Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Scan-Line Optimization for 3D Medical Image Registration

Scan-line optimization via cost accumulation has become very popular for stereo estimation in computer vision applications and is often combined with a semi-global cost integration strategy, known as SGM. This paper introduces this combination as a general and effective optimization technique. It is the first time that this concept is applied to 3D medical image registration. The presented algorithm, SGM-3D, employs a coarse-to-fine strategy and reduces the search space dimension for consecutive pyramid levels by a fixed linear rate. This allows it to handle large displacements to an extent that is required for clinical applications in high dimensional data. SGM-3D is evaluated in context of pulmonary motion analysis on the recently extended DIR-lab benchmark that provides ten 4D computed tomography (CT) image data sets, as well as ten challenging 3D CT scan pairs from the COPDgene study archive. Results show that both registration errors as well as run-time performance are very competitive with current state-of-the-art methods.
Does the prognosis of colorectal mucinous carcinoma depend upon the primary tumour site? Results from two independent databases

Mucinous adenocarcinoma (MUC) is a commonly studied histological subtype of colorectal adenocarcinoma. However, the prognostic value of MUC remains unclear, particularly in patients stratified by the primary tumour site. We aimed to analyse the prognostic value of MUC in colorectal cancer.
Improvement of the Exploration of the Search Space of a Top-Down Algorithm: Theoretical and Experimental Results

We study here concept learning from instances described as attribute-value lists in the frame of Top-Down induction algorithms. The goal of this work is to use known or unknown logical implications which are true on the instances in order to improve the exploration of a search space. We base our work on a lattice formalization (a lattice of instances and a lattice of terms) to show how one can improve the exploration. In particular, we show that a propositional representation is interesting, even in the case of structured attributes, if one takes into account the logical implications. Several experiments on a CN-like algorithm are reported.
Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results

Machine translation between Arabic and Hebrew has so far been limited by a lack of parallel corpora, despite the political and cultural importance of this language pair. Previous work relied on manually-crafted grammars or pivoting via English, both of which are unsatisfactory for building a scalable and accurate MT system. In this work, we compare standard phrase-based and neural systems on Arabic-Hebrew translation. We experiment with tokenization by external tools and sub-word modeling by character-level neural models, and show that both methods lead to improved translation performance, with a small advantage to the neural models.
Computer Vision-Based Identification of Individual Turtles Using Characteristic Patterns of Their Plastrons

The identification of pond turtles is important to scientists who monitor local populations, as it allows them to track the growth and health of subjects over their lifetime. Traditional non-invasive methods for turtle recognition involve the visual inspection of distinctive coloured patterns on their plastron. This visual inspection is time consuming and difficult to scale with a potential growth in the surveyed population. We propose an algorithm for automatic identification of individual turtles based on images of their plastron. Our approach uses a combination of image processing and neural networks. We perform a convexity-concavity analysis of the contours on the plastron. The output of this analysis is combined with additional region-based measurements to compute feature vectors that characterize individual turtles. These features are used to train a neural network. Our goal is to create a neural network which is able to query a database of images of turtles of known identity with an image of an unknown turtle, and which outputs the unknown turtle's identity. The paper provides a thorough experimental evaluation of the proposed approach. Results are promising and point towards future work in the area of standardized image acquisition and image denoising.
Experiencing Liveness of a Cherished Place in the Home

Liveness, as discussed in HCI and in media studies, focuses on an intriguing and beloved experiential quality that can influence new forms of video applications. We suggest a shift from accounts of liveness in "events" to liveness in ambient media for home décor by designing a system called TransLive that exploits the "magic" of mediatizing the "now" at a distant and cherished place. We present an interview study including four families, who experienced the system for two weeks each in a concept apartment setting. It shows how immediacy and unpredictability provide compelling experiences. Authenticity and engagement, which are previously considered as inherent qualities in live media, instead occur in the context of use. Finally, the experience of transcendence triggered by slow and continuous video streams open up a new design space of liveness. Thus, not only do we take inspiration from liveness theory, but we also need to redefine it.
Indexing without Invariants in 3D Object Recognition

We present a method of indexing 3D objects from single 2D images. The method does not rely on invariant features. This allows a richer set of shape information to be used in the recognition process. We also suggest the kd-tree as an alternative indexing data structure to the standard hash table. This makes hypothesis recovery more efficient in high-dimensional spaces, which are necessary to achieve specificity in large model databases. Search efficiency is maintained in these regimes by the use of best-bin first search. Neighbors recovered from the index are used to generate probability estimates, local within the feature space, which are then used to rank hypotheses for verification. On average, the ranking process greatly reduces the number of verifications required. Our approach is general in that it can be applied to any real-valued feature vector. In addition, it is straightforward to add to our index information from real images regarding the true probability distributions of the feature groupings used for indexing.
Investigation of the spatial structure of a high-power fiber laser beam

