Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Image Matching Method based on Optimized BaySAC

This paper proposes a robust image-matching method, which integrates SIFT with the optimized Bayes SAmpling Consensus (BaySAC). As the point correspondences are likely contaminated by outliers, we present a novel robust estimation method involving an efficient BaySAC for eliminating falsely accepted correspondences. The key points of the proposed hypothesis testing algorithm are determining and updating the prior probabilities of pseudo-correspondences. First, we propose a strategy for prior probability determination in terms of the statistical characteristics of a deterministic mathematical model for hypothesis testing. Moreover, the inlier probability updating is simplified based on a memorable form of Bayes’ Theorem. The proposed approach is validated on a variety of image pairs. The results indicate that when compared with the performance of RANdom SAmpling Consensus (RANSAC) and the original BaySAC, the proposed optimized BaySAC consumes less computation and obtains higher matching accuracy when the hypothesis set is contaminated with more outliers.
Se quence de ter mi na tion and anal y sis of S-adenosyl-L-homo - cysteine hydrolase from yel low lu pine (Lupinus luteus) *.

The cod ing se quences of two S-adenosyl-L-homocysteine hy dro las es (SAHases) were iden ti fied in yel low lu pine by screenig of a cDNA li brary. One of them, cor re spond ing to the com plete pro tein, was se quenced and com pared with 52 other SAHase se quences. Phylo gen etic anal y sis of these pro teins iden ti fied three groups of the en zymes. Group A com prises only bac te rial se quences. Group B is sub di vided into two sub groups, one of which (B1) is formed by an i mal se quences. Sub group B2 con sist of two dis tinct clus ters, B2a and B2b. Clus ter B2b com prises all known plant se quences, in clud ing the yel low lu pine en zyme, which are dis tin guished by a 50-residue in sert. Group C is het er o ge neous and contains SAHases from Archaea as well as a new class of an i mal en zymes, dis tinctly dif fer ent from those in group B1.
Improving Text Clustering with Social Tagging

In this paper we study the use of social bookmarking to improve the quality of text clustering. Recently constrained clustering algorithms have been presented as a successful tool to introduce domain knowledge in the clustering process. This paper uses the tags saved by the users of Delicious to generate non artificial constraints for constrained clustering algorithms. The study demonstrates that it is possible to achieve a high percentage of good constraints with this simple approach and, more importantly, the evaluation shows that the use of these constraints produces a great improvement (up to 91.25%) of the clustering algorithms effectiveness.
Clinical and pathological continuum of multisystem TDP-43 proteinopathies.

OBJECTIVE To determine the extent of transactivation response DNA-binding protein with a molecular weight of 43 kDa (TDP-43) pathology in the central nervous system of patients with clinically and autopsy-confirmed diagnoses of frontotemporal lobar degeneration with and without motor neuron disease and amyotrophic lateral sclerosis with and without cognitive impairment. DESIGN Performance of immunohistochemical whole-central nervous system scans for evidence of pathological TDP-43 and retrospective clinical medical record review. SETTING An academic medical center. PARTICIPANTS We included 64 patients with clinically and pathologically confirmed frontotemporal lobar degeneration with ubiquitinated inclusions with or without motor neuron disease and amyotrophic lateral sclerosis with or without cognitive impairment. MAIN OUTCOME MEASURE Neuronal and glial TDP-43 pathology. RESULTS We found evidence of neuronal and glial TDP-43 pathology in all disease groups throughout the neuraxis, albeit with variations in the frequency, morphology, and distribution of TDP-43 lesions. Moreover, the major clinical manifestations (eg, cognitive impairments, motor neuron signs, extrapyramidal symptoms, neuropsychiatric features) were reflected by the predominant distribution and burden of TDP-43 pathology. CONCLUSION These findings strongly suggest that amyotrophic lateral sclerosis, frontotemporal lobar degeneration with amyotrophic lateral sclerosis or motor neuron disease, and frontotemporal lobar degeneration with ubiquitinated inclusions are different manifestations of a multiple-system TDP-43 proteinopathy linked to similar mechanisms of neurodegeneration.
The long time-scale X-ray variability of the radio-quiet quasar PG 0804+761

