Quadrature LC VCO with passive coupling and phase combining network

A circuit and method for generating a signal is disclosed. The circuit includes a set of wide tuning LC tanks, a set of core transistors cross coupled to the set of wide tuning LC tanks, and a combining network coupled to the set of wide tuning LC tanks and the set of core transistors. The combining network further includes a set of inputs connected to the set of wide tuning LC tanks and the set of core transistors, a set of coupling transistors connected to the set of inputs, a set of source inductors connected to the set of coupling transistors, a coupling capacitor connected to the set of source inductors, a load resistor connected to the coupling capacitor. The combining network combines the set of inputs and the signal is delivered to the load resistor as a fourth order harmonic.Board of Regents, University of Texas Syste

Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set. Thus DDFA features are inherently discriminative from the start even though they are trained without knowledge of the ultimate classification problem. Interestingly, DDFA also continues to add new features indefinitely (so it does not depend on a hidden layer size), is not based on minimizing error, and is inherently divergent instead of convergent, thereby providing a unique direction of research for unsupervised feature learning. In this paper the quality of its learned features is demonstrated on the MNIST dataset, where its performance confirms that indeed DDFA is a viable technique for learning useful features.Comment: Corrected citation formattin

Gravitational Acceleration of Spinning Bodies From Lunar Laser Ranging Measurements

The Sun's relativistic gravitational gradient accelerations of Earth and Moon, dependent on the motions of the latter bodies, act upon the system's internal angular momentum. This spin-orbit force (which plays a part in determining the gravity wave signal templates for astrophysical sources) slightly accelerates the Earth-Moon system as a whole, but it more robustly perturbs that system's internal dynamics with a 5 cm, synodically oscillating range contribution which is presently measured to 4 mm precision by more than three decades of lunar laser ranging.Comment: 10 pages, PCTex32.v3.

Nucleotide sequence of the luxA gene of Vibrio harveyi and the complete amino acid sequence of the alpha subunit of bacterial luciferase

The nucleotide sequence of the 1.85-kilobase EcoRI fragment from Vibrio harveyi that was cloned using a mixed-sequence synthetic oligonucleotide probe (Cohn, D. H., Ogden, R. C., Abelson, J. N., Baldwin, T. O., Nealson, K. H., Simon, M. I., and Mileham, A. J. (1983) Proc. Natl. Acad. Sci. U.S.A. 80, 120-123) has been determined. The alpha subunit-coding region (luxA) was found to begin at base number 707 and end at base number 1771. The alpha subunit has a calculated molecular weight of 40,108 and comprises a total of 355 amino acid residues. There are 34 base pairs separating the start of the alpha subunit structural gene and a 669-base open reading frame extending from the proximal EcoRI site. At the 3' end of the luxA coding region there are 26 bases between the end of the structural gene and the start of the luxB structural gene. Approximately two-thirds of the alpha subunit was sequenced by protein chemical techniques. The amino acid sequence implied by the DNA sequence, with few exceptions, confirmed the chemically determined sequence. Regions of the alpha subunit thought to comprise the active center were found to reside in two discrete and relatively basic regions, one from around residues 100-115 and the second from around residues 280-295

