Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of chemical modulators of the constitutive activated receptor (CAR) in a gene expression compendium

The nuclear receptor family member constitutive activated receptor (CAR) is activated by structurally diverse drugs and environmentally-relevant chemicals leading to transcriptional regulation of genes involved in xenobiotic metabolism and transport. Chronic activation of CAR increases liver cancer incidence in rodents, whereas suppression of CAR can lead to steatosis and insulin insensitivity. Here, analytical methods were developed to screen for chemical treatments in a gene expression compendium that lead to alteration of CAR activity. A gene expression biomarker signature of 83 CAR-dependent genes was identified using microarray profiles from the livers of wild-type and CAR-null mice after exposure to three structurally-diverse CAR activators (CITCO, phenobarbital, TCPOBOP). A rank-based algorithm (Running Fisher's algorithm (p-value < 10-4)) was used to evaluate the similarity between the CAR biomarker signature and a test set of 28 and 32 comparisons positive or negative, respectively, for CAR activation; the test resulted in a balanced accuracy of 97%. The biomarker signature was used to identify chemicals that activate or suppress CAR in an annotated mouse liver/primary hepatocyte gene expression database of ∼1850 comparisons. CAR was activated by 1) activators of the aryl hydrocarbon receptor (AhR) in wild-type but not AhR-null mice, 2) pregnane X receptor (PXR) activators in wild-type and to lesser extents in PXR-null mice, and 3) activators of PPARα in wild-type and PPARα-null mice. CAR was consistently activated by five conazole fungicides and four perfluorinated compounds. Comparison of effects in wild-type and CAR-null mice showed that the fungicide propiconazole increased liver weight and hepatocyte proliferation in a CAR-dependent manner, whereas the perfluorinated compound perfluorooctanoic acid (PFOA) increased these endpoints in a CAR-independent manner. A number of compounds suppressed CAR coincident with increases in markers of inflammation including acetaminophen, concanavalin A, lipopolysaccharide, and 300 nm silica particles. In conclusion, we have shown that a CAR biomarker signature coupled with a rank-based similarity method accurately predicts CAR activation. This analytical approach, when applied to a gene expression compendium, increased the universe of known chemicals that directly or indirectly activate CAR, highlighting the promiscuous nature of CAR activation and signaling through activation of other xenobiotic-activated receptors.
ScratchTalk: A Natural Language Framework for Social Computation

Natural Language interfaces have been proposed as a solution to make application functionality more accessible to end users. Practical implementation of such systems has been hampered by both semantic and knowledge-engineering challenges. This paper introduces Social Computation, a theoretical model targeting both challenges. The model employs natural language in a planning framework to enable communities of end-users to collaborate, explicitly and implicitly, in the development of new functionality. The model intrinsically supports the acquisition of user goals and natural language semantics in the context of the application domain. ScratchTalk is a prototype of the natural language planning component of the model applied to the Scratch programming environment. This serves to ground the framework and provide insights into the space of applications likely to be addressable by the Social Computation model. Author
HMAX model: A survey

HMAX model is a bio-inspired feedforward architecture for object recognition, which is derived from the simple and complex cells model in cortex proposed by Hubel and Wiesel. As a hierarchical bio-based recognition model, HMAX captures the properties of primate cortex with alternated S layers and C layers, corresponding to simple cells and complex cells respectively. Although constrained by biological factors, HMAX shows satisfying performance in different fields when competing with other state-of-the-art computer vision algorithms. Insightful ideas and methods have been developed for this hierarchical model, which advances the progress of HMAX model. This paper reviews the origin of this model, as well as the improvements and modifications based on this model.
Nondiminishing disturbance rejection in linear multivariable systems

This paper presents a constructive procedure for the synthesis of linear multivariable systems whose partial state is corrupted by an unknown nondiminishing disturbance. The system zeros and the unstable modes of the disturbance are assumed to be disjoint. A stable desired closed loop transfer matrix is obtained which can be expressed as the product of an open loop transfer matrix and any proper rational transfer matrix. Internal model principle is employed to realize the closed loop transfer matrix while simultaneously eliminating the effect of steady state disturbance at the output. The controller simultaneously ensures: 1) disturbance decoupling, 2) complete and arbitrary closed loop pole placement, and 3) internal stability. The compensation scheme is presented in the frequency domain.
Indoor positioning: technology comparison analysis

