A CAT Algorithm for the Exhaustive Generation of Ice Piles

We present a CAT (constant amortized time) algorithm for generating those partitions of n that are in the ice pile model IPM(k)(n), a generalization of the sand pile model SPM(n). More precisely, for any fixed integer k, we show that the negative lexicographic ordering naturally identifies a tree structure on the lattice IPM(k)(n): this lets us design an algorithm which generates all the ice piles of IPM(k)(n) in amortized time O(1) and in space O(root n)

On the Exhaustive Generation of Plane Partitions

We present a CAT (Constant Amortized Time) algorithm for generating all plane partitions of an integer n, that is, all integer matrices with non-increasing rows and columns having sum n

On the set of Fixed Points of the Parallel Symmetric Sand Pile Model

Sand Pile Models are discrete dynamical systems emphasizing the phenomenon of $\textit{Self-Organized Criticality}$. From a configuration composed of a finite number of stacked grains, we apply on every possible positions (in parallel) two grain moving transition rules. The transition rules permit one grain to fall to its right or left (symmetric) neighboring column if the difference of height between those columns is larger than 2. The model is nondeterministic and grains always fall downward. We propose a study of the set of fixed points reachable in the Parallel Symmetric Sand Pile Model (PSSPM). Using a comparison with the Symmetric Sand Pile Model (SSPM) on which rules are applied once at each iteration, we get a continuity property. This property states that within PSSPM we can't reach every fixed points of SSPM, but a continuous subset according to the lexicographic order. Moreover we define a successor relation to browse exhaustively the sets of fixed points of those models

Nest-Site Selection of Golden Eagles and Ferruginous Hawks and Diet Composition of Sensitive Raptor Species Using Metabarcoding Analysis in the Uinta Basin and Ashley National Forest, UT, USA

Development and climate change in the sagebrush habitats are causing population declines of North American hawks and eagles. For these species, understanding the landscape features that are preferred for nesting and the prey they consume in sagebrush habitats are important in developing conservation plans. Specifically, we know little of the preferred nest-sites and diet of Ferruginous Hawks (Buteo regalis) and Golden Eagles (Aquila chrysaetos) many locales. In our study, we determined the landscape characteristics associated with nest sites for these two raptor species in the Uintah Basin, UT to predict where nests may occur in our study area. We found that slope, elevation, distance to nearest oil and gas wells, geology, and facing south were the most important variables in characterizing Golden Eagle nest-sites. Elevation, slope, vegetation type, and distance to nearest oil and gas wells were the most important variables in characterizing Ferruginous Hawk nest-sites. In addition, we looked at the diets of Golden Eagles, Ferruginous Hawks, and Northern Goshawks in the Uinta Basin, UT using a genetic analysis method novel to raptors. We found species consistent with previous diet studies and detected prey items not previously reported, including the Western Whiptail (Aspidocelis tigris), Domestic Cow (Bos Taurus), Domestic Pig (Sus scrofa), and Rock Bass (Amboplites rupestris) within Ferruginous Hawk samples. Results from our study can provide managers with tools to better survey for nest-sites and to provide an alternative method of diet analysis to provide insight into prey species important to these raptors

Analyzing and Explaining Image Classifiers via Diffusion Guidance

While deep learning has led to huge progress in complex image classification tasks like ImageNet, unexpected failure modes, e.g. via spurious features, call into question how reliably these classifiers work in the wild. Furthermore, for safety-critical tasks the black-box nature of their decisions is problematic, and explanations or at least methods which make decisions plausible are needed urgently. In this paper, we address these problems by generating images that optimize a classifier-derived objective using a framework for guided image generation. We analyze the behavior and decisions of image classifiers by visual counterfactual explanations (VCEs), detection of systematic mistakes by analyzing images where classifiers maximally disagree, and visualization of neurons to verify potential spurious features. In this way, we validate existing observations, e.g. the shape bias of adversarially robust models, as well as novel failure modes, e.g. systematic errors of zero-shot CLIP classifiers, or identify harmful spurious features. Moreover, our VCEs outperform previous work while being more versatile

Influence Scores at Scale for Efficient Language Data Sampling

Modern ML systems ingest data aggregated from diverse sources, such as synthetic, human-annotated, and live customer traffic. Understanding \textit{which} examples are important to the performance of a learning algorithm is crucial for efficient model training. Recently, a growing body of literature has given rise to various "influence scores," which use training artifacts such as model confidence or checkpointed gradients to identify important subsets of data. However, these methods have primarily been developed in computer vision settings, and it remains unclear how well they generalize to language-based tasks using pretrained models. In this paper, we explore the applicability of influence scores in language classification tasks. We evaluate a diverse subset of these scores on the SNLI dataset by quantifying accuracy changes in response to pruning training data through random and influence-score-based sampling. We then stress-test one of the scores -- "variance of gradients" (VoG) from Agarwal et al. (2022) -- in an NLU model stack that was exposed to dynamic user speech patterns in a voice assistant type of setting. Our experiments demonstrate that in many cases, encoder-based language models can be finetuned on roughly 50% of the original data without degradation in performance metrics. Along the way, we summarize lessons learned from applying out-of-the-box implementations of influence scores, quantify the effects of noisy and class-imbalanced data, and offer recommendations on score-based sampling for better accuracy and training efficiency.Comment: Accepted at EMNLP '2

Mississippi Libraries 86(2) Summer 2023 (Full issue)

Complete issue of Mississippi Libraries Volume 86 Number 2 Summer 202