Hasil untuk "Trade associations"

Daniel Matthew Cer, Yinfei Yang, Sheng-yi Kong et al.

We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub.

Daniel Matthew Cer, Yinfei Yang, Sheng-yi Kong et al.

We present easy-to-use TensorFlow Hub sentence embedding models having good task transfer performance. Model variants allow for trade-offs between accuracy and compute resources. We report the relationship between model complexity, resources, and transfer performance. Comparisons are made with baselines without transfer learning and to baselines that incorporate word-level transfer. Transfer learning using sentence-level embeddings is shown to outperform models without transfer learning and often those that use only word-level transfer. We show good transfer task performance with minimal training data and obtain encouraging results on word embedding association tests (WEAT) of model bias.

Kristin J. Forbes, F. Warnock

T. Hendershott, Charles M. Jones, A. Menkveld

Murray Z. Frank, V. Goyal

"This paper examines the relative importance of many factors in the capital structure decisions of publicly traded American firms from 1950 to 2003. The most reliable factors for explaining market leverage are: median industry leverage (p effect on leverage), market-to-book assets ratio ( - ), tangibility (p), profits ( - ), log of assets (p), and expected inflation (p). In addition, we find that dividend-paying firms tend to have lower leverage. When considering book leverage, somewhat similar effects are found. However, for book leverage, the impact of firm size, the market-to-book ratio, and the effect of inflation are not reliable. The empirical evidence seems reasonably consistent with some versions of the trade-off theory of capital structure." Copyright (c) 2009 Financial Management Association International..

Janet Kiholm Smith

Harry Flam, E. Helpman

G. Chichilnisky

Simone Di Gregorio, Paul Dütting, Federico Fusco et al.

Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold private valuations. We study this problem from the perspective of a broker, in a regret minimization framework. At each time step, a new seller and buyer arrive, and the broker has to propose a mechanism that is incentive-compatible and individually rational, with the goal of maximizing profit. We propose a learning algorithm that guarantees a nearly tight $\tilde{O}(\sqrt{T})$ regret in the stochastic setting when seller and buyer valuations are drawn i.i.d. from a fixed and possibly correlated unknown distribution. We further show that it is impossible to achieve sublinear regret in the non-stationary scenario where valuations are generated upfront by an adversary. Our ambitious benchmark for these results is the best incentive-compatible and individually rational mechanism. This separates us from previous works on efficiency maximization in bilateral trade, where the benchmark is a single number: the best fixed price in hindsight. A particular challenge we face is that uniform convergence for all mechanisms' profits is impossible. We overcome this difficulty via a careful chaining analysis that proves convergence for a provably near-optimal mechanism at (essentially) optimal rate. We further showcase the broader applicability of our techniques by providing nearly optimal results for the joint ads problem.

J. Shanalin, R. Chandirakala, K. Chandrakumar

The present study aimed to evaluate the nutritional variability and genetic advancement across F3 and F4 generations in three red sorghum crosses: Paiyur 2 × IS 20603, Paiyur 2 × IS 21731, and CO 32 × Paiyur 2. Key nutritional traits including protein, fat, fibre, ash, moisture, and carbohydrate contents were assessed using descriptive statistics, correlation analysis, and principal component analysis (PCA). Comparative analysis revealed significant improvement in mean protein and fat contents from F3 and F4 generations, with Cross 2 (Paiyur 2 × IS 21731) recording the highest protein (11.45%) and fat (5.66%) values, indicating strong transgressive segregation and potential for nutritional enhancement. Trait ranges broadened in the F4 generation, reflecting greater genetic variability, while standard deviations and coefficients of variation remained within acceptable limits, demonstrating controlled variability and increased trait stability. Correlation heatmaps revealed strong positive relationships among protein, fat, and carbohydrate contents, and strong negative correlations between ash and fibre, and between ash and carbohydrate. These associations underscored the interdependence and trade-offs among nutritional traits, providing critical insights for selection strategies. PCA results indicated that the first two principal components (PC1 and PC2) explained 95.56% of the total variation, with ash and carbohydrate being the most discriminating traits. Traits like protein, fat, and moisture were tightly clustered, confirming their co-expression. Cross 2 emerged as a superior cross for nutritional improvement, while Crosses 1 and 3 exhibited better early trait stability. Overall, the study provided a robust framework for selecting nutritionally rich, genetically stable red sorghum lines for future crop improvement programs.

Steven Joffe, Ronac Mamtani, Ravi B Parikh et al.

