Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Learning from Positive and Unlabeled Data

Learning a classifier from positive and unlabeled data is an important class of classification problems that are conceivable in many practical applications. In this paper, we first show that this problem can be solved by cost-sensitive learning between positive and unlabeled data. We then show that convex surrogate loss functions such as the hinge loss may lead to a wrong classification boundary due to an intrinsic bias, but the problem can be avoided by using non-convex loss functions such as the ramp loss. We next analyze the excess risk when the class prior is estimated from data, and show that the classification accuracy is not sensitive to class prior estimation if the unlabeled data is dominated by the positive data (this is naturally satisfied in inlier-based outlier detection because inliers are dominant in the unlabeled dataset). Finally, we provide generalization error bounds and show that, for an equal number of labeled and unlabeled samples, the generalization error of learning only from positive and unlabeled samples is no worse than 2√2 times the fully supervised case. These theoretical findings are also validated through experiments.
Ontology Oriented Threat Detection System (OOTDS)

The paper discusses the design of a general purpose ontology oriented threat detection system (OOTDS) for environments monitored by sensors. The sensors are assumed to continually provide OOTDS with events reflecting changes in the environment. OOTDS performs event analysis using a network of asynchronous blocks with increasing complexity. Front blocks convert sensor specific events to a unified form. A suggestion block provides hints for threat assessment based on a set of rules. The core threat detection block estimates probabilities of the suggested threats using Multi-Entities Bayesian Networks (MEBN) logic inference based on facts resulting from observed events and knowledge stored in an environment specific threat detection ontology (TDO). Threats with sufficiently high probability levels result in notifications sent to concerned users.The paper defines main OOTDS goals and presents its architecture followed by a short introduction to MEBN logic and an exemplary OOTDS application with a usage scenario.
Real-Time Monocular SLAM With Low Memory Requirements

The localization of a vehicle in an unknown environment is often solved using simultaneous localization and mapping (SLAM) techniques. Many methods have been developed, each requiring a different amount of landmarks (map size), and thus of memory, to work efficiently. Similarly, the required computational time is quite variable from one approach to another. In this paper, we focus on a monocular SLAM problem and propose a new method called MSLAM, which is based on an extended Kalman filter (EKF). The aim is to provide a solution that has low memory and processing time requirements and that can achieve good localization results while benefiting from EKF advantages (i.e., direct access to the covariance matrix, no conversion required for the measures or the state, etc.). To do so, a minimal Cartesian representation (three parameters for three dimensions) is used. However, linearization errors are likely to happen with such a representation. New methods allowing to avoid or hugely decrease the impact of the linearization failures are presented. The first contribution proposed here computes a proper projection of a 3-D uncertainty in the image plane, allowing to track landmarks during longer periods of time. A corrective factor of the Kalman gain is also introduced. It allows to detect wrong updates and correct them, thus reducing the impact of the linearization on the whole system. Our approach is compared with a classic SLAM implementation over different data sets and conditions to illustrate the efficiency of the proposed contributions. The quality of the map built is tested by using it with another vehicle for localization purposes. Finally, a public data set presenting a long trajectory (1.3 km) is also used in order to compare MSLAM with a state-of-the-art monocular EKF-SLAM algorithm, both in terms of accuracy and computational needs.
A depth map refinement algorithm for 2D-to-3D conversion

For showing 2D video contents on 3DTVs, 2D-to-3D conversion is required to convert 2D contents to 3D ones. In general, the conversion process consists of depth map generation, which estimates the 3D geometry of the scene, and rendering, which produces output stereo images. We propose a depth map refinement algorithm which uses adaptive decimation and guided interpolation to refine the depth map. The proposed approach eliminates unnecessary textures and keeps object boundaries on the depth map. In the refined depth map, the transition of depth values within an object is smooth, the object boundaries on the depth map are aligned to those on the input image, and the foreground can be clearly separated from the background. These characteristics on the depth map help to achieve better 3D visual perception. Compared to the state-of-the-art approaches, the blurriness and object boundary alignment are easier to be adjusted by the proposed two-stage operations, and the computational complexity of the proposed algorithm is much lower.
A Fluid Knowledge Representation for Understanding and Generating Creative Metaphors

Creative metaphor is a phenomenon that stretches and bends the conventions of semantic description, often to humorous and poetic extremes. The computational modeling of metaphor thus requires a knowledge representation that is just as stretchable and semantically accommodating. We present here a flexible knowledge representation for metaphor interpretation and generation, called Talking Points, and describe how talking points can be acquired on a large scale from WordNet (Fellbaum, 1998) and from the web. We show how talking points can be fluidly connected to form a slipnet, and demonstrate that talking points provide an especially concise representation for concepts in general.
Nano-mechanical measurements of protein-DNA interactions with a silicon nitride pulley

