Miracle-dz/UniFAR-FTUs huggingface dataset:
Fractal Approximate Nearest Neighbour Search in Log-Log Time

Nearest neighbour searches in the image plane are among the most frequent problems in a variety of computer vision and image processing tasks. They can be used to replace missing values in image filtering, or to group close objects in image segmentation, or to access neighbouring points of interest in feature extraction. In particular, we address two nearest neighbour problems: The nearest neighbour problem is usually stated independently of the application as returning the point p ∈ S,S = {(x1,y1), . ,(xn,yn)} that minimises the Euclidean distance ||p− q||2 to a query point q = (x,y). The simple solution of a linear scan comprises a comparison of q to all elements of S, which is too time-consuming for most applications, especially those with real-time requirements. If the nearest neighbour p ∈ S must be found for every coordinate q ∈ I of an image I = {(0,0),(0,1),(0,2), . ,(W,H)} of width W and height H, we obtain the all nearest neighbours problem. This problem occurs frequently in modern saliency based approaches, where only robustly detectable image regions are processed (for example in SIFT). In this paper, we introduce an approximate solution to solve these problems that is based on using a space filling curve. The central idea of the proposed approach is to map the image plane to one dimension using the Hilbert curve (Fig. 1). The nearest-neighbour problem is then solved efficiently in one dimension and mapped back to the 2D-plane.
Adjutant: A framework for flexible human-machine collaborative systems

Flexible interaction and instruction is a key enabling technology for expanding robotics into small to medium scale manufacturing, in-home assistance for physically disabled individuals, and robotic surgery. In these cases, performing a task manually is neither practical nor scalable, yet complete automation is cost-prohibitive or impossible. Thus, our interest is in collaborative systems that can be easily trained to work with an operator. This collaborative robotic system should be instructable in a generalizable way for a wide range of tasks, and should generalize to new tasks gracefully with minimal retraining. At the same time, for a given task, the system should take advantage of user interaction modalities needed to accomplish the task, subject to the constraints of the available interfaces. These ideas motivate the Adjutant framework. Adjutant supports human-robot collaborative operations for ranges of user roles and robot capability. Adjutant models human-robot systems via sets of robot capabilities, composable high-level functions that can be specialized to specific tasks, and collaborative behaviors which relate these capabilities to specific user interfaces or interaction paradigms. Adjutant also incorporates several methods encapsulating reusable task information into capabilities, thus specializing them, including tool affordances, perceptual grounding templates, and tool movement primitives. We have implemented Adjutant as a software framework in ROS and, in this paper, explore the utility of Adjutant for performing several real-world collaborative manufacturing tasks on an industrial robot test-bed.
Author ' s personal copy Racial-ethnic self-schemas : Multidimensional identity-based motivation q

Prior self-schema research focuses on benefits of being schematic vs. aschematic in stereotyped domains. The current studies build on this work, examining racial-ethnic self-schemas as multidimensional, containing multiple, conflicting, and non-integrated images. A multidimensional perspective captures complexity; examining net effects of dimensions predicts within-group differences in academic engagement and well-being. When racialethnicity self-schemas focus attention on membership in both in-group and broader society, engagement with school should increase since school is not seen as out-group defining. When racial-ethnicity self-schemas focus attention on inclusion (not obstacles to inclusion) in broader society, risk of depressive symptoms should decrease. Support for these hypotheses was found in two separate samples (8th graders, n = 213, 9th graders followed to 12th grade n = 141). 2008 Elsevier Inc. All rights reserved.
Case studies of a robot enhanced walker for training of children with cerebral palsy

Cerebral palsy (CP) is a disorder of movement and posture in children caused by non-progressive insult of the immature brain. The characteristic features are weakness, spasticity, muscle contractures, and poor motor coordination. The gait patterns of children with CP are slow, uncoordinated, and unstable. Our hypothesis is that these impaired children will benefit from robot enhanced walkers to improve their balance, coordination, and speed during gait. In addition, this experience will also impact their clinical scores that relate to their functional performance and caregiver assistance. In this study, we used a specially-designed robotic walker which children used to perform a series of walking tasks, in increasing order of difficulty. This study was performed in 30 training sessions over a period of 3 months. Each training session lasted for 20 minutes. The outcome measures were variables recorded by the robot such as travel distance, average speed, and clinical measured variables that characterize their disability profiles.
Deconjugated Bile Salts Produced by Extracellular Bile-Salt Hydrolase-Like Activities from the Probiotic Lactobacillus johnsonii La1 Inhibit Giardia duodenalis In vitro Growth