Objectives To assess the specificity of postmarketing requirement (PMR) statements and associations between PMR statement specificity and PMR study characteristics, timeliness and regulatory decisions.Methods and analysis This was a cross-sectional analysis of publicly available Food and Drug Administration (FDA) databases to characterise PMR statements for oncology accelerated approvals (AAs) between January 2011 and July 2023. Characteristics of trials supporting AA and PMR studies were identified from product labels on the Drugs@FDA database and ClinicalTrials.gov. Main outcomes and measures included PMR statement characteristics, PMR study submission timeliness (on-time vs late) and regulatory decision (regular approval vs withdrawal).Results We analysed 181 PMR statements for 161 oncology indications. Most PMR statements specified target population (98%), endpoints (81% (44% included clinical endpoints; 37% surrogate endpoints only)), use of randomisation (63%) and comparator (54%). Fewer PMR statements specified a particular trial or protocol (45%), follow-up duration (30%), enrolment targets (26%), multicentre trial (24%), double-blinding (13%) or enrolment diversity (8%). PMR statements for indications granted regular approval were more likely than those for withdrawn indications to specify follow-up duration <1 year (27% vs 0%, p<0.001), allow endpoints other than overall or progression-free survival (27% vs 4%, p=0.01) and mention a specific trial or protocol (71% vs 36%, p=0.003). Compared to those submitted late, on-time PMR studies had fewer sites (110 vs 156, p=0.03), less use of blinding (20% vs 42%, p=0.02), more use of a continuous trial for AA and PMR (37% vs 8%, p=0.003) and more use of primary endpoints other than overall or progression-free survival (37% vs 6%, p<0.001).Conclusion PMR statement specificity for oncology AAs varies substantially. Less rigorous PMR statement and study characteristics were associated with timely PMR study submission and transition to regular approval but with important trade-offs. Given that AAs are granted without demonstrated clinical benefit, improving the balance between PMR study timeliness and rigour should be a priority when negotiating PMR statements.

Adolfo Cristobal Campoamor, Osiris Jorge Parcero

This paper proposes a two blocks and three regions economic geography model that can account for the most salient stylized facts experienced by Eastern European transition economies during the period 1990 2005. In contrast to the existing literature, which has favored technological explanations, trade liberalization is the only driving force. The model correctly predicts that in the first half of the period, trade liberalization led to divergence in GDP per capita, both between the West and the East and within the East. Consistent with the data, in the second half of the period, this process was reversed and convergence became the dominant force.

Collin Sakal, Wenxing Zhao, Wenxin Xu et al.

Abstract Background Adequate sleep and physical activity promote longevity among older adults. Caffeine supplementation could be used to increase activity levels, but its effects have not been examined in real-world settings where potential trade-offs regarding sleep quality are also considered. This study sought to examine associations between caffeine intake and accelerometer-derived sleep and activity among older adults under free-living conditions. Methods Cross-sectional data were gathered from older adults aged 65 + in the 2011-14 National Health and Nutrition Examination Surveys (NHANES). Sleep parameters were derived from accelerometer data using a data-driven machine learning approach. Caffeine consumption was categorized based on weight (in mg/kg: 0, > 0 to 1, > 1 to 2, > 2 to 3, > 3) and absolute consumption (in mg: 0, > 0 to 100, > 100 to 200, > 200 to 300, > 300). Multivariable survey weighted regression models were used to examine associations between caffeine with average total daytime activity, highly active minutes, sleep duration, and sleep efficiency. Covariate adjustments included demographics, body mass index, smoking, alcohol, sleep disorders, sleep parameters (for activity outcomes), and daytime activity (for sleep outcomes). Results N = 1,629 NHANES participants were included. Caffeine consumption was highest in the morning. In adjusted models, older adults who consumed > 3 mg/kg were 16.5% more active during the day (95% CI: 9.0, 24.4) and were highly active for 42.8 additional minutes (95% CI: 20.3, 65.4) compared to non-consumers. Similar results were observed for absolute consumption (mg), and significant but lower magnitude effects were observed for lower levels of consumption. Caffeine showed no association with sleep efficiency, while low levels of consumption (≤ 1 mg/kg, ≤ 200 mg) were associated with longer sleep duration. Conclusions Under free-living dietary, sleep, and activity patterns, this study found older adults who consumed caffeine were more active than non-consumers. Overall consumption was not associated with sleep efficiency but was associated with longer sleep duration at ≤ 1 mg/kg and ≤ 200 mg. Future causal studies should determine the effectiveness of caffeine for promoting higher activity in older adult populations.

A. Dufour, R. Engle

Julia Di

This paper presents a trade study analysis to design and evaluate the perception system architecture for ReachBot. ReachBot is a novel robotic concept that uses grippers at the end of deployable booms for navigation of rough terrain such as walls of caves and lava tubes. Previous studies on ReachBot have discussed the overall robot design, placement and number of deployable booms, and gripper mechanism design; however, analysis of the perception and sensing system remains underdeveloped. Because ReachBot can extend and interact with terrain over long distances on the order of several meters, a robust perception and sensing strategy is crucial to identify grasping locations and enable fully autonomous operation. This trade study focuses on developing the perception trade space and realizing such perception capabilities for a physical prototype. This work includes analysis of: (1) multiple-range sensing strategies for ReachBot, (2) sensor technologies for subsurface climbing robotics, (3) criteria for sensor evaluation, (4) positions and modalities of sensors on ReachBot, and (5) map representations of grasping locations. From our analysis, we identify the overall perception strategy and hardware configuration for a fully-instrumented case study mission to a Martian lava tube, and identify specific sensors for a hardware prototype. The final result of our trade study is a system design conducive to benchtop testing and prototype hardware development.

