Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiently supporting ad hoc queries in large datasets of time sequences

Ad hoc querying is difficult on very large datasets, since it is usually not possible to have the entire dataset on disk. While compression can be used to decrease the size of the dataset, compressed data is notoriously difficult to index or access. In this paper we consider a very large dataset comprising multiple distinct time sequences. Each point in the sequence is a numerical value. We show how to compress such a dataset into a format that supports ad hoc querying, provided that a small error can be tolerated when the data is uncompressed. Experiments on large, real world datasets (AT&T customer calling patterns) show that the proposed method achieves an average of less than 5% error in any data value after compressing to a mere 2.5% of the original space (i.e., a 40:1 compression ratio), with these numbers not very sensitive to dataset size. Experiments on aggregate queries achieved a 0.5% reconstruction error with a space requirement under 2%.
Improving web image search results using query-relative classifiers

Web image search using text queries has received considerable attention. However, current state-of-the-art approaches require training models for every new query, and are therefore unsuitable for real-world web search applications. The key contribution of this paper is to introduce generic classifiers that are based on query-relative features which can be used for new queries without additional training. They combine textual features, based on the occurence of query terms in web pages and image meta-data, and visual histogram representations of images. The second contribution of the paper is a new database for the evaluation of web image search algorithms. It includes 71478 images returned by a web search engine for 353 different search queries, along with their meta-data and ground-truth annotations. Using this data set, we compared the image ranking performance of our model with that of the search engine, and with an approach that learns a separate classifier for each query. Our generic models that use query-relative features improve significantly over the raw search engine ranking, and also outperform the query-specific models.
Practical Preference Relations for Large Data Sets

User-defined preferences allow personalized ranking of query results. A user provides a declarative specification of his/lier preferences, and the system is expected to use that specification to give more prominence to preferred answers. We study constraint formalisms for expressing user preferences as base facts in a partial order. We consider a language that allows comparison and a limited form of arithmetic, and show that the transitive closure computation required to complete the partial order terminates. We consider various ways of composing partial orders from smaller pieces, and provide results on the size of the resulting transitive closures. Finally, we show how preference queries within our language can be supported by suitable index structures for efficient evaluation over large data sets. Our results provide guidance about when complex preferences can be efficiently evaluated, and when they cannot.
Multi-STEPS: Multi-label similarity and truth estimation for propagated segmentations

Quantitative analysis in medical imaging often relies on anatomical segmentation of MR or CT images. Several multi-atlas based segmentation propagation methods have recently been published due to the accurate structural segmentations produced by propagating and combining manual delineations from multiple templates in a database. We propose a new multi-label local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al. [15]. It addresses the known problem of local vs. global image matching and the bias introduced in the performance estimation due to structure size. Results show a significant improvement in terms of segmentation accuracy for key brain areas when compared to state-of-the-art brain parcelation algorithms and reduced discontinuity and fragmentation between structures due to the MRF derived smoothness.
Leveraging Semantic Web Technologies for Managing Resources in a Multi-Domain Infrastructure-as-a-Service Environment

This paper reports on experience with using semantically-enabled network resource models to construct an operational multi-domain networked infrastructure-as-a-service (NIaaS) testbed called ExoGENI, recently funded through NSF’s GENI project. A dening property of NIaaS is the deep integration of network provisioning functions alongside the more common storage and computation provisioning functions. Resource provider topologies and user requests can be described using network resource models with common base classes for fundamental cyber-resources (links, nodes, interfaces) specialized via virtualization and adaptations between networking layers to specic technologies. This problem space gives rise to a number of application areas where semantic web technologies become highly useful - common in
Spline regression for hazard rate estimation when data are censored and measured with error

In this paper, we study an estimation problem where the variables of interest are subject to both right censoring and measurement error. In this context, we propose a nonparametric estimation strategy of the hazard rate, based on a regression contrast minimized in a finite‐dimensional functional space generated by splines bases. We prove a risk bound of the estimator in terms of integrated mean square error and discuss the rate of convergence when the dimension of the projection space is adequately chosen. Then we define a data‐driven criterion of model selection and prove that the resulting estimator performs an adequate compromise. The method is illustrated via simulation experiments that show that the strategy is successful.
Third-order Variational Reranking on Packed-Shared Dependency Forests

We propose a novel forest reranking algorithm for discriminative dependency parsing based on a variant of Eisner's generative model. In our framework, we define two kinds of generative model for reranking. One is learned from training data offline and the other from a forest generated by a baseline parser on the fly. The final prediction in the reranking stage is performed using linear interpolation of these models and discriminative model. In order to efficiently train the model from and decode on a hypergraph data structure representing a forest, we apply extended inside/outside and Viterbi algorithms. Experimental results show that our proposed forest reranking algorithm achieves significant improvement when compared with conventional approaches.
Query Optimization for XML