Giardiasis, currently considered a neglected disease, is caused by the intestinal protozoan parasite Giardia duodenalis and is widely spread in human as well as domestic and wild animals. The lack of appropriate medications and the spread of resistant parasite strains urgently call for the development of novel therapeutic strategies. Host microbiota or certain probiotic strains have the capacity to provide some protection against giardiasis. By combining biological and biochemical approaches, we have been able to decipher a molecular mechanism used by the probiotic strain Lactobacillus johnsonii La1 to prevent Giardia growth in vitro. We provide evidence that the supernatant of this strain contains active principle(s) not directly toxic to Giardia but able to convert non-toxic components of bile into components highly toxic to Giardia. By using bile acid profiling, these components were identified as deconjugated bile-salts. A bacterial bile-salt-hydrolase of commercial origin was able to mimic the properties of the supernatant. Mass spectrometric analysis of the bacterial supernatant identified two of the three bile-salt-hydrolases encoded in the genome of this probiotic strain. These observations document a possible mechanism by which L. johnsonii La1, by secreting, or releasing BSH-like activity(ies) in the vicinity of replicating Giardia in an environment where bile is present and abundant, can fight this parasite. This discovery has both fundamental and applied outcomes to fight giardiasis, based on local delivery of deconjugated bile salts, enzyme deconjugation of bile components, or natural or recombinant probiotic strains that secrete or release such deconjugating activities in a compartment where both bile salts and Giardia are present.
PDB-wide collection of binding data: current status of the PDBbind database

MOTIVATION Molecular recognition between biological macromolecules and organic small molecules plays an important role in various life processes. Both structural information and binding data of biomolecular complexes are indispensable for depicting the underlying mechanism in such an event. The PDBbind database was created to collect experimentally measured binding data for the biomolecular complexes throughout the Protein Data Bank (PDB). It thus provides the linkage between structural information and energetic properties of biomolecular complexes, which is especially desirable for computational studies or statistical analyses. RESULTS Since its first public release in 2004, the PDBbind database has been updated on an annual basis. The latest release (version 2013) provides experimental binding affinity data for 10,776 biomolecular complexes in PDB, including 8302 protein-ligand complexes and 2474 other types of complexes. In this article, we will describe the current methods used for compiling PDBbind and the updated status of this database. We will also review some typical applications of PDBbind published in the scientific literature. AVAILABILITY AND IMPLEMENTATION All contents of this database are freely accessible at the PDBbind-CN Web server at http://www.pdbbind-cn.org/. CONTACT wangrx@mail.sioc. ac.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Performance analysis of eigenvalue based spectrum sensing under frequency selective channels

Detecting primary signals with very low Signal-to-Noise Ratio (SNR) is a very important problem in cognitive radio (CR) systems. Small parameter uncertainties are unavoidable in any practical system, and especially the noise variance uncertainty has great effect on the performance of the most basic spectrum sensing method, energy detection. This has motivated the need for advanced spectrum sensing algorithms, like eigenvalue based spectrum sensing, which can be used to overcome the effects of parameter uncertainty in very low SNR cases. In this paper we study the effects of channel frequency selectivity, in combination with noise uncertainty, in case of energy detector and eigenvalue based spectrum sensing. It is demonstrated that channel frequency selectivity significantly enhances the performance of eigenvalue based spectrum sensing techniques.
Cost-sensitive Naïve Bayes Classification of Uncertain Data

Data uncertainty is widespread in real-word applications. It has captured a lot of attention, but little job has been paid to the research of cost sensitive algorithm on uncertain data. The paper proposes a novel cost-sensitive Naive Bayes algorithm CS-DTU for classifying and predicting uncertain datasets. In the paper, we apply probability and statistics theory on uncertain data model, define the utility of uncertain attribute to total cost, and propose a new test strategy for attribute selection algorithm. Experimental results on UCI Datasets demonstrate the proposed algorithm can effectively reduce total cost, and significantly outperforms the competitor.
Putting Educational Equality in Its Place

Educational equality is one important value of justice in education, but it is only one. This article makes a case for a meritocratic principle of educational equality and shows that certain arguments against that principle do not justify rejecting it. It would be wrong to, for the sake of educational equality, undermine the value of the family or economic growth in ways that damage the prospects for flourishing of the least advantaged. But insofar as educational equality can be improved without harming those other values, it should be pursued; in practice, educational equality can be pursued effectively within the limits set by those values.
Remarks on the Cramer-Rao inequality for Doppler-based target parameter estimation

This paper outlines the problem of multi-static Doppler-based target position and velocity estimation. The Fisher information matrix is derived given a separate target illuminator and then given a target-based isotropic signal emission. Some remarks concerning the Cramer-Rao inequality and its relationship to the estimation problem are given. Some results concerning the placement of the receivers are given and some open problems are discussed.
A Peek into Tropomyosin Binding and Unfolding on the Actin Filament