Aleksandar Tošić, Niki Hrovatin, Jernej Vičič

Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains. In this paper we introduce an innovative methodology designed to detect wash trading activities beyond surface-level transactions. Our approach integrates NFT ownership traces with the Ethereum Transaction Network, encompassing the complete historical record of all Ethereum account normal transactions. By analyzing both networks, our method offers a notable advancement over techniques proposed by existing research. We analyzed the wash trading activity of 7 notable NFT collections. Our results show that wash trading in unregulated NFT markets is an underestimated concern and is much more widespread both in terms of frequency as well as volume. Excluding the Meebits collection, which emerged as an outlier, we found that wash trading constituted up to 25% of the total trading volume. Specifically, for the Meebits collection, a staggering 93% of its total trade volume was attributed to wash trading.

Anna C Rivara, Omar Galárraga, Melania Selu et al.

In Samoa, adult Type 2 diabetes prevalence has increased within the past 30 years. Patient preferences for care are factors known to influence treatment adherence and are associated with reduced disease progression and severity. However, patient preferences for diabetes care, generally, are understudied, and other patient-centered factors such as willingness-to-pay (WTP) for diabetes treatment have never been explored in this setting. Discrete Choice Experiments (DCE) are useful tools to elicit preferences and WTP for healthcare. DCEs present patients with hypothetical scenarios composed of a series of multi-alternative choice profiles made up of attributes and levels. Patients choose a profile based on which attributes and levels may be preferable for them, thereby quantifying and identifying locally relevant patient-centered preferences. This paper presents the protocol for the design, piloting, and implementation of a DCE identifying patient preferences for diabetes care, in Samoa. Using an exploratory sequential mixed methods design, formative data from a literature review and semi-structured interviews with n = 20 Samoan adults living with Type 2 diabetes was used to design a Best-Best DCE instrument. Experimental design procedures were used to reduce the number of choice-sets and balance the instrument. Following pilot testing, the DCE is being administered to n = 450 Samoan adults living with diabetes, along with associated questionnaires, and anthropometrics. Subsequently, we will also be assessing longitudinally how preferences for care change over time. Data will be analyzed using progressive mixed Rank Order Logit models. The results will identify which diabetes care attributes are important to patients (p < 0.05), examine associations between participant characteristics and preference, illuminate the trade-offs participants are willing to make, and the probability of uptake, and WTP for specific attributes and levels. The results from this study will provide integral data useful for designing and adapting efficacious diabetes intervention and treatment approaches in this setting.

M. Keramati, A. Dezfouli, Payam Piray

Instrumental responses are hypothesized to be of two kinds: habitual and goal-directed, mediated by the sensorimotor and the associative cortico-basal ganglia circuits, respectively. The existence of the two heterogeneous associative learning mechanisms can be hypothesized to arise from the comparative advantages that they have at different stages of learning. In this paper, we assume that the goal-directed system is behaviourally flexible, but slow in choice selection. The habitual system, in contrast, is fast in responding, but inflexible in adapting its behavioural strategy to new conditions. Based on these assumptions and using the computational theory of reinforcement learning, we propose a normative model for arbitration between the two processes that makes an approximately optimal balance between search-time and accuracy in decision making. Behaviourally, the model can explain experimental evidence on behavioural sensitivity to outcome at the early stages of learning, but insensitivity at the later stages. It also explains that when two choices with equal incentive values are available concurrently, the behaviour remains outcome-sensitive, even after extensive training. Moreover, the model can explain choice reaction time variations during the course of learning, as well as the experimental observation that as the number of choices increases, the reaction time also increases. Neurobiologically, by assuming that phasic and tonic activities of midbrain dopamine neurons carry the reward prediction error and the average reward signals used by the model, respectively, the model predicts that whereas phasic dopamine indirectly affects behaviour through reinforcing stimulus-response associations, tonic dopamine can directly affect behaviour through manipulating the competition between the habitual and the goal-directed systems and thus, affect reaction time.

Bernhard K Meister

Investors trade shifting prices, portfolio values, and in turn their ability to borrow. Concentrated ownership, high price impact and low collateral requirements are propitious for arbitrage.

George Bouzianis, Lane P. Hughston, Leandro Sánchez-Betancourt

We consider a pair of traders in a market where the information available to the second trader is a strict subset of the information available to the first trader. The traders make prices based on the information available concerning a security that pays a random cash flow at a fixed time $T$ in the future. Market information is modelled in line with the scheme of Brody, Hughston & Macrina (2007, 2008) and Brody, Davis, Friedman & Hughston (2009). The risk-neutral distribution of the cash flow is known to the traders, who make prices with a fixed multiplicative bid-offer spread and report their prices to a game master who declares that a trade has been made when the bid price of one of the traders crosses the offer price of the other. We prove that the value of the first trader's position is strictly greater than that of the second. The results are analyzed by use of simulation studies and generalized to situations where (a) there is a hierarchy of traders, (b) there are multiple successive trades, and (c) there is inventory aversion.