Analysis of experimental results and theoretical studies has confirmed that the free- space propagation and the transformation of a high-power fibre laser beam by an optical system can be described by the theory of laser optics. The need to use the theory of laser optics to develop modern high-performance optical systems of laser processing heads, which form the beam of the high-power fibre laser, is discussed.
Electrogenic nature of lysosomal proton pump as revealed with a cyanine dye.

Studies were carried out on the electrogenicity of the lysosomal proton pump using dipropylthiadicarbocyanine iodide (diS-C3-(5] as a membrane potential probe. Pure lysosome preparations (tritosomes) quenched the fluorescence of diS-C3-(5). The quenching correlated well with the potassium ion diffusion potential (inside negative) generated by K+ with or without valinomycin. The quenching caused by lysosomes was reversed by lipophilic cations, tetraphenylarsonium (TPA) or triphenylmethylphosphonium (TPMP). Mg-ATP also reversed the quenching, which was inhibited by a protonophore, 3,5-di-tert-butyl-4-hydroxybenzylidene-malononitrile (SF-6847). The properties of the ATP-induced recovery of the quenching were exactly the same as those of ATP-induced acidification, as measured with fluorescein isothiocyanate-dextran (FD) (Ohkuma, S., et al. (1982) Proc. Natl. Acad. Sci. U.S. 79, 2758-2762) and acridine orange (Moriyama, Y., et al. (1982) J. Biochem. 92, 1333-1336), except replacement of the anion by an impermeable one enhanced ATP-induced recovery of quenching, but reduced ATP-induced acidification. Amines which dissipate delta pH across the lysosomal membrane also enhanced the Mg-ATP-induced fluorescence recovery. These results suggest that isolated lysosomes exhibit an inside negative membrane potential, especially in low K+ medium, mostly due to the K+-diffusion potential, and that the Mg-ATP-driven proton pump causes membrane depolarization (in the direction of inside positive). These possibilities were supported by results on the uptake of the radioactive membrane-permeant ions [3H]TPMP and [14C]SCN. The present results provide evidence for the electrogenic nature of the lysosomal proton pump.
Self-configuring object-to-relational mapping queries

Object-to-relational maps are nowadays routinely utilized in providing a persistency mechanism for object-oriented programs. We present how an object-to-relational mapper, such as Hibernate, provides transparent persistency to object-oriented programs. We then show how the existing approaches of eager and lazy fetching of associations are problematic under the face of program evolution. As an improvement, we present self-configuring components, which reflectively configure the persistency layer usage sites, thus leading to improved maintainability of software. A self-configuring component analyses the actual persistency layer usage pattern. Based on this information, the actual queries are configured. A general algorithm for retrieving the actual usage pattern is given. As an implementation mechanism, we discuss different approaches for the self-configuring components. Practical choices for implementation can vary between analysis of abstract syntax trees and Java byte-code analysis. Suitability of two byte-code analysis frameworks, namely BCEL and Soot are evaluated.
X-ARM: an asset representation model for component repository systems

Nowadays, one of the most challenging tasks in component-based software development processes is to discover suitable reusable assets that fulfill the requirements of a particular software system under development. In such a context, this paper presents an XML-based asset representation model for describing all kinds of software assets that can be produced or reused within component-based software development processes. The proposed model is based on asset metadata that provides effective means for developing universal repository systems that can deal with discovery, retrieval and composition of software components.
Investigating the usability of a mobile location-based annotation system

We investigate the usability of MobiTOP (Mobile Tagging of Objects and People), a mobile location-based annotation system. MobiTOP allows users to annotate real world locations with both multimedia and textual content and concurrently, share the annotations among its users. Equipped with a new interface as well as additional functionality such as clustering of annotations and advanced search and filtering options, a usability evaluation of MobiTOP was conducted in the context of a travel companion for tourists. The results suggested the potential of the system in terms of functionality for mobile content sharing. Participants agree that the features in MobiTOP are generally usable as a content sharing tool. Implications and future work are also reported in this paper.
Learning to Propagate Rare Labels