A system that allows users to find and track a specific position is known as positioning system. Global Positioning System (GPS) is one of top known position tracking system that commonly used to find position and location of object outdoor. Tracking an object indoor using GPS is not highly recommended because the signals transmitted through a satellite to a device indoor gets blocked and resulted in weak signals. Thus, an indoor positioning system (IPS) that tracks and positions object indoor has been implemented to overcome the issues of signals multipath that resulted from GPS. The aim of this paper is to provide up to date indoor positioning technologies and compares the technologies according to its technical perspectives
Chapter 4 IMPROVING LEARNING BASED ON THE IDENTIFICATION OF WORKING MEMORY CAPACITY , ADAPTIVE CONTEXT SYSTEMS , COLLABORATIVE LEARNING AND LEARNING ANALYTICS

Working memory capacity and learning styles play key roles within adaptive learning environments. In addition, the concepts of collaborative efforts, context awareness, ensuring student engagement and the identification of students at risk of dropping out, play vital roles and are key to any successful learning environment. In this chapter, key concepts and mechanisms for each of them are discussed along with various approaches and frameworks. A means of utilizing artificial intelligence to improve working memory capacity identification and learning styles identification is discussed in the second section. Adaptation is discussed in both the third and fourth section, as it pertains to collaborative learning environments and adaptive context-aware expert systems. The final two sections address the problem of student drop-out rates as it pertains to improving the promotion of scientific competencies and the identification of students at risk of dropping out. All these concepts assist in providing learners with adaptive and improved learning environments that aid in supporting learners in the learning process.
Reconstruction and Rendering from Image Sequences

This contribution describes a system for 3D surface reconstruction and novel view synthesis from image streams of an unknown but static scene. The system operates fully automatic and estimates camera pose and 3D scene geometry using Structure-from-Motion and dense multi-camera stereo reconstruction. From these estimates, novel views of the scene can be rendered at interactive rates.
Freeform shape representations for efficient geometry processing

Summary form only given. The most important concepts for the handling and storage of freeform shapes in geometry processing applications are parametric representations and volumetric representations. Both have their specific advantages and drawbacks. While the algebraic complexity of volumetric representations is independent from the shape complexity, the domain of a parametric representation usually has to have the same structure as the surface itself (which sometimes makes is necessary to update the domain when the surface is modified). On the other hand, the topology of a parametrically defined surface can be controlled explicitly while in a volumetric representation, the surface topology can change accidentally during deformation. A volumetric representation reduces distance queries or inside/outside tests to mere function evaluations but the geodesic neighborhood relation between surface points is difficult to resolve. As a consequence, it seems promising to combine parametric and volumetric representations to effectively exploit both advantages. In this talk, a number of projects is presented and discussed where such a combination leads to efficient and numerically stable algorithms for the solution of various geometry processing tasks. Applications include global error control for mesh decimation and smoothing, topology control for level-set surfaces, mesh repair, and shape modeling with unstructured point clouds.
CDMTCS Research Report Series On Partial Randomness

If x = x1x2 · · ·xn · · · is a random sequence, then the sequence y = 0x10x2 · · · 0xn · · · is clearly not random; however, y seems to be “about half random”. Staiger [14, 15] and Tadaki [16] have studied the degree of randomness of sequences or reals by measuring their “degree of compression”. This line of study leads to various definitions of partial randomness. In this paper we explore some relations between these definitions. Among other results we obtain ∗Part of the work of the first author was done in Halle with support from the Martin-LutherUniversität Halle-Wittenberg. †The second author was in part supported by the University of Auckland Research Fund (Project Number 3602449/9343). ‡The third author was supported by the Austrian Research Fund (Lise Meitner grant M699-N05). a characterisation of Σ1-dimension (as defined by Schnorr and Lutz in terms of martingales) in terms of strong Martin-Löf ε-tests (a variant of Martin-Löf tests), and we show that ε-randomness for ε ∈ (0, 1) is different (and more difficult to study) than the classical 1-randomness.
Supporting Keyword Search in Product Database: A Probabilistic Approach

The ability to let users search for products conveniently in product database is critical to the success of e-commerce. Although structured query languages (e.g. SQL) can be used to effectively access the product database, it is very difficult for end users to learn and use. In this paper, we study how to optimize search over structured product entities (represented by specifications) with keyword queries such as "cheap gaming laptop". One major difficulty in this problem is the vocabulary gap between the specifications of products in the database and the keywords people use in search queries. To solve the problem, we propose a novel probabilistic entity retrieval model based on query generation, where the entities would be ranked for a given keyword query based on the likelihood that a user who likes an entity would pose the query. Different ways to estimate the model parameters would lead to different variants of ranking functions. We start with simple estimates based on the specifications of entities, and then leverage user reviews and product search logs to improve the estimation. Multiple estimation algorithms are developed based on Maximum Likelihood and Maximum a Posteriori estimators. We evaluate the proposed product entity retrieval models on two newly created product search test collections. The results show that the proposed model significantly outperforms the existing retrieval models, benefiting from the modeling of attribute-level relevance. Despite the focus on product retrieval, the proposed modeling method is general and opens up many new opportunities in analyzing structured entity data with unstructured text data. We show the proposed probabilistic model can be easily adapted for many interesting applications including facet generation and review annotation.
Optimal Policy with Partial Information in a Forward-Looking Model: Certainty-Equivalence Redux