Ammonia-Labile Bonds in High- and Low-Digestibility Strains of Switchgrass

Improvement in the forage quality of switchgrass (Panicum virgatum L.) through phenotypic selection for increased in vitro dry matter digestibility (IVDMD) has been demonstrated. This study tested the hypothesis that genetic improvement of fiber digestibility in switchgrass has been achieved by selection for a strain with a decreased quantity of ammonia-labile bonds. Tissue samples of a high-digestibility (high-IVDMD) and a low-digestibility strain (low-IVDMD) of switchgrass were ammoniated at rates of 0, 10, 20, and 40 g kg-1 dry matter. Fiber composition and in vitro rate and extent of neutral-detergent fiber (NDF) digestion were determined on control and ammoniated samples. The high-IVDMD strain had lower (P \u3c 0.05) concentrations of NDF and acid-detergent lignin (ADL) than the low-IVDMD strain. Lignin concentrations averaged 53 and 71 g kg-1 for the high- and low-IVDMD strains, respectively. The high-IVDMD strain had a greater (P \u3c 0.05) extent of NDF digestion when compared with the low strain; however, the rate of NDF digestion did not differ (P \u3e 0.05) between strains. Increased digestibility of the high-IVDMD strain was primarily attributed to increased cell-wall (NDF) digestibility. Ammoniation at 20 and 40 g kg-1 resulted in small decreases (P \u3c 0.05) in NDF concentrations when compared with the control; however, ammoniation had no effect on hemicellulose, cellulose, or ADL concentrations. Ammoniation increased (P \u3c 0.05) both the rate and extent of NDF digestion. Extent of NDF digestion averaged 0.395 for the control and 0.465, 0.498, and 0.493 for the 10, 20, and 40-g kg-1 treatments, respectively. Strain X ammoniation rate interaction was not significant for rate and extent of digestion, suggesting that genetic improvement in digestibility of switchgrass was not related to the number of ammonia-labile bonds

Real-time hebbian learning from autoencoder features for control tasks

Neural plasticity and in particular Hebbian learning play an important role in many research areas related to artficial life. By allowing artificial neural networks (ANNs) to adjust their weights in real time, Hebbian ANNs can adapt over their lifetime. However, even as researchers improve and extend Hebbian learning, a fundamental limitation of such systems is that they learn correlations between preexisting static features and network outputs. A Hebbian ANN could in principle achieve significantly more if it could accumulate new features over its lifetime from which to learn correlations. Interestingly, autoencoders, which have recently gained prominence in deep learning, are themselves in effect a kind of feature accumulator that extract meaningful features from their inputs. The insight in this paper is that if an autoencoder is connected to a Hebbian learning layer, then the resulting Realtime Autoencoder-Augmented Hebbian Network (RAAHN) can actually learn new features (with the autoencoder) while simultaneously learning control policies from those new features (with the Hebbian layer) in real time as an agent experiences its environment. In this paper, the RAAHN is shown in a simulated robot maze navigation experiment to enable a controller to learn the perfect navigation strategy significantly more often than several Hebbian-based variant approaches that lack the autoencoder. In the long run, this approach opens up the intriguing possibility of real-time deep learning for control

A commentary on the eleventh book of the Punica of Silius Italicus

Preface: The scope of the commentary is limited. I have been concerned with establishing the diction of Silius in Book 11. I have shown which words are confined to epic, which words are poetic and which words are prosaic. I have not attempted to establish whether there is a correlation between Silius' use of 'poetic' and' prosaic' words and the content of what he is saying. But I have noticed that Silius frequently uses prosaic words when he is following Livy or some other historical source. In other cases, Silius may be using prosaic words because of his own training as an orator. He is clearly indebted to Cicero. Nor have I attempted to establish whether there is any particular effect when Silius uses a'poetic' or 'prosaic' word or phrase or construction. I have been influenced by considerations of length and also by the fact that I believe any such attempted interpretation, although it might produce valuable results, would of necessity be much more subjective than what I have actually done. I leave any such interpretation to future researchers of Silius

An Analysis of Economic Efficiency in Bean Production: Evidence from Eastern Uganda

Bean has emerged to be an important cash crop as well as a staple food in Uganda; however, the country’s bean productivity per unit area cultivated has been on the decline for the past ten years. This study estimated the economic efficiency levels and assessed the factors influencing economic efficiency among bean farmers in Eastern Uganda, by applying a stochastic frontier cost function and a two-limit Tobit regression model, based on a random sample of 580 households. Findings revealed that the mean economic efficiency level was 59.94% and it was positively influenced by value of assets, off-farm income, credit and farmers’ primary occupation. Based on the findings from this study, there is need for government and stakeholders to train farmers on entrepreneurial skills so that they can divest their farm profits into more income generating activities which would harness more farming capital. Finally, there is a need for initiatives geared towards enhancing farmers’ access to adequate credit for farming at affordable interest rates and using groups as collateral, so that they could invest more in farming to increase their economic efficiency and farm productivity. Key words: stochastic frontier approach, smallholder farmers, Tobit regression mode