gebhart/patrepeval huggingface dataset:
Supervised reranking for web image search (2010)

Visual search reranking that aims to improve the text-based image search with the help from visual content analysis has rapidly grown into a hot research topic. The interestingness of the topic stems mainly from the fact that the search reranking is an unsupervised process and therefore has the potential to scale better than its main alternative, namely the search based on offline-learned semantic concepts. However, the unsupervised nature of the reranking paradigm also makes it suffer from problems, the main of which can be identified as the difficulty to optimally determine the role of visual modality over different application scenarios. Inspired by the success of the "learning-to-rank" idea proposed in the field of information retrieval, we propose in this paper the "learning-to-rerank" paradigm, which derives the reranking function in a supervised fashion from the human-labeled training data. Although supervised learning is introduced, our approach does not suffer from scalability issues since a unified reranking model is learned that can be applied to all queries. In other words, a query-independent reranking model will be learned for all queries using query-dependent reranking features. The query-dependent reranking feature extraction is challenging since the textual query and the visual documents have different representation. In this paper, 11 lightweight reranking features are proposed by representing the textual query using visual context and pseudo relevant images from the initial search result. The experiments performed on two representative Web image datasets demonstrate that the proposed learning-to-rerank algorithm outperforms the state-of-the-art unsupervised reranking methods, which makes the learning-to-rerank paradigm a promising alternative for robust and reliable Web-scale image search.
A combined <i>ab initio</i> quantum mechanical and molecular mechanical method for carrying out simulations on complex molecular systems: Applications to the CH<sub>3</sub>Cl + Cl<sup>−</sup> exchange reaction and gas phase protonation of polyethers (1986)

Abstract We present an approach to couple ab initio quantum mechanical geometry optimiuzations with molecular mechanical optimizations, with the added capability to carry out molecular dynamics simulations of the systems to earch for new local minima. The approach is applied to the aqueous solution CH 3 Cl + Cl − exchange reaction and the gas phase protonation of polyethers.
Architecting aircraft power distribution systems via redundancy allocation (2014)

Recently, the environmental impact of aircraft and rising fuel prices have become an increasing concern in the aviation industry. To address these problems, organizations such as NASA have set demanding goals for reducing aircraft emissions, fuel burn, and noise. In an effort to reach the goals, a movement toward more-electric aircraft and electric propulsion has emerged. With this movement, the number of critical electrical loads on an aircraft is increasing causing power system reliability to be a point of concern.\n\nCurrently, power system reliability is maintained through the use of back-up power supplies such as batteries and ram-air-turbines (RATs). However, the increasing power requirements for critical loads will quickly outgrow the capacity of the emergency devices. Therefore, reliability needs to be addressed when designing the primary power distribution system.\n\nPower system reliability is a function of component reliability and redundancy. Component reliability is often not determined until detailed component design has occurred; however, the amount of redundancy in the system is often set during the system architecting phase. In order to meet the capacity and reliability requirements of future power distribution systems, a method for redundancy allocation during the system architecting phase is needed. \n\nThis thesis presents an aircraft power system design methodology that is based upon the engineering decision process. The methodology provides a redundancy allocation strategy and quantitative trade-off environment to compare architecture and technology combinations based upon system capacity, weight, and reliability criteria. \n\nThe methodology is demonstrated by architecting the power distribution system of an aircraft using turboelectric propulsion. The first step in the process is determining the design criteria which includes a 40 MW capacity requirement, a 20 MW capacity requirement for the an engine-out scenario, and a maximum catastrophic failure rate of one failure per billion flight hours. The next step is determining gaps between the performance of current power distribution systems and the requirements of the turboelectric system. A baseline architecture is analyzed by sizing the system using the turboelectric system power requirements and by calculating reliability using a stochastic flow network. To overcome the deficiencies discovered, new technologies and architectures are considered. Global optimization methods are used to find technology and architecture combinations that meet the system objectives and requirements. Lastly, a dynamic modeling environment is constructed to study the performance and stability of the candidate architectures. The combination of the optimization process and dynamic modeling facilitates the selection of a power system architecture that meets the system requirements and objectives.
A two‐color BCR–ABL probe that greatly reduces the false positive and false negative rates for fluorescence in situ hybridization in chronic myeloid leukemia (1998)