Proteins adhere to DNA at locations and with strengths that depend on the protein conformation, the underlying DNA sequence and the ionic content of the solution. A facile technique to probe the positions and strengths of protein-DNA binding would aid in understanding these important interactions. Here, we describe a ‘DNA pulley’ for position-resolved nano-mechanical measurements of protein-DNA interactions. A molecule of λ DNA is tethered by one end to a glass surface, and by the other end to a magnetic bead. The DNA is stretched horizontally by a magnet, and a nanoscale knife made of silicon nitride is manipulated to contact, bend and scan along the DNA. The mechanical profile of the DNA at the contact with the knife is probed via nanometer-precision optical tracking of the magnetic bead. This system enables detection of protein bumps on the DNA and localization of their binding sites. We study theoretically the technical requirements to detect mechanical heterogeneities in the DNA itself.
Exploiting non-content preference attributes through hybrid recommendation method

This paper explores a method for incorporating into a recommender system explicit representations of user's preferences over non-content attributes such as popularity, recency, and similarity of recommended items. We show how such attributes can be modeled as a preference vector that can be used in a vector-space content-based recommender, and how that content-based recommender can be integrated with various collaborative filtering techniques through re-weighting of Top-M recommendations. We evaluate this approach on several recommender systems datasets and collaborative filtering methods, and find that incorporating the three preference attributes can lead to a substantial increase in Top-50 precision while also enhancing diversity and novelty.
Using global constraints for local search

Conventional ways of using local search are di cult to generalize Increased e ciency is the only goal generality often being disregarded This is manifested in the highly monolithic encodings of complex problems and the application of highly speci c satisfaction methods Other approaches take the general constraint programming framework as a starting point and try to introduce local search methods for constraint satisfaction These methods frequently fail because they have only a very limited view of the unknown search space structure The present paper attempts to overcome the drawbacks of these two ap proaches by using global constraints The use of global constraints for local search allows us to revise a current state on a more global level with domain speci c knowledge while preserving features like reusability and maintenance The proposed strategy is demonstrated on a dynamic job shop scheduling prob lem Introduction The use of local search has become very popular in recent years as applications have begun to tackle complex real world optimization problems for which complete re nement search methods are still not powerful enough Problem domains with nontrivial optimization functions large problems short computation time limits partial constraint satisfaction dynamic constraint satisfaction and only limited demands on optimality are promising candidates for local search Local search approaches perform a search by iteratively changing an initial as signment of variables In each iteration a neighborhood of potential successor states is generated The quality of the neighborhood states can be computed by a cost function This information is used by the successor choice criterion to determine the successor state Conventional ways of using local search are di cult to generalize Increased e ciency is the only goal generality often being disregarded It is quite common to de ne highly sophisticated and problem tailored representations with special ized neighborhoods and successor selection methods see Aarts for examples Mathematics Subject Classi cation Primary C Secondary T This work is supported by the German Research Foundation DFG NICOSIO Cross Plat form Research Germany CPR and Conitec Datensysteme GmbH c American Mathematical Society
Megastereo: Constructing High-Resolution Stereo Panoramas

We present a solution for generating high-quality stereo panoramas at mega pixel resolutions. While previous approaches introduced the basic principles, we show that those techniques do not generalise well to today's high image resolutions and lead to disturbing visual artefacts. As our first contribution, we describe the necessary correction steps and a compact representation for the input images in order to achieve a highly accurate approximation to the required ray space. Our second contribution is a flow-based up sampling of the available input rays which effectively resolves known aliasing issues like stitching artefacts. The required rays are generated on the fly to perfectly match the desired output resolution, even for small numbers of input images. In addition, the up sampling is real-time and enables direct interactive control over the desired stereoscopic depth effect. In combination, our contributions allow the generation of stereoscopic panoramas at high output resolutions that are virtually free of artefacts such as seams, stereo discontinuities, vertical parallax and other mono-/stereoscopic shape distortions. Our process is robust, and other types of multiperspective panoramas, such as linear panoramas, can also benefit from our contributions. We show various comparisons and high-resolution results.
Retrieving channel reciprocity for coordinated multi-point transmission

Due to the difficulty of self antenna calibration among different base stations (BSs), the uplink-downlink channels are no longer reciprocal in time division duplex coordinated multi-point transmission with joint processing (CoMP-JP) systems. To retrieve the channel reciprocity, we propose an inter-BS antenna calibration method. Specifically, we estimate the inter-BS ambiguity factors with weighted least square criterion by using the uplink and downlink channel estimates and the received signal of a calibration sounding signal of multiple supporting users or multiple frames. We then analyze the mean square error of the proposed estimator, and compare the performance of the estimators with multiple supporters and with multiple frames. Simulation results show that the channel reciprocity is largely retrieved by the proposed antenna calibration method, which provides substantial throughput gain over the CoMP-JP systems without calibration.
SoTL Research Fellows: Collaborative Pathfinding through Uncertain Terrain

