S18RS SGFB No. 3 (Spring Greening)

A FINANCE BILL To allocate a maximum of seven thousand dollars and zero cents ($7,000.00) from the Student Government Initiatives account to fund sustainable plants for Spring Greening Day 201

S17RS SGFB No. 5 (LSU Dance Marathon\u27s Big Event)

To allocate a maximum of fifteen thousand dollars and zero cents ($15,000.00) from the Student Senate Surplus Account to fund Dance Marathon at LSU’s 2017 Big Event’s flooring and staging cost

Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language

A core tension in models of concept learning is that the model must carefully balance the tractability of inference against the expressivity of the hypothesis class. Humans, however, can efficiently learn a broad range of concepts. We introduce a model of inductive learning that seeks to be human-like in that sense. It implements a Bayesian reasoning process where a language model first proposes candidate hypotheses expressed in natural language, which are then re-weighed by a prior and a likelihood. By estimating the prior from human data, we can predict human judgments on learning problems involving numbers and sets, spanning concepts that are generative, discriminative, propositional, and higher-order.Comment: NeurIPS 2023 ora

The Mass Assembly Histories of Galaxies of Various Morphologies in the GOODS Fields

We present an analysis of the growth of stellar mass with cosmic time partitioned according to galaxy morphology. Using a well-defined catalog of 2150 galaxies based, in part, on archival data in the GOODS fields, we assign morphological types in three broad classes (Ellipticals, Spirals, Peculiar/Irregulars) to a limit of z_AB=22.5 and make the resulting catalog publicly available. We combine redshift information, optical photometry from the GOODS catalog and deep K-band imaging to assign stellar masses. We find little evolution in the form of the galaxy stellar mass function from z~1 to z=0, especially at the high mass end where our results are most robust. Although the population of massive galaxies is relatively well established at z~1, its morphological mix continues to change, with an increasing proportion of early-type galaxies at later times. By constructing type-dependent stellar mass functions, we show that in each of three redshift intervals, E/S0's dominate the higher mass population, while spirals are favored at lower masses. This transition occurs at a stellar mass of 2--3 times 10^{10} Msun at z~0.3 (similar to local studies) but there is evidence that the relevant mass scale moves to higher mass at earlier epochs. Such evolution may represent the morphological extension of the ``downsizing'' phenomenon, in which the most massive galaxies stop forming stars first, with lower mass galaxies becoming quiescent later. We infer that more massive galaxies evolve into spheroidal systems at earlier times, and that this morphological transformation may only be completed 1--2 Gyr after the galaxies emerge from their active star forming phase. We discuss several lines of evidence suggesting that merging may play a key role in generating this pattern of evolution.Comment: 24 pages, 1 table, 8 figures, accepted for publication in Ap

Learning to Infer Graphics Programs from Hand-Drawn Images

We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input

S18RS SGR No. 4 (Experience LSU)

A RESOLUTION TO URGE AND REQUEST EXPERIENCE LSU TO REVIEW THE CURRENT FUNDRAISING METHODS FOR ORGNANZATIONS AND INVESTIGATE ALTERNATE METHODS OF FUNDRAISIN

From Perception to Programs: Regularize, Overparameterize, and Amortize

Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into a low-dimensional interpretable representation, which is then processed by a synthesized program. We explore several techniques for relaxing the problem and jointly learning all modules end-to-end with gradient descent: multitask learning; amortized inference; overparameterization; and a differentiable strategy for penalizing lengthy programs. Collectedly this toolbox improves the stability of gradient-guided program search, and suggests ways of learning both how to perceive input as discrete abstractions, and how to symbolically process those abstractions as programs.Comment: ICML 202

Neutron and Muon Studies of Spin Dynamics in Magnetic Systems

In this thesis I present an investigation on the spin dynamics observed during moment localisation, non-ergodic magnetic phase transitions, and weak itinerant electron magnetism. The pseudo-binary compound Y(Mn1-xAlx)2 has been investigated under the influence of equivalent opposing chemical and mechanical pressures using Muon Spin Relaxation. The results reveal the application of external mechanical pressure (4.5kbar) to destabilise the manganese moment, and produce a ground stte distinctly different to that seen under ambient pressure conditions. Short-range nuclear and spin correlations have been studies via diffuse neutron scattering, and through a combination of analysis techniques I have mapped the temperature dependence of these correlations and their evolution due to the substitution of manganese for aluminium. Applying new methods of hierarchical relaxation and non-extensive entopy I have studied the slow relaxation dynamics of the spin glass phase using Beutron Spin Echo spectroscopy. The results are dveloped further by applying the same analysis to a variety of glassy magnetic phenomena: spin glass freezing ((La1-xEr x)Al )Al2), and superparamagnetic blocking (Cr 1-xFe x). I have shown that within this framework the underlying freezing mechanisms result in distinctly different responses, and that in the case of spin glass relaxation an apparantly universal scaling relationship is present. Finally the results of a Muon Spin Relaxation study on the moment fluctuations in Au4V above the Curie temperature are reported. The temperature dependence of the muon spin relaxation rate is to be similar to that of the archetypal weak itinerant helimagnet, MnSi