Background Tropomyosin is a prototypical coiled coil along its length with subtle variations in structure that allow interactions with actin and other proteins. Actin binding globally stabilizes tropomyosin. Tropomyosin-actin interaction occurs periodically along the length of tropomyosin. However, it is not well understood how tropomyosin binds actin. Principal Findings Tropomyosin's periodic binding sites make differential contributions to two components of actin binding, cooperativity and affinity, and can be classified as primary or secondary sites. We show through mutagenesis and analysis of recombinant striated muscle α-tropomyosins that primary actin binding sites have a destabilizing coiled-coil interface, typically alanine-rich, embedded within a non-interface recognition sequence. Introduction of an Ala cluster in place of the native, more stable interface in period 2 and/or period 3 sites (of seven) increased the affinity or cooperativity of actin binding, analysed by cosedimentation and differential scanning calorimetry. Replacement of period 3 with period 5 sequence, an unstable region of known importance for cooperative actin binding, increased the cooperativity of binding. Introduction of the fluorescent probe, pyrene, near the mutation sites in periods 2 and 3 reported local instability, stabilization by actin binding, and local unfolding before or coincident with dissociation from actin (measured using light scattering), and chain dissociation (analyzed using circular dichroism). Conclusions This, and previous work, suggests that regions of tropomyosin involved in binding actin have non-interface residues specific for interaction with actin and an unstable interface that is locally stabilized upon binding. The destabilized interface allows residues on the coiled-coil surface to obtain an optimal conformation for interaction with actin by increasing the number of local substates that the side chains can sample. We suggest that local disorder is a property typical of coiled coil binding sites and proteins that have multiple binding partners, of which tropomyosin is one type.
Synthesis, Characterization and Application of 1-Butyl-3 Methylimidazolium Chloride as Green Material for Extractive Desulfurization of Liquid Fuel

The possible application of imidazolium ionic liquids as energy-efficient green material for extractive deep desulfurization of liquid fuel has been investigated. 1-Butyl-3-methylimidazolium chloride [BMIM]Cl was synthesized by nucleophilic substitution reaction of n-methylimidazolium and 1-chlorobutane. Molecular structures of the ILs were confirmed by FTIR, 1H-NMR, and 13C-NMR. The thermal properties, conductivity, solubility, water content and viscosity analysis of [BMIM]Cl were carried out. The effects of reaction time, reaction temperature, sulfur compounds, and recycling of IL without regeneration on dibenzothiophene removal of liquid fuel were presented. In the extractive desulfurization process, the removal of dibenzothiophene in n-dodecane using [BMIM]Cl was 81% with mass ratio of 1 : 1, in 30 min at 30°C under the mild reaction conditions. Also, desulfurization of real fuels with IL and multistage extraction were studied. The results of this work might offer significant insights in the perceptive use of imidazoled ILs as energy-efficient green material for extractive deep desulfurization of liquid fuels as it can be reused without regeneration with considerable extraction efficiency.
A Keynesian Solution to Classical Unemployment

In a classical macroeconomic model, the real wage equals labor's marginal product and the real interest rate can fall no lower than the rate of investment. These rigidities may prevent labor market clearing. Economies with rapid labor supply growth, capital immobility and a low capital labor ratio will be prone to such `classical unemployment'. Downward iaexibility in real wages restores full employment, lowers real interest rates and stimulates investment provided that i¥rms also perceive that they are rationed in output sales. Such quantity constraints have been identii¥ed by Clower (1965) as a critical feature in Keynes (1936).
A distributed fault diagnosis approach utilizing adaptive approximation for a class of interconnected continuous-time nonlinear systems

This paper develops an adaptive approximation based approach for distributed fault diagnosis for a class of interconnected continuous-time nonlinear systems with modeling uncertainties and measurement noise. The proposed approach integrates learning with filtering techniques and allows the derivation of tight detection thresholds. This is accomplished in two ways: at first, by learning the modeling uncertainty through adaptive approximation methods, so that the learned function is used for the derivation of the residual signal, and then by using filtering for dampening measurement noise. The required signals for both tasks are derived through a two-stage filtering process, by exploiting the properties of the filtering framework. Finally, simulation results are used to demonstrate the effectiveness of the proposed approach.
Searching with iterated maps

In many problems that require extensive searching, the solution can be described as satisfying two competing constraints, where satisfying each independently does not pose a challenge. As an alternative to tree-based and stochastic searching, for these problems we propose using an iterated map built from the projections to the two constraint sets. Algorithms of this kind have been the method of choice in a large variety of signal-processing applications; we show here that the scope of these algorithms is surprisingly broad, with applications as diverse as protein folding and Sudoku.
A phonotactic-semantic paradigm for automatic spoken document classification

