275 research outputs found
The assay of Rochelle salt
Thesis (M.A.)--Boston University, 1940The author has reported in a previous paper, the results of an investigation of the United States Pharmacopoeia XI method for the assay of Rochelle salt. In this investigation, it was found that all samples of Rochelle salt bought on the market, were below standard requirement by the official method, were below standard requirement when assayed by the official method. The United States Pharmacopocia XI requires that Rochelle salt contain no less that 99 percent of KNaC4H6O6.4H2O. The results obtained indicated that the samples assayed were close to 90 percent.
To account for this discrepancy the following possibilites were considered as probable explanations; (1) the official method gives erroneous results, (2) the samples were not of standard quality, and (3) the requirements were too high. Since the samples assayed were from large chemical companies, and all of them gave the same result, it was assumed that these samples were very likely representative of the Rochelle salt available, and would be of standard quality. References were fiven to the literature where it is reported that it is difficult to obtain pure crystals of potassium and sodium tartrate. When the salt is crystallized by the usual method, it was found the product obtained was a mixture of potassium and sodium tartrate, potassium tartrate and sodium tartrate
Unilateral vs. Bilateral Incentives: Evidence from the U.S. Pork Industry
The idea that individuals adapt their behaviors in response to changes in incentive systems is fundamental to most economic analysis. This paper incorporates the concept of price discovery costs into the incentive theory to offer a theoretical model and empirical evidence on the differential incentive effects of long-term contracts and spot markets. Using the US pork industry case where procuring intertemporally consistent weights of hogs have been critical to pork processors, we show why the effectiveness of unilaterally determined and posted incentive price for the hog quality by the pork packers on the intertemporal consistency erodes and why a bilateral incentive structure built through long-term hog procurement contracts is demanded, in the presence of volatile hog price and feed price movements. The MGARCH model analysis of USDA AMS data supported our hypotheses that long-term hog procurement contracts would help moderate the erosion relative to the spot markets, resulting greater intertemporal consistency of hog weights.long-term contracts, incentive effects, price discovery costs, MGARCH model, Livestock Production/Industries,
A (Mis)guided Adventure Tourism Experience: An Autoethnographic Analysis of Mountaineering in Bolivia
Due to the fast growing nature of the adventure tourism industry and the commodification of adventure activities therein, improved understanding of adventure tourism experiences and mountaineer adventure tourists in particular is needed. In an effort to move beyond traditional market segmentation approaches, this study analysed autoethnographical data from an adventure tourism mountaineering experience in Bolivia. This autoethnographic method facilitated a deeper understanding of mountaineering adventure tourism experiences and allowed for a multifaceted view of risk perceptions that has often been neglected in the literature. Data were analysed with a robust psychological framework (i.e. reversal theory) that was used to explain: (a) paradoxical desires for risk and safety in adventure tourism and (b) emotional and motivational fluctuations experienced by mountaineer adventure tourists. The importance of creating a ‘protective frame’ to ensure enjoyable experiences was identified, along with key factors that influenced this frame (e.g. guide behaviour, equipment, safety management procedures, other tourists, environmental conditions). Implications for adventure tourism practitioners are discussed, along with theoretical analyses. The utility of autoethnographic research in adventure settings, particularly in conjunction with established psychological theory, is highlighted and suggested as a fruitful avenue through which to enhance the adventure tourism discourse
Connector 0.5: A unified framework for graph representation learning
Graph representation learning models aim to represent the graph structure and
its features into low-dimensional vectors in a latent space, which can benefit
various downstream tasks, such as node classification and link prediction. Due
to its powerful graph data modelling capabilities, various graph embedding
models and libraries have been proposed to learn embeddings and help
researchers ease conducting experiments. In this paper, we introduce a novel
graph representation framework covering various graph embedding models, ranging
from shallow to state-of-the-art models, namely Connector. First, we consider
graph generation by constructing various types of graphs with different
structural relations, including homogeneous, signed, heterogeneous, and
knowledge graphs. Second, we introduce various graph representation learning
models, ranging from shallow to deep graph embedding models. Finally, we plan
to build an efficient open-source framework that can provide deep graph
embedding models to represent structural relations in graphs. The framework is
available at https://github.com/NSLab-CUK/Connector.Comment: An unified framework for graph representation learnin
Efficient Parallel Audio Generation using Group Masked Language Modeling
We present a fast and high-quality codec language model for parallel audio
generation. While SoundStorm, a state-of-the-art parallel audio generation
model, accelerates inference speed compared to autoregressive models, it still
suffers from slow inference due to iterative sampling. To resolve this problem,
we propose Group-Masked Language Modeling~(G-MLM) and Group Iterative Parallel
Decoding~(G-IPD) for efficient parallel audio generation. Both the training and
sampling schemes enable the model to synthesize high-quality audio with a small
number of iterations by effectively modeling the group-wise conditional
dependencies. In addition, our model employs a cross-attention-based
architecture to capture the speaker style of the prompt voice and improves
computational efficiency. Experimental results demonstrate that our proposed
model outperforms the baselines in prompt-based audio generation.Comment: This work has been submitted to the IEEE for possible publication.
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Examining Technology Perception and User Competence on Two Types of Smartphone Usages
This study intends to explain smartphone usage behaviours in the post-adoption stage of information systems (IS) based on the IS continuance model, the technology acceptance model (TAM), and the competence of the users. In this study, smartphone usage is divided into two types: usage of the smartphone’s device functions and usage of applications. This is the first time this concept has been proposed and empirically tested. The results found strong predictors of user satisfaction (perceived usefulness and perceived ease of use) toward smartphone satisfaction and finally confirmed the influence of smartphone function use on smartphone app use. Finally, several important theoretical and practical implications and directions for future research based on limitations are suggested
SNAC: Speaker-normalized affine coupling layer in flow-based architecture for zero-shot multi-speaker text-to-speech
Zero-shot multi-speaker text-to-speech (ZSM-TTS) models aim to generate a
speech sample with the voice characteristic of an unseen speaker. The main
challenge of ZSM-TTS is to increase the overall speaker similarity for unseen
speakers. One of the most successful speaker conditioning methods for
flow-based multi-speaker text-to-speech (TTS) models is to utilize the
functions which predict the scale and bias parameters of the affine coupling
layers according to the given speaker embedding vector. In this letter, we
improve on the previous speaker conditioning method by introducing a
speaker-normalized affine coupling (SNAC) layer which allows for unseen speaker
speech synthesis in a zero-shot manner leveraging a normalization-based
conditioning technique. The newly designed coupling layer explicitly normalizes
the input by the parameters predicted from a speaker embedding vector while
training, enabling an inverse process of denormalizing for a new speaker
embedding at inference. The proposed conditioning scheme yields the
state-of-the-art performance in terms of the speech quality and speaker
similarity in a ZSM-TTS setting.Comment: Accepted to IEEE Signal Processing Letter
Large network multi-level control for CAV and Smart Infrastructure: AI-based Fog-Cloud collaboration
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