The Mirage of Triangular Arbitrage in the Spot Foreign Exchange Market

We investigate triangular arbitrage within the spot foreign exchange market using high-frequency executable prices. We show that triangular arbitrage opportunities do exist, but that most have short durations and small magnitudes. We find intra-day variations in the number and length of arbitrage opportunities, with larger numbers of opportunities with shorter mean durations occurring during more liquid hours. We demonstrate further that the number of arbitrage opportunities has decreased in recent years, implying a corresponding increase in pricing efficiency. Using trading simulations, we show that a trader would need to beat other market participants to an unfeasibly large proportion of arbitrage prices to profit from triangular arbitrage over a prolonged period of time. Our results suggest that the foreign exchange market is internally self-consistent and provide a limited verification of market efficiency

Activating and inhibiting connections in biological network dynamics

<p>Abstract</p> <p>Background</p> <p>Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos.</p> <p>Results</p> <p>Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate.</p> <p>Conclusion</p> <p>The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks.</p> <p>Reviewers</p> <p>Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon) Xia (nominated by Mark Gerstein). For the full reviews, please go to the Reviewers' comments section.</p

Reducing Youth Risk Behaviors Through Interactive Theater Intervention

The reduction of risk behaviors in secondary schools is a key concern for parents, teachers, and school administrators. School is one of the primary contexts of socialization for young people; thus, the investment in school-based programs to reduce risk behaviors is essential. In this study, we report on youth who participated in an intervention designed to improve decision-making skills based on positive youth development approaches. We examine changes in decision-making skills before and after involvement in the Teen Interactive Theater Education (TITE) program and retrospective self-assessment of change in knowledge, abilities, and beliefs as a result of participating in TITE (n = 127). Youth that reported increases in knowledge, abilities, and beliefs due to the intervention (n = 89) were more likely to think about the consequences of their decisions and list options before making a decision compared to their counterparts that reported less overall learning (n = 38). Implications for intervention research and stakeholders are discussed

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a multi-armed bandit problem with delayed rewards. We observe that there is an apparent trade-off in choosing the learning signal: Waiting for the full reward to become available might take several weeks, hurting the rate at which learning happens, whereas measuring short-term proxy rewards reflects the actual long-term goal only imperfectly. We address this challenge in two steps. First, we develop a predictive model of delayed rewards that incorporates all information obtained to date. Full observations as well as partial (short or medium-term) outcomes are combined through a Bayesian filter to obtain a probabilistic belief. Second, we devise a bandit algorithm that takes advantage of this new predictive model. The algorithm quickly learns to identify content aligned with long-term success by carefully balancing exploration and exploitation. We apply our approach to a podcast recommendation problem, where we seek to identify shows that users engage with repeatedly over two months. We empirically validate that our approach results in substantially better performance compared to approaches that either optimize for short-term proxies, or wait for the long-term outcome to be fully realized.Comment: Presented at the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23

Ultrasonic Inspection of Wooden Pallet Parts Using Time of Flight

Wooden pallets exceed furniture and other solid wood products as the largest single use of sawn hardwood logs in the USA. Most wooden pallets are constructed from two types of pallet parts (Figure 1): (1) stringers—the structural center members that support the pallet load and (2) deckboards—the top and bottom facing members that provide dimensional stability and product placement. There are many variants of this basic design, but most pallets contain solid wood components that are produced from lumber or from the center cant material of logs. Cant material has a high percentage of defect area and is generally not highly valuable for other solid wood products. Therefore, the pallet manufacturing industry must make use of low-quality raw materials and yet produce a product that remains in service for many trips

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p &lt; .001; accuracy: 70%, p &lt; .001) and the CV-model (AUC: .59, p &lt; .001; accuracy: 64%, p &lt; .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Designing Shared Decision-Making Interventions for Dissemination and Sustainment: Can Implementation Science Help Translate Shared Decision Making Into Routine Practice

Shared decision making (SDM) is not widely practiced in routine care due to a variety of organizational, provider, patient, and contextual factors. This article explores how implementation science-which encourages attention to the multilevel contextual factors that influence the adoption, implementation, and sustainment of health care practices-can provide useful insights for increasing SDM use in routine practice. We engaged with stakeholders representing different organizations and geographic locations over three phases: 1) multidisciplinary workgroup meeting comprising researchers and clinicians (n = 11); 2) survey among a purposive sample of 47 patient advocates, clinicians, health care system leaders, funders, policymakers, and researchers; and 3) working session among diverse stakeholders (n = 30). The workgroup meeting identified priorities for action and research, which included targeting multiple audiences and levels, shifting culture toward valuing and supporting SDM, and considering contextual factors influencing SDM implementation. Survey respondents provided recommendations for increasing adoption, implementation, and maintenance of SDM in practice including providing tools to support SDM, obtaining stakeholders\u27 involvement, and raising awareness of the importance of SDM. Stakeholders in the working session provided recommendations on the design of a guide for implementation of SDM in clinical settings, strategies to disseminate educational curricula on SDM, and strategies to influence policies to increase SDM use. These specific recommendations serve as a call to action to pursuing specific promising strategies aimed at increasing SDM use in practice and enhance understanding of the perspectives of diverse stakeholders at multiple levels from an implementation science perspective that appear fruitful for further study and application

Using Bacteria to Determine Protein Kinase Specificity and Predict Target Substrates

The identification of protein kinase targets remains a significant bottleneck for our understanding of signal transduction in normal and diseased cellular states. Kinases recognize their substrates in part through sequence motifs on substrate proteins, which, to date, have most effectively been elucidated using combinatorial peptide library approaches. Here, we present and demonstrate the ProPeL method for easy and accurate discovery of kinase specificity motifs through the use of native bacterial proteomes that serve as in vivo libraries for thousands of simultaneous phosphorylation reactions. Using recombinant kinases expressed in E. coli followed by mass spectrometry, the approach accurately recapitulated the well-established motif preferences of human basophilic (Protein Kinase A) and acidophilic (Casein Kinase II) kinases. These motifs, derived for PKA and CK II using only bacterial sequence data, were then further validated by utilizing them in conjunction with the scan-x software program to computationally predict known human phosphorylation sites with high confidence