We demonstrate a phonotactic-semantic paradigm for spoken document categorization. In this framework, we define a set of acoustic words instead of lexical words to represent acoustic activities in spoken languages. The strategy for acoustic vocabulary selection is studied by comparing different feature selection methods. With an appropriate acoustic vocabulary, a voice tokenizer converts a spoken document into a text-like document of acoustic words. Thus, a spoken document can be represented by a count vector, named a bag-of-sounds vector, which characterizes a spoken document's semantic domain. We study two phonotactic-semantic classifiers, the support vector machine classifier and the latent semantic analysis classifier, and their properties. The phonotactic-semantic framework constitutes a new paradigm in spoken document classification, as demonstrated by its success in the spoken language identification task. It achieves 18.2% error reduction over state-of-the-art benchmark performance on the 1996 NIST Language Recognition Evaluation database.
ADAPTIVE BAYESIAN EQUALIZER WITH SUPERIMPOSED TRAINING FOR MIMO CHANNELS

The Bayesian equalizer is implementable by a proper employment of a radial basis function (RBF) neural network, with the inverse filtering problem posed as a classification problem. The proposed approach allows that the transmission of information and the RBF training be accomplished in a simultaneous and uninterrupted way. Moreover, the channel estimation procedure remains an unimodal optimization problem. Simulation results confirm the effectiveness of the proposed MIMO equalizer.
SPL8, an SBP-Box Gene That Affects Pollen Sac Development in Arabidopsis Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.010678.

SQUAMOSA PROMOTER BINDING PROTEIN–box genes (SBP-box genes) encode plant-specific proteins that share a highly conserved DNA binding domain, the SBP domain. Although likely to represent transcription factors, little is known about their role in development. In Arabidopsis, SBP-box genes constitute a structurally heterogeneous family of 16 members known as SPL genes. For one of these genes, SPL8, we isolated three independent transposon-tagged mutants, all of which exhibited a strong reduction in fertility. Microscopic analysis revealed that this reduced fertility is attributable primarily to abnormally developed microsporangia, which exhibit premeiotic abortion of the sporocytes. In addition to its role in microsporogenesis, the SPL8 knockout also seems to affect megasporogenesis, trichome formation on sepals, and stamen filament elongation. The SPL8 mutants described help to uncover the roles of SBP-box genes in plant development.
Tracking Federated Queries in the Linked Data

Federated query engines allow data consumers to execute queries over the federation of Linked Data (LD). However, as feder-ated queries are decomposed into potentially thousands of subqueries distributed among SPARQL endpoints, data providers do not know fed-erated queries, they only know subqueries they process. Consequently, unlike warehousing approaches, LD data providers have no access to secondary data. In this paper, we propose FETA (FEderated query TrAcking), a query tracking algorithm that infers Basic Graph Patterns (BGPs) processed by a federation from a shared log maintained by data providers. Concurrent execution of thousand subqueries generated by multiple fed-erated query engines makes the query tracking process challenging and uncertain. Experiments with Anapsid show that FETA is able to extract BGPs which, even in a worst case scenario, contain BGPs of original queries.
Computational Comparative Study of Tuberculosis Proteomes Using a Model Learned from Signal Peptide Structures

Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor. To improve signal peptide prediction without prior knowledge of the associated organisms, we present a machine-learning method, called SVMSignal, which uses biochemical properties as features, as well as features acquired from a novel encoding, to capture biochemical profile patterns for learning the structures of signal peptides directly. We tested SVMSignal and five popular methods on two benchmark datasets from the SPdb and UniProt/Swiss-Prot databases, respectively. Although SVMSignal was trained on an old dataset, it performed well, and the results demonstrate that learning the structures of signal peptides directly is a promising approach. We also utilized SVMSignal to analyze proteomes in the entire HAMAP microbial database. Finally, we conducted a comparative study of secretome analysis on seven tuberculosis-related strains selected from the HAMAP database. We identified ten potential secreted proteins, two of which are drug resistant and four are potential transmembrane proteins. SVMSignal is publicly available at http://bio-cluster.iis. sinica.edu. tw/SVMSignal. It provides user-friendly interfaces and visualizations, and the prediction results are available for download.
Private Ciphertext-Policy Attribute-based Encryption Schemes With Constant-Size Ciphertext Supporting CNF Access Policy

Attribute-based encryption (ABE) is an extension of traditional public key encryption in which the encryption and decryption phases are based on user’s attributes. More precisely, we focus on ciphertext-policy ABE (CP-ABE) where the secret-key is associated to a set of attributes and the ciphertext is generated with an access policy. It then becomes feasible to decrypt a ciphertext only if one’s attributes satisfy the used access policy. In this paper, we give the first private CP-ABE constructions with a constant-size ciphertext, supporting CNF (Conjunctive Normal Form) access policy, with the simple restriction that each attribute can only appear kmax times in the access formula. Our two constructions are based on the BGW scheme at Crypto’05. The first scheme is basic selective secure (in the standard model) while our second one reaches the selective CCA security (in the random oracle model).