We present the results from a study of the timing properties and the energy spectrum of the radio-quiet quasar PG 0804+761, based on monitoring RXTE/PCA observations that lasted for a year. This is a systematic study of the X-ray variations on time-scales of weeks/months of the most luminous radio-quiet quasar studied so far. We detect significant variations in the 2-10 keV band of an average amplitude of ∼15 per cent. The excess variance of the light curve is smaller than that of Seyfert galaxies, entirely consistent with the relationship between variability amplitude and luminosity defined from the Seyfert data alone. The power spectrum of the RXTE light curve follows a power-like form of slope ∼-1. However, when we extend the power spectrum estimation at higher frequencies using archival ASCA data, we find strong evidence for an intrinsic steepening to a slope of ∼-2 at around ∼1 × 10 - 6 Hz. This 'break frequency' corresponds to a time-scale of ∼10 d. The time-average energy spectrum is well fitted by a Γ ∼2 power-law model. We also find evidence for an iron line at ∼6.4 keV (rest frame) with EW ∼ 110 eV, similar to what is observed in Seyfert galaxies. The flux variations are not associated with any spectral variation. This is the only major difference that we find when we compare the variability properties of PG 0804+761 with those of Seyfert galaxies. Our results support the hypothesis that the same X-ray emission and variability mechanism operates in both Seyfert galaxies and quasars.
Discovering SIFIs in Interbank Communities

This paper proposes a new methodology based on non-negative matrix factorization to detect communities and to identify central nodes in a network as well as within communities. The method is specifically designed for directed weighted networks and, consequently, it has been applied to the interbank network derived from the e-MID interbank market. In an interbank network indeed links are directed, representing flows of funds between lenders and borrowers. Besides distinguishing between Systemically Important Borrowers and Lenders, the technique complements the detection of systemically important banks, revealing the community structure of the network, that proxies the most plausible areas of contagion of institutions’ distress.
Low Thalamic NAA-Concentration Corresponds to Strong Neural Activation in Working Memory in Kleine-Levin Syndrome

Background Kleine Levin Syndrome (KLS) is a rare disorder of periodic hypersomnia and behavioural disturbances in young individuals. It has previously been shown to be associated with disturbances of working memory (WM), which, in turn, was associated with higher activation of the thalamus with increasing WM load, demonstrated with functional magnetic resonance imaging (fMRI). In this study we aimed to further elucidate how these findings are related to the metabolism of the thalamus. Methods fMRI and magnetic resonance spectroscopy were applied while performing a WM task. Standard metabolites were examined: n-acetylaspartate (NAA), myo-inositol, choline, creatine and glutamate-glutamine. Fourteen KLS-patients and 15 healthy controls participated in the study. The patients with active disease were examined in asymptomatic periods. Results There was a statistically significant negative correlation between thalamic fMRI-activation and thalamic NAA, i.e., high fMRI-activation corresponded to low NAA-levels. This correlation was not seen in healthy controls. Thalamic levels of NAA in patients and controls showed no significant differences between the groups. None of the other metabolites showed any co-variation with fMRI-activiation. Conclusion This study shows negative correlation between NAA-levels and fMRI-activity in the left thalamus of KLS-patients while performing a WM task. This correlation could not be found in healthy control subjects, primarily interpreted as an effect of increased effort in the patient group upon performing the task. It might indicate a disturbance in the neuronal networks responsible for WM in KLS patients, resulting in higher effort at lower WM load, compared with healthy subjects. The general relationship between NAA and BOLD-signal is also discussed in the article.
A replica control method for improving availability for read-only transactions

Data replication is often considered in distributed database systems to enhance availability and performance. The benefit of data replication, however can only be realized at the cost of maintaining the consistency of data. In particular, network partition failures make it more difficult to achieve high data availability while ensuring strong correctness criteria such as l-copy serializability. We propose a replica control method to improve the availability of data in the presence of network partition failures. Our method extends the traditional primary copy method by using the relaxed correctness criterion called insular consistency for large-scale distributed systems, where partition failures frequently occur. We focus on increasing the availability of data for read-only transactions. We introduce a version vector as a tool for guaranteeing insular consistency and present a mechanism that allows read-only transactions to be executed at any partition as long as the insular consistency is satisfied. An asynchronous update propagation mechanism is also employed to improve the performance of update operations. We also show that the proposed method is correct and give some performance considerations.
CC-SLIQ : Performance Enhancement with 2 k Split Points in SLIQ Decision Tree Algorithm