Label propagation is a well-explored family of methods for training a semi-supervised classifier where input data points (both labeled and unlabeled) are connected in the form of a weighted graph. For binary classification, the performance of these methods starts degrading considerably whenever input dataset exhibits following characteristics - (i) one of the class label is rare label or equivalently, class imbalance (CI) is very high, and (ii) degree of supervision (DoS) is very low -- defined as fraction of labeled points. These characteristics are common in many real-world datasets relating to network fraud detection. Moreover, in such applications, the amount of class imbalance is not known a priori. In this paper, we have proposed and justified the use of an alternative formulation for graph label propagation under such extreme behavior of the datasets. In our formulation, objective function is the difference of two convex quadratic functions and the constraints are box constraints. We solve this program using Concave-Convex Procedure (CCCP). Whenever the problem size becomes too large, we suggest to work with a k-NN subgraph of the given graph which can be sampled by using Locality Sensitive Hashing (LSH) technique. We have also discussed various issues that one typically faces while sampling such a k-NN subgraph in practice. Further, we have proposed a novel label flipping method on top of the CCCP solution, which improves the result of CCCP further whenever class imbalance information is made available a priori. Our method can be easily adopted for a MapReduce platform, such as Hadoop. We have conducted experiments on 11 datasets comprising a graph size of up to 20K nodes, CI as high as 99:6%, and DoS as low as 0:5%. Our method has resulted up to 19:5-times improvement in F-measure and up to 17:5-times improvement in AUC-PR measure against baseline methods.
Automatic Error Correction Using P 3 Response Verification for a Brain-Computer Interface

Recent advances in signal processing have made brain-computer interfaces (BCIs) feasible for use as an alternative control option for those with severe motor impairments. It is possible to increase the accuracy of a BCI system by requiring users to make multiple choices to control a single item, but this comes at a cost of reduced system speed. We present experimental results from an evoked potential BCI used to control items in a virtual apartment and show the existence of the evoked potential P3 component in responses to successfully controlled goal items. A reduced response exists when items are accidentally controlled. The presence of the P3 component in responses to goal items means that it can be used for automatic error correction. Off-line experiments have been run and with a theoretical mean improvement in recognition from 78% to 85%, we show a statistically significant improvement (p < 0.004, Wilcoxen test, two-tailed) in accuracy of 3% using the variable averaging algorithm. Since the error correction technique depends on responses to controlled items in a particular experimental paradigm rather than the control of the items themselves, it may be possible to use this response in non-evoked potential BCI systems. Future work is discussed in this context.
Single Low-Density Lipoprotein Apheresis Does Not Improve Vascular Endothelial Function in Chronically Treated Hypercholesterolemic Patients

Objective. To investigate vascular endothelial function (VEF) responses to a single low-density lipoprotein (LDL) apheresis session in hypercholesterolemic patients undergoing chronic treatment. Methods. We measured brachial artery flow-mediated dilation (FMD), plasma lipids, vitamin E (α- and γ-tocopherol), markers of oxidative/nitrative stress (malondialdehyde (MDA) and nitro-γ-tocopherol (NGT)), and regulators of NO metabolism (arginine (ARG) and asymmetric dimethylarginine (ADMA)) prior to (Pre) and immediately following (Post) LDL apheresis and at 1, 3, 7, and 14 d Post in 5 hypercholesterolemic patients (52 ± 11 y). Results. Relative to Pre, total cholesterol (7.8 ± 1.5 mmol/L) and LDL-cholesterol (6.2 ± 1.2 mmol/L) were 61% and 70% lower (P < 0.01), respectively, at Post and returned to Pre levels at 14 d. Brachial FMD responses (6.9 ± 3.6%) and plasma MDA, ARG, and ADMA concentrations were unaffected by LDL apheresis. Plasma α-tocopherol, γ-tocopherol, and NGT concentrations were 52–69% lower at Post (P < 0.01), and α-tocopherol remained 36% lower at 1 d whereas NGT remained 41% lower at d 3. Conclusions. Acute cholesterol reduction by LDL apheresis does not alter VEF, oxidative stress, or NO homeostasis in patients treated chronically for hypercholesterolemia.
22nd International Conference on Computational Linguistics Proceedings of the Workshop on Cross-Framework and Cross-Domain

This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding. For such purposes, we argue that dependency schemes must follow a simple design and provide semantically contentful information, as well as offer an automatic procedure to extract the relations. We consider the underlying design principles of the Stanford scheme from this perspective, and compare it to the GR and PARC representations. Finally, we address the question of the suitability of the Stanford scheme for parser evaluation.
Shared memory computing on clusters with symmetric multiprocessors and system area networks