This paper proves a certainty equivalence result for optimal policy under commitment with symmetric partial information about the state of the economy in a model with forward-looking variables. This result is used in our previous paper, Indicator Variables for Optimal Policy,' which synthesizes what is known about the case of symmetric partial information, and derives useful general formulas for computation of the optimal policy response coefficients and efficient estimates of the state of the economy in the context of a fairly general forward-looking rational-expectations model. In particular, our proof takes into account that, under commitment, the policymaker can affect the future evolution of the observable variables, and thereby potentially affect the future information available.
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis

There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One approach to resolving this is a shared response factor model. This assumes a shared and time synchronized stimulus across subjects. Such a model can often identify shared information, but it may not be able to pinpoint with high resolution the spatial location of this information. In this work, we examine a searchlight based shared response model to identify shared information in small contiguous regions (searchlights) across the whole brain. Validation using classification tasks demonstrates that we can pinpoint informative local regions.
A Collaborative Filtering Recommendation Algorithm Based on User Interest Change and Trust Evaluation

Collaborative filtering algorithm is one of the most successful technologies used in personalized recommendation system. However, traditional algorithms focus only on user ratings and do not consider the changes of user interest and the credibility of ratings data, which affected the quality of the system's recommendation seriously. To solve this problem, this paper presents an improved algorithm. Firstly, the user’s rating is given a weight by a gradual time decrease and credit assessment in the course of user similarity measurement, and then several users highly similar with active user are selected as his neighbor. Finally, the active user’s preference for an item can be represented by the average scores of his neighbor. Experimental results show that the algorithm can make the neighbor recognition more accurately and enhance the quality of recommendation system effectively. Keyword: Collaborative Filtering, Similarity Measure, Time Weight, Trust Evaluation
Ontology Enrichment : A Tool to Empower Hybrid Recommender Systems

Tremendous growth of online available information creates the need of Recommender Systems (RS). Different recommendation techniques are used based on need and purpose. Each technique is having its own advantages and disadvantages. Hybrid approach is used to overcome the limitations of different approaches for Recommendations. Approach of hybridization also varies based on the desired results. We have developed a hybrid recommender system using mixed strategy of hybridization. Designed model suppresses the limitations of individual techniques. Our designed portal recommends news based on content based approach as well collaborative approach. The system is empowered by making use of semantically enriched ontology as a knowledge base. The ontology plays a central role because it is used to model the knowledge of the domain, to make product profile as well as user profiles and the recommendation process. This empowerment helps in handling issues in recommender system. We have tested ontology enrichment for recommendations in news domain. For recommendations RSS feed news items are first pre-classified into categories and entity types without ambiguity making use semantic knowledge of ontology like synonymy, hyponymy and meronymy information. Semantics in recommendations helps in handling the issue of over specialization, diversity, sparsity, cold start problem for new item, accuracy, adaptivity and missing information in recommendations. The semantic approach also makes it possible for user to query the annotated ontology to retrieve desired information. User has also been given the choice of news source to improve personalization in recommendations. Moreover, Users are also provided with information popular among all the users using collaborative approach. Incorporating semantic knowledge into hybrid recommendation approach increases the flexibility and quality of the personalized recommendations, making it usefulness for end user.
SIFT and SURF feature analysis in visible and infrared imaging for UAVs

This paper presents an in-depth analysis of the SIFT and SURF feature detection and matching techniques in characterizing natural environments for vision based navigation problems, in particular, the performance of feature extraction algorithms and matching when both visual and infrared data are used. With successful utilization of both feature extraction methods on different characteristic images, performance metrics include processing time, number of detected features and matching rate with RANSAC outlier rejection applied are discussed, with relevant conclusions made.
Facilitating orientation in shared hypermedia workspaces

Shared workspaces are an important means for supporting long-term synchronous and asynchronous collaboration. Shared workspaces themselves become difficult to manage due to increasing size and constant change. This is especially true for shared hypermedia workspaces. Thus means for managing the shared hypermedia workspace in terms of keeping an overview of the group's work and coordinating changes become necessary. In this paper we propose a shared hypermedia workspace model representing not only shared content but also team and process related information. Four complementary tools facilitate orientation and coordination in the shared workspace: a group aware content browser, a group aware overview browser, a shared workspace search tool, and a shared process space browser. Together, these tools should enable groups to stay aware of each other's activities and to control the level of awareness according to their needs.
An Approach to Thai Decorative Pattern Recognition Using Bezier Curve Representation with Progressive Iterative Approximation