XML is an emerging standard for data representation and exchange on the World-Wide Web. Due to the nature of information on the Web and the inherent flexibility of XML, we expect that much of the data encoded in XML will be semistructured: the data may be irregular or incomplete, and its structure may change rapidly or unpredictably. This paper describes the query processor of Lore, a DBMS for XML-based data supporting an expressive query language. We focus primarily on Lore's cost-based query optimizer. While all of the usual problems associated with cost-based query optimization apply to XML-based query languages, a number of additional problems arise, such as new kinds of indexing, more complicated notions of database statistics, and vastly different query execution strategies for different databases. We define appropriate logical and physical query plans, database statistics, and a cost model, and we describe plan enumeration including heuristics for reducing the large search space. Our optimizer is fully implemented in Lore and preliminary performance results are reported. This is a short version of the paper Query Optimization for Semistructured Data which is available at: http://www-db.stanford. edu/~mchughj/publications/qo.ps
CDP-6-deoxy-delta 3,4-glucoseen reductase from Yersinia pseudotuberculosis: enzyme purification and characterization of the cloned gene

The 3,6-dideoxyhexoses, usually confined to the cell wall lipopolysaccharide of gram-negative bacteria, are essential to serological specificity and are formed via a complex biosynthetic pathway beginning with CDP-D-hexoses. In particular, the biosynthesis of CDP-ascarylose, one of the naturally occurring 3,6-dideoxyhexoses, consists of five enzymatic steps, with CDP-6-deoxy-delta 3,4-glucoseen reductase (E3) participating as the key enzyme in this catalysis. This enzyme has been previously purified from Yersinia pseudotuberculosis by an unusual procedure (protocol I) including a trypsin digestion step (O. Han, V.P. Miller, and H.-W. Liu, J. Biol. Chem. 265:8033-8041, 1990). However, the cloned gene showed disparity with the expected gene characteristics, and upon expression, the resulting gene product exhibited no E3 activity. These findings strongly suggested that the protein isolated by protocol I may have been misidentified as E3. A reinvestigation of the purification protocol produced a new and improved procedure (protocol II) consisting of DEAE-Sephacel, phenyl-Sepharose, Cibacron blue A, and Sephadex G-100 chromatography, which efficiently yielded a new homogeneous enzyme composed of a single polypeptide with a molecular weight of 39,000. This highly purified protein had a specific activity nearly 8,000-fold higher than that of cell lysates, and more importantly, the corresponding gene (ascD) was found to be part of the ascarylose biosynthetic cluster. Presented are the identification and confirmation of the E3 gene through cloning and overexpression and the culminating purification and unambiguous assignment of homogeneous E3. The nucleotide and translated amino acid sequences of the genuine E3 are also presented.
Mistargeting of peroxisomal EHHADH and inherited renal Fanconi's syndrome.

BACKGROUND In renal Fanconi's syndrome, dysfunction in proximal tubular cells leads to renal losses of water, electrolytes, and low-molecular-weight nutrients. For most types of isolated Fanconi's syndrome, the genetic cause and underlying defect remain unknown. METHODS We clinically and genetically characterized members of a five-generation black family with isolated autosomal dominant Fanconi's syndrome. We performed genomewide linkage analysis, gene sequencing, biochemical and cell-biologic investigations of renal proximal tubular cells, studies in knockout mice, and functional evaluations of mitochondria. Urine was studied with the use of proton nuclear magnetic resonance ((1)H-NMR) spectroscopy. RESULTS We linked the phenotype of this family's Fanconi's syndrome to a single locus on chromosome 3q27, where a heterozygous missense mutation in EHHADH segregated with the disease. The p.E3K mutation created a new mitochondrial targeting motif in the N-terminal portion of EHHADH, an enzyme that is involved in peroxisomal oxidation of fatty acids and is expressed in the proximal tubule. Immunocytofluorescence studies showed mistargeting of the mutant EHHADH to mitochondria. Studies of proximal tubular cells revealed impaired mitochondrial oxidative phosphorylation and defects in the transport of fluids and a glucose analogue across the epithelium. (1)H-NMR spectroscopy showed elevated levels of mitochondrial metabolites in urine from affected family members. Ehhadh knockout mice showed no abnormalities in renal tubular cells, a finding that indicates a dominant negative nature of the mutation rather than haploinsufficiency. CONCLUSIONS Mistargeting of peroxisomal EHHADH disrupts mitochondrial metabolism and leads to renal Fanconi's syndrome; this indicates a central role of mitochondria in proximal tubular function. The dominant negative effect of the mistargeted protein adds to the spectrum of monogenic mechanisms of Fanconi's syndrome. (Funded by the European Commission Seventh Framework Programme and others.).
Partial Discharge Measurements of Cryogenic Dielectric Materials in an HTS Transformer Using HFCT

