11,807 research outputs found
Encode/Decode facility for FORTRAN 4
An ENCODE and DECODE facility, a subroutine added to a FORTRAN 4 library, allows alphanumeric data to be transfered to or from an area in memory rather than to or from external input/output devices. A buffer storage array allows the operations on the data prior to writing
Punch-magnet delay eliminated by modification of circuit
Reduction of retardation by diode-resistor networks of the current-decay time of a punch magnet by connection of a Zener diode in series with the damping network increases the reliability of data on paper tape
A New Scale to Measure War Attitudes: Construction and Predictors
Attitudes people have toward war in general have been of recent interest due to the war on terrorism and the war in Iraq. The purpose of this research was to develop a scale to measure war attitudes and to investigate factors that may influence these attitudes. In the first study, a scale was developed that measured war attitudes. Three factors emerging from the War Attitude Scale were labeled ethics of war, support for war, and affect about war. Patriotism-nationalism, authoritarianism, social criticism, belief in war outcomes, support of the president, and gender were found to be significant predictors of war attitudes. In the second study, the scale was administered to a community sample. A confirmatory factor analysis was conducted with three similar factors emerging. Additionally, the community sample results allowed further generalization of the findings. Implications for the construction of the War Attitude Scale and its predictors are discussed
Magnetic Inhomogeneity and Magnetotransport in Electron-Doped Ca(1-x)La(x)MnO(3) (0<=x<=0.10)
The dc magnetization (M) and electrical resistivity (\rho) as functions of
magnetic field and temperature are reported for a series of lightly electron
dopedCa(1-x)La(x)MnO(3) (0<=x<=0.10) specimens for which magnetization [Phys.
Rev. B {\bf 61}, 14319 (2000)] and scattering studies [Phys. Rev. B {\bf 68},
134440 (2003)] indicate an inhomogeneous magnetic ground state composed of
ferromagnetic (FM) droplets embedded in a G-type antiferromagnetic matrix. A
change in the magnetic behavior near x=0.02 has been suggested to be the
signature of a crossover to a long-ranged spin-canted phase. The data reported
here provide further detail about this crossover in the magnetization, and
additional insight into the origin of this phenomenon through its manifestation
in the magnetotransport. In the paramagnetic phase (T>=125 K) we find a
magnetoresistance =-C(M/M_S)^2 (M_S is the low-T saturation magnetization), as
observed in many manganites in the ferromagnetic (FM), colossal
magnetoresistance (CMR) region of the phase diagram, but with a value of C that
is two orders of magnitude smaller than observed for CMR materials. The doping
behavior C(x) follows that of M_S(x), indicating that electronic inhomogeneity
associated with FM fluctuations occurs well above the magnetic ordering
transition.Comment: 7 pp., 10 Fig.s, submitted to PR
Microwave diode amplifiers with low intermodulation distortion
Distortions can be greatly reduced in narrow-band applications by using the second harmonic. The ac behavior of simplified diode amplifier has negative resistance depending on slope of equivalent I-V curve
Structured Prediction of Sequences and Trees using Infinite Contexts
Linguistic structures exhibit a rich array of global phenomena, however
commonly used Markov models are unable to adequately describe these phenomena
due to their strong locality assumptions. We propose a novel hierarchical model
for structured prediction over sequences and trees which exploits global
context by conditioning each generation decision on an unbounded context of
prior decisions. This builds on the success of Markov models but without
imposing a fixed bound in order to better represent global phenomena. To
facilitate learning of this large and unbounded model, we use a hierarchical
Pitman-Yor process prior which provides a recursive form of smoothing. We
propose prediction algorithms based on A* and Markov Chain Monte Carlo
sampling. Empirical results demonstrate the potential of our model compared to
baseline finite-context Markov models on part-of-speech tagging and syntactic
parsing
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