The Pulping of Hardwood Sawdust by the Neutral Sulfite Semichemical Process for Use in Corrugating Medium

The need for using less expensive raw materials is ever increasing. Here the use of hardwood sawdust as one of these raw materials was investigated for use in Semichemical Corrugating Medium. It was found that 20 to 30% sawdust could be used in the furnish without a significant loss in sheet strength. It was also found that sawdust could be pulped either separately or combined with hardwood chips giving the same product quality. This was accomplished by either a regular neutral sulfite semichemical cook or by a vapor phase cook. The conventional disk refiner was found to do the best job of refining, when the plate clearance is very close. The use of 20 to 30% sawdust in the furnish could lead to a substantial cost savings, and would aid in pollution control in that it would end the disposal problem of the sawdust

Genetic Assessment of Breeding Patterns and Population Size of the Sicklefin Lemon Shark Negaprion acutidens in a Tropical Marine Protected Area: Implications for Conservation and Management

The sicklefin lemon shark (Negaprion acutidens) is found in coastal waters of the Indo-West Pacific where it has been assessed by the IUCN as threatened with extinction. Information on the species’ reproductive ecology and local abundance, which are important considerations for effective management, remain limited. I used genetic analyses of tissue samples collected from juvenile N. acutidens, at the Curieuse Marine National Park (CMNP), Seychelles, between 2014-2017, to (1) estimate the number of adults reproducing at CMNP annually and (2) identify their breeding patterns through pedigree reconstruction. I report strong evidence of philopatry; primarily in females. Over the study period 25 reconstructed females produced multiple litters; the majority (88%) displayed biennial parturition. The remaining 12% displayed annual parturition. Multiple paternity was common (66% of 58 litters; mean number of sires per litter = 1.92). Convenience polyandry provides a likely explanation for this and may be driven by biased operational sex ratios during mating. Male philopatry to CMNP was low (17% of 114 reconstructed males) and may be influenced by habitat availability. Males likely breed over broader geographic scales than females. The breeding patterns I report are similar to those identified in other populations of lemon sharks and are likely applicable across the genus. In Seychelles, shark stocks are in decline due to overfishing. The high female philopatry in N. acutidens suggests protection of parturition sites, such as CMNP, is likely important to the conservation of local populations. However, adult life-stages, particularly males due to wider-ranging behaviour, are still subject to fishing pressure outside the park. Additional management measures are required to prevent further population declines. Species-specific management appears to be the best approach. The introduction of science-based fisheries control measures, for N. acutidens and other shark species, should be an urgent priority in the Seychelles

A mathematical theory of semantic development in deep neural networks

An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: what are the theoretical principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge by integrating across many individual experiences? We address this question by mathematically analyzing the nonlinear dynamics of learning in deep linear networks. We find exact solutions to this learning dynamics that yield a conceptual explanation for the prevalence of many disparate phenomena in semantic cognition, including the hierarchical differentiation of concepts through rapid developmental transitions, the ubiquity of semantic illusions between such transitions, the emergence of item typicality and category coherence as factors controlling the speed of semantic processing, changing patterns of inductive projection over development, and the conservation of semantic similarity in neural representations across species. Thus, surprisingly, our simple neural model qualitatively recapitulates many diverse regularities underlying semantic development, while providing analytic insight into how the statistical structure of an environment can interact with nonlinear deep learning dynamics to give rise to these regularities

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output map, such networks have nonlinear gradient descent dynamics on weights that change with the addition of each new hidden layer. We show that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions. We provide an analytical description of these phenomena by finding new exact solutions to the nonlinear dynamics of deep learning. Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite, depth independent, delay in learning speed relative to shallow networks. We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initializations cannot. We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times. We further show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate in a special regime known as the edge of chaos.Comment: Submission to ICLR2014. Revised based on reviewer feedbac