Decision trees have been found to be very effective for classification in the emerging field of data mining. This paper proposes a new method: CC-SLIQ (Cascading Clustering and Supervised Learning In Quest) to improve the performance of the SLIQ decision tree algorithm. The drawback of the SLIQ algorithm is that in order to decide which attribute is to be split at each node, a large number of Gini indices have to be computed for all attributes and for each successor pair for all records that have not been classified. SLIQ employs a presorting technique in the tree growth phase that strongly affects its ability to find the best split at a decision tree node. However, the proposed model eliminates the need to sort the data at every node of the decision tree; as an alternative the training data uses a k-means clustering data segmentation only once for every numeric attribute at the beginning of the tree growth phase. The CC-SLIQ algorithm inexpensively evaluates split points that are twice the cluster size k and results in a compact and accurate tree, scalable for large datasets as well as classified datasets with a large number of attributes, classes, and records. The classification accuracy of this technique has been compared to the existing SLIQ and Elegant decision tree methods on a large number of datasets from the UCI machine learning and Weather Underground repository. The experiments show that the proposed algorithm reduces the computation of split points by 95.62%, decision rules generated by 56.5% and also leads to better mean classification accuracy of 79.29%, thus making it a practical tool for data mining.
Randomized Composable Core-sets for Distributed Submodular Maximization

An effective technique for solving optimization problems over massive data sets is to partition the data into smaller pieces, solve the problem on each piece and compute a representative solution from it, and finally obtain a solution inside the union of the representative solutions for all pieces. This technique can be captured via the concept of composable core-sets, and has been recently applied to solve diversity maximization problems as well as several clustering problems [7,15,8]. However, for coverage and submodular maximization problems, impossibility bounds are known for this technique [15]. In this paper, we focus on efficient construction of a randomized variant of composable core-sets where the above idea is applied on a random clustering of the data. We employ this technique for the coverage, monotone and non-monotone submodular maximization problems. Our results significantly improve upon the hardness results for non-randomized core-sets, and imply improved results for submodular maximization in a distributed and streaming settings. The effectiveness of this technique has been confirmed empirically for several machine learning applications [22], and our proof provides a theoretical foundation to this idea. In summary, we show that a simple greedy algorithm results in a 1/3-approximate randomized composable core-set for submodular maximization under a cardinality constraint. Our result also extends to non-monotone submodular functions, and leads to the first 2-round MapReduce-based constant-factor approximation algorithm with O(n) total communication complexity for either monotone or non-monotone functions. Finally, using an improved analysis technique and a new algorithm PseudoGreedy, we present an improved 0.545-approximation algorithm for monotone submodular maximization, which is in turn the first MapReduce-based algorithm beating factor 1/2 in a constant number of rounds.
Early Steps in the Unfolding of Thermolysin-like Proteases*

Several series of site-directed mutations in thermolysin-like proteases are presented that show remarkable nonadditivity in their effect on thermal stability. A simple model is proposed that relates this nonadditivity to the occurrence of independent partial unfolding processes that occur in parallel at elevated temperatures. To prove this model, a thermolysin-like protease was designed in which two mutations located ∼35 Å apart in the structure individually exert small stabilizing effects of 2.3 and 4.1 °C, respectively, but when combined stabilize the protease by 14.6 °C. This overadditivity, which follows directly from the model, confirms that unfolding of this engineered protease starts in parallel at two different regions of the protein.
Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays

An essay-based discourse analysis system can help students improve their writing by identifying relevant essay-based discourse elements in their essays. Our discourse analysis software, which is embedded in Criterion, an online essay evaluation application, uses machine learning to identify discourse elements in student essays. The system makes decisions that exemplify how teachers perform this task. For instance, when grading student essays, teachers comment on the discourse structure. Teachers might explicitly state that the essay lacks a thesis statement or that an essay's single main idea has insufficient support. Training the systems to model this behavior requires human judges to annotate a data sample of student essays. The annotation schema reflects the highly structured discourse of genres such as persuasive writing. Our discourse analysis system uses a voting algorithm that takes into account the discourse labeling decisions of three independent systems.
Toward ‘normal’ politics? Security, parliaments and the politicisation of intelligence oversight in the German Bundestag

Security has often been considered a special kind of politics that presents a particular challenge for liberal democracy, whether due to securitised states of exception or technocratic risk management. This article examines whether and how parliaments can be sites of politicisation, moving security from exceptional and technocratic politics toward more ‘normal’ democratic politics. Moving beyond the narrow focus on decisions over the use of military force, the examination focuses on the ‘hard case’ of parliamentary oversight of intelligence agencies and provides a case study on the German Bundestag. Overall, it finds that a strict divide between security and normal politics is overly simplistic, even when it comes to intelligence. There is evidence for politicisation that reveals patterns of normal politics through an increasingly institutionalised framework as well as public, increasingly controversial debates, including a good deal of partisan politics. However, debates tend to center on institutional and legal issues as well as symbolic skirmishes after specific events of high visibility, while many restrictions are deeply entrenched in parliamentary conventions and attitudes.
Acetylation dynamics and stoichiometry in Saccharomyces cerevisiae