The t(9;22) translocation resulting in the fusion of BCRand ABLgenes is pathognomonic in chronic myeloid leukemia (CML) and may be investigated at the molecular level using fluorescence in situ hybridization (FISH). Two-color BCR–ABL probes visualizing one fusion signal (1F FISH) have high false positive rates (FPR) and false negative rates (FNR). The FPR is a result of the random spatial association of probe signals within normal interphase cells so that some cells appear to contain the BCR–ABLfusion gene. The FNR of 1F FISH probes depends on the distance between the BCR and ABL probes hybridized to the BCR–ABL fusion gene (≤368 kb); the “gap” between the signals causing the cell to be interpreted as normal. To overcome these difficulties, a two-color probe was used, employing four yeast artificial chromosome (YAC) sequences that span the breakpoint regions of the BCR and ABL genes and that visualize the two fusion signals BCR–ABL and ABL–BCRin CML cells (2F FISH). The FNR for the 2F FISH probes was assessed on clonal Ph+ granulocyte-macrophage-colony-forming cell (CFU-GM) derived colonies and was reduced to 0.4% (2/450), compared with an FNR of 13.5% (111/823) with 1F FISH. The FPR in normal mononuclear cells for the 2F FISH was 0.19 ± 0.12% (3/1,700), whereas the FPR using 1F FISH was 4.5 ± 2.3% (63/1,294). The 2F FISH can thus be used to evaluate very small frequencies of BCR–ABL-positive and -negative interphase cells and may be of use in the clinical monitoring of CML. Genes Chromosomes Cancer 23:109–115, 1998. © 1998 Wiley-Liss, Inc.
Highly Ordered Mesoporous Silicon Carbide Ceramics with Large Surface Areas and High Stability (2006)

Abstract Highly ordered mesoporous silicon carbide ceramics have been successfully synthesized with yields higher than 75 % via a one‐step nanocasting process using commercial polycarbosilane (PCS) as a precursor and mesoporous silica as hard templates. Mesoporous SiC nanowires in two‐dimensional (2D) hexagonal arrays ( p 6 m ) can be easily replicated from a mesoporous silica SBA‐15 template. Small‐angle X‐ray diffraction (XRD) patterns and transmission electron microscopy (TEM) images show that the SiC nanowires have long‐range regularity over large areas because of the interwire pillar connections. A three‐dimensional (3D) bicontinuous cubic mesoporous SiC structure ( Ia 3 d ) can be fabricated using mesoporous silica KIT‐6 as the mother template. The structure shows higher thermal stability than the 2D hexagonal mesoporous SiC, mostly because of the 3D network connections. The major constituent of the products is SiC, with 12 % excess carbon and 14 % oxygen measured by elemental analysis. The obtained mesoporous SiC ceramics are amorphous below 1200 °C and are mainly composed of randomly oriented β‐SiC crystallites after treatment at 1400 °C. N 2 ‐sorption isotherms reveal that these ordered mesoporous SiC ceramics have high Brunauer–Emmett–Teller (BET) specific surface areas (up to 720 m 2 g –1 ), large pore volumes (∼ 0.8 cm 3 g –1 ), and narrow pore‐size distributions (mean values of 2.0–3.7 nm), even upon calcination at temperatures as high as 1400 °C. The rough surface and high order of the nanowire arrays result from the strong interconnections of the SiC products and are the main reasons for such high surface areas. XRD, N 2 ‐sorption, and TEM measurements show that the mesoporous SiC ceramics have ultrahigh stability even after re‐treatment at 1400 °C under a N 2 atmosphere. Compared with 2D hexagonal SiC nanowire arrays, 3D cubic mesoporous SiC shows superior thermal stability, as well as higher surface areas (590 m 2 g –1 ) and larger pore volumes (∼ 0.71 cm 3 g –1 ).
Personalized characterization of diseases using sample-specific networks (2016)

A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.
Spontaneously oscillating K+ channel activity in transformed Madin-Darby canine kidney cells. (1993)

Intracellular alkalinization is known to be associated with tumorigenic transformation. Besides phenotypical alterations alkali-transformed Madin-Darby canine kidney (MDCK) cells exhibit a spontaneously oscillating cell membrane potential (PD). Using single-channel patch clamp techniques, it was the aim of this study to identify the ion channel underlying the rhythmic hyperpolarizations of the PD. In the cell-attached patch configuration, we found that channel activity was oscillating. The frequency of channel oscillations is 1.1 +/- 0.1 min-1. At the peak of oscillatory channel activity, single-channel current was -2.7 +/- 0.05 pA, and in the resting state it was -1.95 +/- 0.05 pA. Given the single-channel conductance of 53 +/- 3 pS for inward (and of 27 +/- 5 pS for outward) current the difference of single-channel current amplitude corresponded to a hyperpolarization of approximately 14 mV. The channel is selective for K+ over Na+. Channel kinetics are characterized by one open and by three closed time constants. The channel is Ca2+ sensitive. Half maximal activation in the inside-out patch mode is achieved at a Ca2+ concentration of 10 mumol/liter. In addition, we also found a 13-pS K+ channel that shows no oscillatory activity in the cell-attached patch configuration and that was not Ca2+ sensitive. We conclude that the Ca(2+)-sensitive 53-pS K+ channel is underlying spontaneous oscillations of the PD. It has virtually identical biophysical properties as a Ca(2+)-sensitive K+ channel in nontransformed parent MDCK cells. Hence, alkali-induced transformation of MDCK cells did not affect the channel protein itself but its regulators thereby causing spontaneous fluctuations of the PD.
Deep hypothermic circulatory arrest. (2013)