Various types of high-temperature superconducting (HTS) devices have been developed, some of which have completed field testing and are ready for use in commercial applications. In order to facilitate the commercialization of HTS electric equipment for extra high voltage levels, cryogenic dielectric technologies should be established. In this work, in order to develop partial discharge (PD) measurement techniques for HTS transformers, artificial defects were installed both in liquid nitrogen and insulating oil. Experimental investigations were carried out for the diagnosis of the HTS transformer, and various PD patterns, which occurred due to the artificial defects of immersion in both insulating oil and liquid nitrogen, were measured. Throughout this work, the PD patterns that occurred in liquid nitrogen were detected, analyzed, and compared to those in insulating oil through the use of a high frequency current transformer (HFCT) diagnostic method. The partial discharge measuring techniques we developed could be fundamental in establishing HTS transformer diagnosis technologies for power applications.
Renormalization of diophantine skew flows, with applications to the reducibility problem

We introduce a renormalization group framework for the study of quasiperiodic skew flows on Lie groups of real or complex $n\times n$ matrices, for arbitrary Diophantine frequency vectors in $R^{d}$ and dimensions $d,n$. In cases where the Lie algebra component of the vector field is small, it is shown that there exists an analytic manifold of reducible skew systems, for each Diophantine frequency vector. More general near-linear flows are mapped to this case by increasing the dimension of the torus. This strategy is applied for the group of unimodular $2\times 2$ matrices, where the stable manifold is identified with the set of skew systems having a fixed fibered rotation number. Our results apply to vector fields of class Cγ, with $\gamma$ depending on the number of independent frequencies, and on the Diophantine exponent.
The effects of restricted energy and fluid intake on simulated amateur boxing performance.

This study examined the effects of serial reductions in energy and fluid intake on two simulated boxing performances separated by 2 days recovery. Eight amateur boxers (age: 23.6 +/- 3.2 years; height 175 +/- 5 cm; body mass [BM] 73.3 +/- 8.3 kg [Mean +/- SD]) performed two simulated boxing bouts (BB) under normal (N-trial) and restricted (R-trial) diets in a counterbalanced design over 5 days. The trials were separated by a 9-day period of normal dietary behavior (X-trial). BM was recorded on days 1, 3, and 5 of each trial. Simulated bouts of three, 3-min rounds with 1-min recovery were completed on days 3 (BB1) and 5 (BB2) of each 5-day trial. Punching force (N) was recorded from 8 sets of 7 punches by a purpose-built boxing ergometer. Heart rate (fC) was monitored continuously (PE3000 Polar Sports Tester, Kempele, Finland), and blood lactate (BLa) and glucose (BG) were determined 4-min post-performance (2300 StaPlus, YSI, Ohio). Energy and fluid intakes were significantly lower in the R-trial (p < .05). Body mass was maintained during the N-trial but fell 3% (p < .05) during the R-trial. There were no significant differences in end-of-bout fC or post-bout BG, but BLa was higher in the N- than the R-trial (p < .05). R-trial punching forces were 3.2% and 4.6% lower, respectively, compared to the corresponding N-trial bouts, but the differences did not reach statistical significance. These results suggest that energy and fluid restrictions in weight-governed sports do not always lead to a significant decrease in performance, but because of the small sample size and big variations in individual performances, these findings should be interpreted with care.
Mining probabilistic models learned by EDAs in the optimization of multi-objective problems

One of the uses of the probabilistic models learned by estimation of distribution algorithms is to reveal previous unknown information about the problem structure. In this paper we investigate the mapping between the problem structure and the dependencies captured in the probabilistic models learned by EDAs for a set of multi-objective satisfiability problems. We present and discuss the application of different data mining and visualization techniques for processing and visualizing relevant information from the structure of the learned probabilistic models. We show that also in the case of multi-objective optimization problems, some features of the original problem structure can be translated to the probabilistic models and unveiled by using algorithms that mine the model structures.
Simulating realistic force and shape of virtual cloth with adaptive meshes and its parallel implementation in OpenMP

Realistic simulation and manipulation of virtual cloth based on energy minimization techniques require higher computations. A model realizing force as well as shape to manipulate the virtual cloth demands even more computational cost. Mesh resolution of cloth has a great effect on the computational cost of cloth simulation. Therefore, granularity of mesh must be chosen in a manner that represents the cloth with a minimum computational cost and maintaining the quality (reality) of simulation. In this paper, we propose the adaptive mesh refinement combined with coarsening (AMRC) to have maximum possible efficiency and to maintain the acceptable reality. In addition, we propose an efficient parallel implementation in OpenMP to speed up the processing. AMRC changes the mesh density during execution time and requires the dynamic redistribution of load. A scheme of creating Active-List has been developed to manage the load balancing. We have parallelized the cloth simulation with a satisfactory efficiency by analyzing communication and synchronization overheads caused by OpenMP.
Compendium of Speech Testing Material and Typical Noise Spectra for use in Evaluating Communications Equipment

Abstract : The compendium of speech tests and typical naval noise spectra was assembled to meet the recurring need for good speech tests for evaluation of speech communication systems. Because of the complex nature of spoken language, many facets of language and test design must be specified before a quantitative score has meaning. These points are treated briefly, and published material is referenced for the reader who has need for further details.
High-Speed Counter-Current Chromatography with an Online Storage Technique for the Preparative Isolation and Purification of Dihydroflavonoids from Sophora alopecuroides L.