Lysine acetylation is a frequently occurring posttranslational modification; however, little is known about the origin and regulation of most sites. Here we used quantitative mass spectrometry to analyze acetylation dynamics and stoichiometry in Saccharomyces cerevisiae. We found that acetylation accumulated in growth‐arrested cells in a manner that depended on acetyl‐CoA generation in distinct subcellular compartments. Mitochondrial acetylation levels correlated with acetyl‐CoA concentration in vivo and acetyl‐CoA acetylated lysine residues nonenzymatically in vitro. We developed a method to estimate acetylation stoichiometry and found that the vast majority of mitochondrial and cytoplasmic acetylation had a very low stoichiometry. However, mitochondrial acetylation occurred at a significantly higher basal level than cytoplasmic acetylation, consistent with the distinct acetylation dynamics and higher acetyl‐CoA concentration in mitochondria. High stoichiometry acetylation occurred mostly on histones, proteins present in histone acetyltransferase and deacetylase complexes, and on transcription factors. These data show that a majority of acetylation occurs at very low levels in exponentially growing yeast and is uniformly affected by exposure to acetyl‐CoA.
Diagnosis and Tolerant Strategy of an Open-Switch Fault for T-Type Three-Level Inverter Systems

This paper proposes a new diagnosis method of an open-switch fault and fault-tolerant control strategy for T-type three-level inverter systems. The location of the faulty switch can be identified by the average of the normalized phase current and the change of the neutral-point voltage. The proposed fault-tolerant strategy is explained by dividing into two cases: the faulty condition of half-bridge switches and neutral-point switches. The performance of the T-type inverter system improves considerably by the proposed fault-tolerant algorithm when a switch fails. The proposed method does not require additional components and complex calculations. Simulation and experimental results verify the feasibility of the proposed fault diagnosis and fault-tolerant control strategy.
Multiresolution Shape Deformations for Meshes with Dynamic Vertex Connectivity

Multiresolution shape representation is a very effective way to decompose surface geometry into several levels of detail. Geometric modeling with such representations enables flexible modifications of the global shape while preserving the detail information. Many schemes for modeling with multiresolution decompositions based on splines, polygonal meshes and subdivision surfaces have been proposed recently. In this paper we modify the classical concept of multiresolution representation by no longer requiring a global hierarchical structure that links the different levels of detail. Instead we represent the detail information implicitly by the geometric difference between independent meshes. The detail function is evaluated by shooting rays in normal direction from one surface to the other without assuming a consistent tesselation. In the context of multiresolution shape deformation, we propose a dynamic mesh representation which adapts the connectivity during the modification in order to maintain a prescribed mesh quality. Combining the two techniques leads to an efficient mechanism which enables extreme deformations of the global shape while preventing the mesh from degenerating. During the deformation, the detail is reconstructed in a natural and robust way. The key to the intuitive detail preservation is a transformation map which associates points on the original and the modified geometry with minimum distortion. We show several examples which demonstrate the effectiveness and robustness of our approach including the editing of multiresolution models and models with texture.
Improved modified active clamp circuit for a current fed DC/DC power converter

In this paper the performance of an improved modified active clamp circuit (MACC) for an isolated full bridge current-fed DC/DC converter (FBCFC) is presented. The new active clamp circuit reduces power losses in the switching devices by shaping the bridge currents, leading to a more efficient converter. The operation of the new circuit is described in detail. For comparison with a conventional active clamp, the modified active clamp circuit has been simulated and implemented in a prototype 1.2 kW FBCFC. The simulations and test results show a marked improvement in the efficiency of the FBCFC converter with the new active clamp circuit.
Rapid Learning with Stochastic Focus of Attention

We present a method to determine when to stop the evaluation of a decision-making process. The method determines to stop the evaluation process when the result of the full evaluation is obvious. This trait is highly desirable for margin-based Machine Learning algorithms where a classifier traditionally evaluates all the features for every example. However, some examples are easier to classify than others, a phenomenon which is characterized by the event when most of the features agree on the class of an example. By stopping the feature evaluation when encountering an easy to classify example, a margin-based Machine Learning algorithm can achieve substantial reduction in running times. To determine when to stop the feature evaluation, we develop a set of novel sequential tests, the Sequential Thresholded Sum Tests (STST). These tests stop the partial evaluation of the sum when the result of the full summation is guaranteed with high probability. By making different assumptions on the data and the features different tests arise. In general we look at the feature evaluation process as a random walk and apply different Brownian motion early stopping inequalities to determine when to stop the walk. From these inequalities we derive a family of stopping thresholds for sequential feature evaluations under different assumptions. We demonstrate the effectiveness of the different STST by speeding up several Online Learning algorithms on synthetic and real data.