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