Cashmere is a software distributed shared memory (S-DSM) system designed for clusters of server-class machines. It is distinguished from most other S-DSM projects by (1) the effective use of fast user-level messaging, as provided by modern system-area networks, and (2) a “two-level” protocol structure that exploits hardware coherence within multiprocessor nodes. Fast user-level messages change the tradeoffs in coherence protocol design; they allow Cashmere to employ a relatively simple directory-based coherence protocol. Exploiting hardware coherence within SMP nodes improves overall performance when care is taken to avoid interference with inter-node software coherence.We have implemented Cashmere on a Compaq AlphaServer/Memory Channel cluster, an architecture that provides fast user-level messages. Experiments indicate that a one-level, version of the Cashmere protocol provides performance comparable to, or slightly better than, that of TreadMarks' lazy release consistency. Comparisons to Compaq's Shasta protocol also suggest that while fast user-level messages make finer-grain software DSMs competitive, VM-based systems continue to outperform software-based access control for applications without extensive fine-grain sharing.Within the family of Cashmere protocols, we find that leveraging intranode hardware coherence provides a 37% performance advantage over a more straightforward one-level implementation. Moreover, contrary to our original expectations, noncoherent hardware support for remote memory writes, total message ordering, and broadcast, provide comparatively little in the way of additional benefits over just fast messaging for our application suite.
DMS 15-721 Project Report

Indexing structures of modern multimedia databases almost always use a predefined measure of distance, which is usually independent of the data. However, knowing how a given data point relates to other data points in the database can often yield insight into what data points should be considered similar. Computationally expensive data mining operations allow users to classify data into clusters. However, these operations require several passes through data, and classifications can be made obsolete when data is inserted, deleted, or modified. As a response to this problem, we developed and compared two indexing schemes that facilitate cluster-based queries.
Wakame: sense making of multi-dimensional spatial-temporal data

As our ability to measure the world around us improves, we are quickly generating massive quantities of high-dimensional, spatial-temporal data. In this paper, we concern ourselves with datasets in which the spatial characteristics are relatively static but many dimensions prevail and data is sampled over different time periods. Example applications include building energy management and HVAC unit diagnostics. We present methods employed in our Wakame visualization system to support such tasks as discovering anomalies and comparing performance across multiple time series. Novel methods include animated transitions that relate data in spatially located 3D views with conventional 2D graphs. Additionally, several components of our prototype employ analytics to guide the user to "interesting" portions of the dataset.
Construction of a vector, pRSVcatamb38, for the rapid and sensitive assay of amber suppression in human and other mammalian cells.

We describe the generation of an amber mutation in the chloramphenicol acetyltransferase (cat) gene of the mammalian cell transfection vector pRSVcat (Gorman et.al. (1982), Proc.Natl. Acad.Sci. 79 6777-6791). We have demonstrated the in vivo suppression of this amber mutation in monkey and human cells by co-transfection with a synthetic Xenopus suppressor tRNATyr under the control of the late SV40 promoter. The vector, pRSVcatamb38, may be used to quantitate amber suppression in various mammalian cells.
1-to-Many and many-to-1 communication in hybrid wireless network-on-chip

To alleviate the complex communication problems arising in the network-on-chip (NoC) architectures as the number of on-chip components increases, wireless network-on-chip (WiNoC) is among the most promising scalable interconnection architectures for future generation NoCs. Since 1-to-many and many-to-1 communications caused by the cache coherence protocol in the multiprocessor system have high demands on the network, we propose several optimization schemes to improve both 1-to-many and many-to-1 communications in the hybrid WiNoC. Experimental results show that our optimized hybrid WiNoC can reduce the network latency by ~50% on average.
Using prior knowledge to improve genetic network reconstruction from microarray data

The use of Bayesian Network methods to recover transcriptional regulatory networks from static microarray data is an active area of bioinformatics research. However, early work in this area lacked realistic analysis of the effects of data set size on learning performance and ignored the potentially immense benefits of using prior biological knowledge. More recent work which has utilized such information has tended to focus on qualitative descriptions of the results. In this paper, we construct a detailed, realistic model for glucose homeostasis and use this model to generate static, synthetic gene expression data. We then use a Bayesian Network method to reconstruct this genetic network from the synthetic microarray data utilizing various amounts and types of prior knowledge. By quantitatively analyzing the effects of data set size and the incorporation of different types of prior biological knowledge on our ability to reconstruct the original network, we show that characteristic portions of genetic networks can be reconstructed from microarray data. Incorporating prior knowledge into the learning scheme greatly reduces the data required, allowing these reverse engineering techniques to be used to learn regulatory interactions from microarray data sets of realistic size.