Effective cerebral protection remains the principle concern during aortic arch surgery. Hypothermic circulatory arrest (HCA) is entrenched as the primary neuroprotection mechanism since the 70s, as it slows injury-inducing pathways by limiting cerebral metabolism. However, increases in HCA duration has been associated with poorer neurological outcomes, necessitating the adjunctive use of antegrade (ACP) and retrograde cerebral perfusion (RCP). ACP has superseded RCP as the preferred perfusion strategy as it most closely mimic physiological perfusion, although there exists uncertainty regarding several technical details, such as unilateral versus bilateral perfusion, flow rate and temperature, perfusion site, undue trauma to head vessels, and risks of embolization. Nevertheless, we believe that the convenience, simplicity and effectiveness of straight DHCA justifies its use in the majority of elective and emergency cases. The following perspective offers a historical and clinical comparison of the DHCA with other techniques of cerebral protection.
FPGA-based Electronics for Confocal Line-Scanners with Linear Detector Arrays (2008)

Linear detector arrays are conveniently driven by FPGA-based electronics and enable simple line-scanning confocal microscope configurations. Their responsivity and signal-to-noise ratio for reflectance line-scanning is comparable to that of standard APD detectors for point-scanning.
Zinc Protoporphyrin in the Erythrocytes of Patients with Lead Intoxication and Iron Deficiency Anemia (1974)

The fluorescent porphyrin in the erythrocytes of patients with lead intoxication or with iron deficiency anemia is zinc protoporphyrin that is bound to globin moieties, probably at heme binding sites.
A statistical model for water‐vapour absorption (1952)

Abstract A statistical model of a water‐vapour absorption band is discussed, and comparison with computations made by Cowling shows that it provides a very accurate account of the average transmission over narrow frequency bands. The model can be used to compute the atmospheric transmission by water vapour under conditions of meteorological importance which were not treated by Cowling.
BANKING – EINE SCHLÜSSELINDUSTRIE (2009)

In einer Bank arbeiten heißt an zentraler Stelle in der Gesellschaft mitwirken. Denn Banken sind die Durchlauferhitzer der Wirtschaft: Wo Wachstum ist, sind Banken nicht weit, und wo Wachstum ist, steigt der Wohlstand. Banken haben einen erheblichen Anteil daran, wie sich der Wohlstand in einer Gesellschaft entwickelt – Banker arbeiten an entscheidender Stelle daran mit. Ohne Banken herrscht Stillstand und damit Rückschritt. Keine neuen Maschinen, keine neuen Fabriken, keine Solaranlagen auf dem Dach oder Windparks im Meer.
Isolation and expression analysis of gibberellin 20‐oxidase homologous gene in apple (2001)

To characterize the gibberellin (GA) 20‐oxidase gene in apple, the genomic and cDNA clone from ‘Fuji’ apple (accession no. AB037114) was isolated. The deduced amino acid sequence of this cDNA showed 71% and 66% identity to those of GA 20‐oxidase cloned from French bean and Arabidopsis, respectively. The transcript of this gene was detected mainly in immature seeds between 1–3 months after full bloom. These results suggested that this apple GA 20‐oxidase gene might be involved in GA biosynthesis in developing apple seed.
Neural Operator: Learning Maps Between Function Spaces (2021)

The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets. We propose a generalization of neural networks to learn operators, termed neural operators, that map between infinite dimensional function spaces. We formulate the neural operator as a composition of linear integral operators and nonlinear activation functions. We prove a universal approximation theorem for our proposed neural operator, showing that it can approximate any given nonlinear continuous operator. The proposed neural operators are also discretization-invariant, i.e., they share the same model parameters among different discretization of the underlying function spaces. Furthermore, we introduce four classes of efficient parameterization, viz., graph neural operators, multi-pole graph neural operators, low-rank neural operators, and Fourier neural operators. An important application for neural operators is learning surrogate maps for the solution operators of partial differential equations (PDEs). We consider standard PDEs such as the Burgers, Darcy subsurface flow, and the Navier-Stokes equations, and show that the proposed neural operators have superior performance compared to existing machine learning based methodologies, while being several orders of magnitude faster than conventional PDE solvers.
CTCF participates in DNA damage response via poly(ADP-ribosyl)ation (2017)