A circumpolar perspective on fluvial sediment flux to the Arctic ocean

Quantification of sediment fluxes from rivers is fundamental to understanding land‐ocean linkages in the Arctic. Numerous publications have focused on this subject over the past century, yet assessments of temporal trends are scarce and consensus on contemporary fluxes is lacking. Published estimates vary widely, but often provide little accessory information needed to interpret the differences. We present a pan‐arctic synthesis of sediment flux from 19 arctic rivers, primarily focusing on contributions from the eight largest ones. For this synthesis, historical records and recent unpublished data were compiled from Russian, Canadian, and United States sources. Evaluation of these data revealed no long‐term trends in sediment flux, but did show stepwise changes in the historical records of two of the rivers. In some cases, old values that do not reflect contemporary fluxes are still being reported, while in other cases, typographical errors have been propagated into the recent literature. Most of the discrepancy among published estimates, however, can be explained by differences in years of records examined and gauging stations used. Variations in sediment flux from year to year in arctic rivers are large, so estimates based on relatively few years can differ substantially. To determine best contemporary estimates of sediment flux for the eight largest arctic rivers, we used a combination of newly available data, historical records, and literature values. These estimates contribute to our understanding of carbon, nutrient, and contaminant transport to the Arctic Ocean and provide a baseline for detecting future anthropogenic or natural change in the Arctic

Testing multi-alternative decision models with non-stationary evidence

Recent research has investigated the process of integrating perceptual evidence toward a decision, converging on a number of sequential sampling choice models, such as variants of race and diffusion models and the non-linear leaky competing accumulator (LCA) model. Here we study extensions of these models to multi-alternative choice, considering how well they can account for data from a psychophysical experiment in which the evidence supporting each of the alternatives changes dynamically during the trial, in a way that creates temporal correlations. We find that participants exhibit a tendency to choose an alternative whose evidence profile is temporally anti-correlated with (or dissimilar from) that of other alternatives. This advantage of the anti-correlated alternative is well accounted for in the LCA, and provides constraints that challenge several other models of multi-alternative choice

Alternative protein conformations: yeast iso-1-cytochrome c and heme crevice dynamics

The field of protein biochemistry has been dominated by the dogma that a protein sequence yields a 3-dimensional structure important for a singular function. More modern insights are beginning to demonstrate that proteins are not static structures. Rather, proteins undergo numerous conformational fluctuations yielding an ensemble of conformational populations. Conformational change can result in changed or altered protein function. Small or large energetic barriers existing between conformers regulate the ease with which a protein can sample alternative conformations. In the dissertation work presented here, alternative conformations of yeast iso-1-cytochrome c are investigated with particular emphasis on heme crevice loop dynamics. The heme crevice loop, or O-loop D, is a highly conserved, dynamic region. Conformational changes in O-loop D lead to altered electron transfer and peroxidase activity in cytochrome c (Cytc). As Cytc participates in both the electron transport chain and functions as a peroxidase during apoptosis, it is important to understand how this conformational change is regulated. Within O-loop D we investigate the effects of a trimethyllysine to alanine mutation and a destabilizing leucine to alanine mutation at residues 72 and 85, respectively, on heme crevice dynamics. Residue 72 plays an important role in regulating access to alternative heme crevice conformers. Of particular interest, residue 72 plays a role in regulating access to a peroxidase capable conformer of Cytc, a function of Cytc during the early stages of apoptosis. We have also solved the structure of the first monomeric Cytc structure in a peroxidase capable conformer, as well as, a dimeric Cytc structure with CYMAL-6 protruding into the interior of the heme cavity, in a manner potentially similar to the Cytc/cardiolipin interaction

Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks

Transformer networks have seen great success in natural language processing and machine vision, where task objectives such as next word prediction and image classification benefit from nuanced context sensitivity across high-dimensional inputs. However, there is an ongoing debate about how and when transformers can acquire highly structured behavior and achieve systematic generalization. Here, we explore how well a causal transformer can perform a set of algorithmic tasks, including copying, sorting, and hierarchical compositions of these operations. We demonstrate strong generalization to sequences longer than those used in training by replacing the standard positional encoding typically used in transformers with labels arbitrarily paired with items in the sequence. We search for the layer and head configuration sufficient to solve these tasks, then probe for signs of systematic processing in latent representations and attention patterns. We show that two-layer transformers learn reliable solutions to multi-level problems, develop signs of task decomposition, and encode input items in a way that encourages the exploitation of shared computation across related tasks. These results provide key insights into how attention layers support structured computation both within a task and across multiple tasks.Comment: 18 page