This paper presents a method to convert Thai decorative pattern (Lai-Thai) from a hand-drawn picture into a vector graphic image. The proposed method samples a series of points from a scanned image. Then a Progressive Iterative Approximation (PIA) algorithm is used to plot a corresponding Bézier curve that fits sampling points. Examples of curve fitting to Thai famous decorative patterns, Kra-Jung-Taa-Aoy and Pra-jam-yam patterns are demonstrated. The results show that more number of sampling points on the input path yields better quality in pattern recognition but the process may consume longer computational time. A challenge of this method is to choose an optimal number of sampling points to yield good quality of the approximation with minimal computational time. Quality of the output also depends on the methods of selecting sampling points.
Closing and Opening based on discrete t-norms. Applications to natural Image Analysis

This paper delves deeply into the recently introduced mathematical morphology based on discrete t-norms. Closing and opening operators and the concepts of open and closed objects are introduced. All the properties satisfied by nilpotent t-norms, even the generalized idempotence, hold too. After that, some experimental results, using comparative measures, on edge detection are showed. Some experimental results concerning the Top-Hat transformations and the basic filters built from the opening and closing operators are presented. Top-Hat experiments are compared with those obtained with the umbra approach, nilpotent t-norms and uninorms, proving that the discrete approach provides notable results. Moreover, different objective measures are used in order to evaluate the filtered results depending on the amount of noise in the image.
Adult neurogenesis produces a large pool of new granule cells in the dentate gyrus

Knowing the rate of addition of new granule cells to the adult dentate gyrus is critical to understanding the function of adult neurogenesis. Despite the large number of studies of neurogenesis in the adult dentate gyrus, basic questions about the magnitude of this phenomenon have never been addressed. The S‐phase marker bromodeoxyuridine (BrdU) has been extensively used in recent studies of adult neurogenesis, but it has been carefully tested only in the embryonic brain. Here, we show that a high dose of BrdU (300 mg/kg) is a specific, quantitative, and nontoxic marker of dividing cells in the adult rat dentate gyrus, whereas lower doses label only a fraction of the S‐phase cells. By using this high dose of BrdU along with a second S‐phase marker, [3H]thymidine, we found that young adult rats have 9,400 dividing cells proliferating with a cell cycle time of 25 hours, which would generate 9,000 new cells each day, or more than 250,000 per month. Within 5–12 days of BrdU injection, a substantial pool of immature granule neurons, 50% of all BrdU‐labeled cells in the dentate gyrus, could be identified with neuron‐specific antibodies TuJ1 and TUC‐4. This number of new granule neurons generated each month is 6% of the total size of the granule cell population and 30–60% of the size of the afferent and efferent populations (West et al. [ 1991 ] Anat Rec 231:482–497; Mulders et al. [ 1997 ] J Comp Neurol 385:83–94). The large number of the adult‐generated granule cells supports the idea that these new neurons play an important role in hippocampal function. J. Comp. Neurol. 435:406–417, 2001. Published 2001 Wiley‐Liss, Inc.
3D Pose from Motion for Cross-View Action Recognition via Non-linear Circulant Temporal Encoding

We describe a new approach to transfer knowledge across views for action recognition by using examples from a large collection of unlabelled mocap data. We achieve this by directly matching purely motion based features from videos to mocap. Our approach recovers 3D pose sequences without performing any body part tracking. We use these matches to generate multiple motion projections and thus add view invariance to our action recognition model. We also introduce a closed form solution for approximate non-linear Circulant Temporal Encoding (nCTE), which allows us to efficiently perform the matches in the frequency domain. We test our approach on the challenging unsupervised modality of the IXMAS dataset, and use publicly available motion capture data for matching. Without any additional annotation effort, we are able to significantly outperform the current state of the art.
The Prehistory of Egypt: From the First Egyptians to the First Pharaohs

Illustrations. Preface. Acknowledgements. Translatora s Note. Introduction. Part I: The Land of Egypt:. 1. Between the River and the Desert. Part II: The Palaeolithic Period: . 2. The Earliest Evidence for Humans in the Nile Valley. 3. The Beginnings of Cultural Diversity. 4. Diversity or Nilotic Adaptation. Part III: The Neolithic Period:. 5. The Process of a Neolithicizationa . 6. The Neolithic Period (Fifth Millennium BC). Part IV: The Approach to the Pharaonic Period (Fourth Millennium BC): . 7. The Predynastic Period (c. 4000--3000 BC). 8. The First Pharaohs and the Unification of the Two Lands. Conclusion. Appendix 1: Relative Chronology and the Traditional Dating Systems. Appendix 2: a Absolute Datesa . Glossary. Abbreviations. Bibliography. Index.