Abstract CCCTC-binding factor (CTCF) plays an essential role in regulating the structure of chromatin by binding DNA strands for defining the boundary between active and heterochromatic DNA. However, the role of CTCF in DNA damage response remains elusive. Here, we show that CTCF is quickly recruited to the sites of DNA damage. The fast recruitment is mediated by the zinc finger domain and poly (ADP-ribose) (PAR). Further analyses show that only three zinc finger motifs are essential for PAR recognition. Moreover, CTCF-deficient cells are hypersensitive to genotoxic stress such as ionizing radiation (IR). Taken together, these results suggest that CTCF participate in DNA damage response via poly(ADP-ribosylation).
Hedgehog induces formation of PKA-Smoothened complexes to promote Smoothened phosphorylation and pathway activation (2014)

Hedgehog (Hh) is a secreted glycoprotein that binds its receptor Patched to activate the G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptor-like protein Smoothened (Smo). In Drosophila, protein kinase A (PKA) phosphorylates and activates Smo in cells stimulated with Hh. In unstimulated cells, PKA phosphorylates and inhibits the transcription factor Cubitus interruptus (Ci). We found that in cells exposed to Hh, the catalytic subunit of PKA (PKAc) bound to the juxtamembrane region of the carboxyl terminus of Smo. PKA-mediated phosphorylation of Smo further enhanced its association with PKAc to form stable kinase-substrate complexes that promoted the PKA-mediated transphosphorylation of Smo dimers. We identified multiple basic residues in the carboxyl terminus of Smo that were required for interaction with PKAc, Smo phosphorylation, and Hh pathway activation. Hh induced a switch from the association of PKAc with a cytosolic complex of Ci and the kinesin-like protein Costal2 (Cos2) to a membrane-bound Smo-Cos2 complex. Thus, our study uncovers a previously uncharacterized mechanism for regulation of PKA activity and demonstrates that the signal-regulated formation of kinase-substrate complexes plays a central role in Hh signal transduction.
Analysis of Rate Decline Derivatives (1994)

ABSTRACT This paper describes rate derivative curves of constant pressure tests similar to derivative analysis for constant rate tests. Four rate decline derivatives: Cartesian, log-semi, semi-log, and log-log, are constructed, Typically, the rate derivative curves have some special characters. The specific characteristics of these derivative curves are related to the different flow regimes and various boundary effects. The derivative curves and decline curves can be used for well test interpretation and diagnostic in a similar way to the constant rate tests. Therefore, in some cases nonunique problems in well test analysis are eliminated. In this paper, we consider rate declines of an arbitrarily placed well in various shapes such as a circle, a rectangle, a stringer, and an irregular shape.
Distinguishing realistic military blasts from firecrackers in mitigation studies of blast-induced traumatic brain injury (2011)

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans the biological, physical, and social sciences.
ChemInform Abstract: Spectrophotometric and Voltammetric Characterization of Complexes of Bis(2,2′‐bipyridine)(2,2′‐bibenzimidazole)ruthenium and ‐osmium in Oxidation States II, III, and IV in Acetonitrile‐Water Mixtures. (1987)

Abstract Acid‐base and redox chemistry of the title compounds is investigated.
Compression, clustering, and pattern discovery in very high-dimensional discrete-attribute data sets (2005)

This paper presents an efficient framework for error-bounded compression of high-dimensional discrete-attribute data sets. Such data sets, which frequently arise in a wide variety of applications, pose some of the most significant challenges in data analysis. Subsampling and compression are two key technologies for analyzing these data sets. The proposed framework, PROXIMUS, provides a technique for reducing large data sets into a much smaller set of representative patterns, on which traditional (expensive) analysis algorithms can be applied with minimal loss of accuracy. We show desirable properties of PROXIMUS in terms of runtime, scalability to large data sets, and performance in terms of capability to represent data in a compact form and discovery and interpretation of interesting patterns. We also demonstrate sample applications of PROXIMUS in association rule mining and semantic classification of term-document matrices. Our experimental results on real data sets show that use of the compressed data for association rule mining provides excellent precision and recall values (above 90 percent) across a range of problem parameters while reducing the time required for analysis drastically. We also show excellent interpretability of the patterns discovered by PROXIMUS in the context of clustering and classification of terms and documents. In doing so, we establish PROXIMUS as a tool for both preprocessing data before applying computationally expensive algorithms and directly extracting correlated patterns.
Temperature contour maps at the strain-induced martensitic transition of a Cu–Zn–Al shape-memory single crystal (2011)

We study temperature changes at the reverse strain-induced martensitic transformation in a Cu–Zn–Al single crystal. Infrared thermal imaging reveals a markedly inhomogeneous temperature distribution. The evolution of the contour temperature maps enables information to be extracted on the kinetics of the interface motion.