From 2014-2016, Scholarship of Teaching and Learning (SoTL) Research Fellows at a mid-sized Canadian research-intensive, medical-doctoral university undertook to study their own formation as scholars of teaching and learning, as well as benefits and challenges of their cross-appointment to our central teaching and learning institute from their home academic departments. Findings from surveys and focus groups identified themes such as identity, community, access, transfer, and structural elements (each with benefits and challenges to practice). Our autoethnographic work confirms assertions in the literature about the uneasy relation between SoTL and traditional scholarship, while also bearing out the need for departmental support, and for key interventions along the path from novice to practitioner identity. Some discussion of the ambassador or translator role that can flow from such arrangements is included. De 2014 a 2016, les chercheurs en Avancement des connaissances en enseignement et en apprentissage (ACEA) d’une universite canadienne medicale-doctorale de taille moyenne ayant un coefficient de recherche eleve ont entrepris une etude portant sur leur propre formation en tant que chercheurs erudits en matiere d’enseignement et d’apprentissage, ainsi que sur les avantages et les defis de leur nomination conjointe a notre institut central d’enseignement et d’apprentissage tout en enseignant dans leur propre departement universitaire. Les resultats des sondages et des groupes de discussion ont permis d’identifier certains themes tels que l’identite, la communaute, l’acces, le transfert, ainsi que des elements structuraux (chacun presentant des avantages et des defis concernant la pratique). Notre travail autoethnographique confirme les assertions presentes dans la documentation existante concernant la relation difficile qui existe entre l’ACEA et la recherche traditionnelle, tout en tenant compte de la necessite du soutien departemental ainsi que pour les interventions cles sur la voie qui consiste a passer de l’identite de novice a celle de praticien. L’article contient egalement des discussions sur le role d’ambassadeur ou de traducteur qui peut decouler de tels arrangements.
Adenovirus Tales: From the Cell Surface to the Nuclear Pore Complex

Despite lingering safety concerns [1] and potential restrictions imposed by the host immune response, including the innate immune pattern recognition receptors (PRR), replication-defective and conditionally replicating human and nonhuman adenovirus (AdV) vectors continue to be a favorite vehicle for short-term (e.g., vaccine) and long-term gene delivery. This is due in part to several desirable features of AdV including their broad tissue tropism, their ample capacity for foreign gene insertion, and their LEGO-like structural adaptability (Fig 1A) to add, delete, and swap proteins and motifs from other viruses or host molecules. Fig 1 Adenovirus structure and trafficking. While much is known about the molecular genetics and replication of AdVs, many investigators are continuing to decipher the captivating intracellular events of the first thirty minutes in the virus life cycle. This Pearl accentuates the strikingly diverse mechanisms for AdV entry, comparing human AdV type 5 (HAdV-C5) in epithelial cells and canine type 2 (CAdV2, or commonly referred to as CAV-2) in neurons. Similar viral and cellular proteins are used, and although the function of the cellular protein varies among cell types, these cell protein—virus associations promote similar outcomes. We also highlight some outstanding questions and hurdles needed to improve vector-mediated gene and vaccine delivery and treatments for AdV disease. The take home message is that one may be able to take advantage of a better understanding of these cell entry variations to control AdV pathogenesis and vector tropism for gene therapy.
The "Authoring on the Fly" system for automatic presentation recording

In the presentation recording scenario there are two aspects regarding the user interface: How the recording is done (i.e., how the presenter or his/her assistants have to interact with the recording tools (hardware and software)), and how users can access the produced multimedia documents (i.e., how replay, as well as search and navigation in the files can be done comfortably and efficiently). In this demonstration, we will illustrate Authoring on the Fly (AOF), a system for presentation recording which provides new approaches and solutions for both of these issues.
Preprint Typeset in Jhep Style -paper Version

We complete the investigation of N = (2, 2) supersymmetric nonlinear σ-models in the presence of a boundary. We study the full bihermitian geometry parameter-ized by chiral, twisted chiral and semi-chiral superfields and identify the D-brane configurations preserving an N = 2 worldsheet supersymmetry. Combining twisted with semi-chiral superfields leads to a clearly defined notion of lagrangian and coisotropic branes generalizing lagrangian and coisotropic A-branes on Kähler manifolds to manifolds which are not necessarily Kähler (but still bihermitian). Adding chiral fields complicates the picture and results in hybrid configurations interpolating between lagrangian/coisotropic branes and branes wrapping around a holomorphic cycle. Even here the branes can be viewed as coisotropic submanifolds albeit in a generalized sense. All supersymmetric D-brane configurations are characterized in the context of generalized complex geometry. Duality transformations interchanging the various types of superfields while preserving all super-symmetries are explicitly constructed and provide for a powerful technique to construct various highly non-trivial D-brane configurations. Several explicit examples are given.
Improvement of texture image segmentation based on visual model

