Differential Recurrent Neural Networks for Action Recognition

The long short-term memory (LSTM) neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model any sequential time-series data, where the current hidden state has to be considered in the context of the past hidden states. This property makes LSTM an ideal choice to learn the complex dynamics of various actions. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient motion patterns, when they gate the information that ought to be memorized through time. To address this problem, we propose a differential gating scheme for the LSTM neural network, which emphasizes on the change in information gain caused by the salient motions between the successive frames. This change in information gain is quantified by Derivative of States (DoS), and thus the proposed LSTM model is termed as differential Recurrent Neural Network (dRNN). We demonstrate the effectiveness of the proposed model by automatically recognizing actions from the real-world 2D and 3D human action datasets. Our study is one of the first works towards demonstrating the potential of learning complex time-series representations via high-order derivatives of states

ECONOMIC ANALYSIS OF CELLULASE PRODUCTION BY CLOSTRIDIUM THERMOCELLUM IN SOLID STATE AND SUBMERGED FERMENTATION

Dependence on foreign oil remains a serious issue for the U.S. economy. Additionally, automobile emissions related to petroleum-based, fossil fuel has been cited as one source of environmental problems, such as global warming and reduced air quality. Using agricultural and forest biomass as a source for the biofuel ethanol industry, provides a partial solution by displacing some fossil fuels. However, the use of high cost enzymes as an input is a significant limitation for ethanol production.Economic analyses of cellulase enzyme production costs using solid state cultivation (SSC) are performed and compared to the traditional submerged fermentation (SmF) method. Results from this study indicate that the unit costs for the cellulase enzyme production are $15.67 per kilogram ($/kg) and $40.36/kg, for the SSC and SmF methods, respectively, while the market price for the cellulase enzyme is$36.00/kg. Profitability analysis and sensitivity analysis also provide positive results.Since these results indicate that the SSC method is economical, ethanol production costs may be reduced, with the potential to make ethanol a viable supplemental fuel source in light of current political, economic and environmental issues

INVESTMENT ANALYSIS OF REPLACING ENDOPHYTE-INFECTED WITH ENDOPHYTE-FREE TALL FESCUE PASTURES

Cattle consuming tall fescue pastures infected with the endophyte Neotyphodium coenophialum often suffer physiological disorders that reduce animal performance. One solution is to replace endophyte-infected tall fescue pastures with an endophyte-free mixture. A benefit-cost analysis was conducted to determine the profitability of pasture restoration. The profitability of this action depends on the percentage of endophyte in existing pastures, the discount rate, and the stand life of the endophyte-free tall fescue variety. Our benefit-cost analysis results indicate that in order for pasture replacement to be profitable, the existing pastures must be infected with more than 16.1% of the endophyte, assuming the stand life of endophyte-free tall fescue is 12 years and the discount rate is three per cent. Additionally, a sensitivity analysis was conducted to determine the impact on the critical infestation level when the following parameters are changed: the discount rate, the baseline calving rates, and the pasture stand life. This research provides farmers with a practical investment analysis model for replacing endophyte-infected with endophyte-free tall fescue pastures.Land Economics/Use,

Economic Analysis of Cellulase Production by Clostridium thermocellum in Solid State and Submerged Fermentation

Replaced with revised version of paper 09/24/04.Resource /Energy Economics and Policy,

Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning

As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous. In this paper, we develop a method to automatically assign survey papers to a taxonomy. We collect the metadata of 144 LLM survey papers and explore three paradigms to classify papers within the taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models in two paradigms; pre-trained language models' fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our model surpasses an average human recognition level and that fine-tuning LLMs using weak labels generated by a smaller model, such as the GCN in this study, can be more effective than using ground-truth labels, revealing the potential of weak-to-strong generalization in the taxonomy classification task.Comment: TL;DR: We collected metadata about LLM surveys and developed a method for categorizing them into a taxonomy, indicating the superiority of graph representation learning over language models and revealing the efficacy of fine-tuning using weak label

A Broad and General Sequential Sampling Scheme

In this paper, we propose a broad and general sequential sampling scheme, which incorporates four different types of sampling procedures: i) the classic Anscombe-Chow-Robbins purely sequential sampling procedure; ii) the ordinary accelerated sequential sampling procedure; iii) the relatively new k-at-a-time purely sequential sampling procedure; iv) the new k-at-a-time improved accelerated sequential sampling procedure. The first-order and second-order properties of this general sequential sampling scheme are fully investigated with two illustrations on minimum risk point estimation for the mean of a normal distribution and on bounded variance point estimation for the location parameter of a negative exponential distribution, respectively. We also provide extensive computational simulation studies and real data analyses for each illustration