faces and shoes. We also collect and make available the largest relative attributes dataset containing 29 attributes of faces from 60 categories.
APC Forum: Business Implications of Virtual Worlds and Serious Gaming

Business opportunities provided by 3-D virtual worlds and “serious gaming” (adapting computer-gaming technology for business use) are beginning to emerge. The CIO membership of the Society for Information Management’s Advanced Practice Council (APC) commissioned research to track these opportunities and the management implications. Early experiments of virtual world and serious gaming applications fall into three categories — those that use social presence capabilities, those that use visualization capabilities
Digital WPI Major Qualifying Projects ( All Years ) Major Qualifying Projects March 2019 Privacy-Preserving Annotation

In this project we investigate the viability of collecting annotations for face images while preserving privacy by using synthesized images as surrogates. We compare two approaches: a deep learning model [1] to render a detailed 3D reconstruction of the face from an input image; and a novel generative adversarial network architecture that extends BEGAN-CS [2] to generated images conditioned on desired facial features. Using these two models, we conduct an experiment with crowdsourced workers to compare annotation quality of original face images and synthesized versions. Across 60 workers annotating a total of 180 images (60 of each version), we find that while original versions have the best accuracy (84.5%), the 3D (75.9%) and GAN (75.6%) versions show promising results.
Oral anticoagulant therapy

Oral anticoagulant therapy is effective antithrombotic treatment for several indications. The results of prothrombin time monitoring should be reported as the International Normalized Ratio (INR). An INR of 2 to 3 is the recommended therapeutic range for all indications except for the prevention of systemic embolism in patients with mechanical heart valves and for the long‐term treatment of patients with myocardial infarction, for whom an INR range of 2.5 to 3.5 is recommended. Oral anticoagulant therapy with warfarin sodium is the preferred approach for preventing stroke in most patients with atrial fibrillation. The available data suggest that warfarin is more effective than aspirin. Aspirin, 325 mg/d, is indicated for patients in whom warfarin is contraindicated or in patients less than 75 years of age who are at low risk for stroke because risk factors are absent. In patients 75 years of age or more, close monitoring of warfarin treatment is prudent to avoid major bleeding due to poor anticoagulant control. In selected patients, patient‐self‐monitoring and adjustment of warfarin treatment using a portable prothrombin time monitor may be effective and safe.
Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces

A key element in the solution of reinforcement learning problems is the value function. The purpose of this function is to measure the long-term utility or value of any given state. The function is important because an agent can use this measure to decide what to do next. A common problem in reinforcement learning when applied to systems having continuous states and action spaces is that the value function must operate with a domain consisting of real-valued variables, which means that it should be able to represent the value of infinitely many state and action pairs. For this reason, function approximators are used to represent the value function when a close-form solution of the optimal policy is not available. In this article, we extend a previously proposed reinforcement learning algorithm so that it can be used with function approximators that generalize the value of individual experiences across both state and action spaces. In particular, we discuss the benefits of using sparse coarse-coded function approximators to represent value functions and describe in detail three implementations: cerebellar model articulation controllers, instance-based, and case-based. Additionally, we discuss how function approximators having different degrees of resolution in different regions of the state and action spaces may influence the performance and learning efficiency of the agent. We propose a simple and modular technique that can be used to implement function approximators with nonuniform degrees of resolution so that the value function can be represented with higher accuracy in important regions of the state and action spaces. We performed extensive experiments in the double-integrator and pendulum swing-up systems to demonstrate the proposed ideas. '
Detail-Preserving Controllable Deformation from Sparse Examples

Recent advances in laser scanning technology have made it possible to faithfully scan a real object with tiny geometric details, such as pores and wrinkles. However, a faithful digital model should not only capture static details of the real counterpart but also be able to reproduce the deformed versions of such details. In this paper, we develop a data-driven model that has two components; the first accommodates smooth large-scale deformations and the second captures high-resolution details. Large-scale deformations are based on a nonlinear mapping between sparse control points and bone transformations. A global mapping, however, would fail to synthesize realistic geometries from sparse examples, for highly deformable models with a large range of motion. The key is to train a collection of mappings defined over regions locally in both the geometry and the pose space. Deformable fine-scale details are generated from a second nonlinear mapping between the control points and per-vertex displacements. We apply our modeling scheme to scanned human hand models, scanned face models, face models reconstructed from multiview video sequences, and manually constructed dinosaur models. Experiments show that our deformation models, learned from extremely sparse training data, are effective and robust in synthesizing highly deformable models with rich fine features, for keyframe animation as well as performance-driven animation. We also compare our results with those obtained by alternative techniques.
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity

Causal analysis of continuous-valued variables typically uses either autoregressive models or linear Gaussian Bayesian networks with instantaneous effects. Estimation of Gaussian Bayesian networks poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how to combine the non-Gaussian instantaneous model with autoregressive models. We show that such a non-Gaussian model is identifiable without prior knowledge of network structure, and we propose an estimation method shown to be consistent. This approach also points out how neglecting instantaneous effects can lead to completely wrong estimates of the autoregressive coefficients.
Merging multiple criteria to identify suspicious reviews

Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor, given the rewards that can be derived from posting false or biased reviews. In this paper we present a number of criteria that might be indicative of suspicious reviews and evaluate alternative methods for integrating these criteria to produce a unified "suspiciousness" ranking. The criteria derive from characteristics of the network of reviewers and also from analysis of the content and impact of reviews and ratings. The integration methods that are evaluated are singular value decomposition and the unsupervised hedge algorithm. These alternatives are evaluated in a user study on TripAdvisor reviews, where volunteers were asked to rate the suspiciousness of reviews that have been highlighted by the criteria.