As an important aspect of image segmentation, texture segmentation has long been one of the hot spots in segmentation field. In this paper, the human visual cognitive model is used as a new texture feature extraction method for image texture segmentation. In order to promote the effect of clustering segmentation method, spatial location information is considered to smooth the segment result. Experiments show that the proposed texture feature descriptor with visual cognitive model is more conducive than that of the Gabor feature.
Association between Proton Pump Inhibitor Therapy and Clostridium difficile Infection: A Contemporary Systematic Review and Meta-Analysis

Introduction Emerging epidemiological evidence suggests that proton pump inhibitor (PPI) acid-suppression therapy is associated with an increased risk of Clostridium difficile infection (CDI). Methods Ovid MEDLINE, EMBASE, ISI Web of Science, and Scopus were searched from 1990 to January 2012 for analytical studies that reported an adjusted effect estimate of the association between PPI use and CDI. We performed random-effect meta-analyses. We used the GRADE framework to interpret the findings. Results We identified 47 eligible citations (37 case-control and 14 cohort studies) with corresponding 51 effect estimates. The pooled OR was 1.65, 95% CI (1.47, 1.85), I2 = 89.9%, with evidence of publication bias suggested by a contour funnel plot. A novel regression based method was used to adjust for publication bias and resulted in an adjusted pooled OR of 1.51 (95% CI, 1.26–1.83). In a speculative analysis that assumes that this association is based on causality, and based on published baseline CDI incidence, the risk of CDI would be very low in the general population taking PPIs with an estimated NNH of 3925 at 1 year. Conclusions In this rigorously conducted systemic review and meta-analysis, we found very low quality evidence (GRADE class) for an association between PPI use and CDI that does not support a cause-effect relationship.
Algorithms for Molecular Biology

1 The following lecture briefly overviews selected topics from the general domain of " functional genomics " (with one deviation to SBH-sequencing by hybridization). Functional Genomics is at the front line of todays computational biology, dealing with the understanding of biological systems when the DNA sequence is already known. Computational biology (or bioinformatics) emerged as a critical component in the scientific machinery assembled to reconstruct large genomes DNA sequences. In the post-genomic era, when the complete DNA sequence of many organisms is available, we begin using computational techniques to answer the most fundamental questions of biology : what is the function of each and any gene and gene product in the cell, how does these factors react to different conditions and how do they interact to process information and create a biological system. In what follows we shall overview some techniques and describe some of the important problems we study today. We begin by an introduction to DNA chips which are part of the technological revolution enabling post-genomic high throughput biology. We then turn to one of the basic problems in analyzing DNA chips experiments, clustering gene expression data, provide some terminology and notion and describe CLICK, a clustering algorithm developed for gene expression analysis. Following are some examples on applications of clustering to study concrete biological questions. In the last part of this lecture we introduce gene networks and overview the difficulties and possibilities in the computational study of biological systems as a whole. We primarily outline the problems and present the more promising directions in contemporary research. Having (almost) reached the end of Human Genome Project, the question that needs to be asked is: " What's next? ". The complete sequencing of the Human Genome is an immense task, which is now declared as being completed (and is actually nearing completion). While much work remains to be done even there, the existence of the full DNA sequence of an organism represents only the beginning of a long journey toward the understanding of living organisms. " functional genomics " is the study of the functionality of specific genes, their 1 Based on a scribe by Ronny Morad and Tal Moran, Fall 2000.
Reliable detection of core and delta in fingerprints by using singular candidate method

The singular points of fingerprints, namely, core and delta, are important referential points for the classification of fingerprints. Several conventional approaches such as the Poincare index method have been proposed; however, these approaches cannot achieve the reliable detection of poor-quality fingerprints. In this paper, we propose a new core and delta detection method by singular candidate analysis using an extended relational graph. In order to use both the local and global features of the ridge direction patterns and to realize a method with high tolerance to local image noise, singular candidate analysis is adopted in the detection process; this analysis involves the extraction of locations in which the probability of the existence of a singular point is high. The experimental results show that the success rate of this approach is higher than that of the Poincare index method by 10% for singularity detection using the fingerprint image databases FVC2000 and FVC2002. These databases contain several poor quality images, even though the average computation time is 15%-30% greater than the Poincare index method.