INTRODUCTION High-speed counter-current chromatography (HSCCC) is an efficient and non-absorption separation technique, but limitations still exist in simultaneous isolation of complex structures of natural products. Moreover, particular methods are various for different kinds of natural products. OBJECTIVE A novel HSCCC strategy combined with an online storage recycling elution (OSR-CCC) technique was developed for the quick separation of naturally occurring dihydroflavonoids from the extract of the herb Sophora alopecuroides L. METHODOLOGY In the separation procedure, a storage loop and two six-port valves were connected to a HSCCC system. Effluent A was subjected to an online storage loop and then to recycling separation three times after effluent B was collected in head-to-tail mode. After completion of the recycling separation of effluent A, the elution was switched to tail-to-head mode to collect effluent C. A biphasic solvent system of n-hexane/ethyl acetate/methanol/water (9:6:6:8, v/v/v/v) was used as the separation solvent during the whole elution. RESULTS Six constituents were isolated simultaneously from the extract (200 mg) of S. alopecuroides by running HSCCC non-stop, and their purities were higher than 95.0%. Their structures were determined as the pterocarpan glycoside sophoratonkin (1) (10.0 mg) and five dihydroflavonoids, alopecurone F (2) (5.4 mg), lehmannin (3) (11.0 mg), alopecurone A (4) (35.0 mg), sophoraflavanone G (5) (21.0 mg), alopecurone B (6) (31.0 mg). CONCLUSION This recycling HSCCC method combined with an online storage technique could be a rapid, effective and simple approach to isolate stilbene-dihydroflavonoids from herbs of the Sophora genus simultaneously. Copyright © 2017 John Wiley & Sons, Ltd.
Influence of leg muscle vibration on human walking.

We studied the effect of vibratory stimulation of different leg muscles [bilateral quadriceps (Q), hamstring (HS) muscles, triceps surae (TS), and tibialis anterior (TA)] in seven normal subjects during 1) quiet standing, 2) stepping in place movements, and 3) walking on the treadmill. The experiments were performed in a dimly illuminated room, and the subjects were given the instruction not to resist the applied perturbation. In one condition the velocity of the treadmill was controlled by a feedback from the subject's current position. In normal standing, TA vibration elicited a prominent forward body tilt, whereas HS and TS vibration elicited backward trunk or whole body inclination, respectively. Q vibration had little effect. During stepping in place, continuous HS vibration produced an involuntary forward stepping at about 0.3 m s(-1) without modifying the stepping frequency. When the subjects (with eyes closed) kept a hand contact with an external still object, they did not move forward but perceived an illusory forward leg flexion relative to the trunk. Q, TS, and TA vibration did not cause any systematic body translation nor illusory changes in body configuration. In treadmill locomotion, HS vibration produced an involuntary steplike increase of walking speed (by 0.1-0.6 m.s(-1)). Continuous vibration elicited larger speed increments than phasic stimulation during swing or stance phase. For phasic stimulation, HS vibration tended to be more effective when applied during swing than during stance phase. Q, TA, and TS vibration had little if any effect. Vibration of thigh muscles altered the walking speed depending on the direction of progression. During backward locomotion, the walking speed tended to decrease after HS vibration, whereas it significantly increased after Q vibration. Thus the influence of leg muscle vibration on stepping in place and locomotion differed significantly from that on normal posture. We suggest that the proprioceptive input from thigh muscles may convey information about the velocity of the foot movement relative to the trunk.
Wavelet based Artificial Light Receptor - A feature extraction model for face recognition

paper presents a novel biologically inspired and wavelet based model for extracting features of faces from face images. The biological knowledge about the distribution of light receptors, cones and rods, over the surface of the retina, and the way they are associated with the nerve ends for pattern vision forms the basis for the design of this model. A combination of classical wavelet decomposition and wavelet packet decomposition is used for simulating the functional model of cones and rods in pattern vision. The paper also describes the experiments performed for face recognition using the features extracted on the AT&T face database containing 400 face images of 40 different individuals. In the recognition stage we used k-Nearest Neighbour classifier. A feature vector of size 40 is formed for face images of each person and recognition accuracy is computed using k-NN classifier.