Indiana University-Purdue University Indianapolis (IUPUI) / Biomechanical Big Data is of great significance to precision health applications, among which we take special interest in Physical Activity Detection (PAD). In this study, we have performed extensive research on deep learning-based PAD from biomechanical big data, focusing on the challenges raised by the need for real-time edge inference. First, considering there are many places we can place the motion sensors, we have thoroughly compared and analyzed the location difference in terms of deep learning-based PAD performance. We have further compared the difference among six sensor channels (3-axis accelerometer and 3-axis gyroscope). Second, we have selected the optimal sensor and the optimal sensor channel, which can not only provide sensor usage suggestions but also enable ultra-lowpower application on the edge. Third, we have investigated innovative methods to minimize the training effort of the deep learning model, leveraging the transfer learning strategy. More specifically, we propose to pre-train a transferable deep learning model using the data from other subjects and then fine-tune the model using limited data from the target-user. In such a way, we have found that, for single-channel case, the transfer learning can effectively increase the deep model performance even when the fine-tuning effort is very small. This research, demonstrated by comprehensive experimental evaluation, has shown the potential of ultra-low-power PAD with minimized sensor stream, and minimized training effort. / 2023-06-01

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

This study aimed to compare the validity of a local positioning system (LPS) during outdoor and indoor conditions for team sports. The impact of different filtering techniques was also investigated. Five male team sport athletes (age: 27 ± 2 years; maximum oxygen uptake: 48.4 ± 5.1 mL/min/kg) performed 10 trials on a team sport-specific circuit on an artificial turf and in a sports hall. During the circuit, athletes wore two devices of a recent 20-Hz LPS. From the reported raw and differently filtered velocity data, distances covered during different walking, jogging, and sprinting sections within the circuit were computed for which the circuit was equipped with double-light timing gates as criterion measures. The validity was determined by comparing the known and measured distances via the relative typical error of estimate (TEE). The LPS validity for measuring distances covered was good to moderate during both environments (TEE: 0.9–7.1%), whereby the outdoor validity (TEE: 0.9–6.4%) was superior than indoor validity (TEE: 1.2–7.1%). During both environments, validity outcomes of an unknown manufacturer filter were superior (TEE: 0.9–6.2%) compared to those of a standard Butterworth filter (TEE: 0.9–6.4%) and to unprocessed raw data (TEE: 1.0–7.1%). Our findings show that the evaluated LPS can be considered as a good to moderately valid tracking technology to assess running-based movement patterns in team sports during outdoor and indoor conditions. However, outdoor was superior to indoor validity, and also impacted by the applied filtering technique. Our outcomes should be considered for practical purposes like match and training analyses in team sport environments.

Bakgrund: Internationell forskning pekar nästan samstämmigt på ett systematiskt problem med att aktieanalytiker regelbundet misslyckas med att publicera korrekta riktkurser. Aktieanalytikerna tenderar att övervärdera aktier, och deras prognoser stämmer sällan. I litteraturen har det funnits ett antal variabler som samvarierar med den bristande träffsäkerheten. Ett ökat intresse av att handla svenska aktier gör problemet mer påtagligt på den svenska aktiemarknaden. Brister i aktieanalytikernas träffsäkerhet är ett relativt outforskat område på svenska börsnoterade bolag, varför det blir relevant att undersöka. Kunskapsbidraget kan nyttjas av privata och institutionella investerare som har ett intresse av svenska aktier. Syfte: Syftet med studien är att undersöka och analysera träffsäkerheten hos aktieanalytiker när det gäller riktkurser för svenska börsnoterade bolag. Studien syftar också till att undersöka hur olika variabler så som storlek, tillväxt, betavärde, kapitalstruktur, branschtillhörighet och implicit avkastning relaterar till prognosfelet på riktkurserna. Metod: Genom en kvantitativ metod baserad på en deduktiv ansats testas samband mellan aktieanalytikers träffsäkerhet och ett antal variabler som tidigare har påvisats i litteraturen. Paneldata från en sekundärkälla bygger upp studiens empiriska material och potentiella samband mäts med regressionsanalyser för tre olika tidshorisonter. Den beroende variabeln är det absoluta prognosfelet på publicerade riktkurser, och de förklarande variablerna är storlek, tillväxt, betavärde, kapitalstruktur, branschtillhörighet och absolut implicit avkastning. Vidare analyseras eventuell överoptimism hos aktieanalytikerna med hjälp av det relativa prognosfelet och t-tester. Resultat: Studien finner att aktieanalytiker systematiskt misslyckas med att korrekt prognostisera riktkurser på svenska börsnoterade bolag, och det genomsnittliga prognosfelet är 28% efter tre månader, 31% efter sex månader och 36% efter tolv månader. Vidare finner studien att träffsäkerheten är bättre för stora bolag, bolag med låg tillväxt och vid låg absolut implicit avkastning. Resultaten pekar på en systematisk överoptimism då aktiekurserna övervärderas i en mycket hög utsträckning. / Background: International research almost unanimously points to a systematic issue with equity analysts regularly failing to publish accurate target prices. Stock analysts tend to overvalue stocks, and their forecasts are rarely correct. The literature has identified correlations between the lack of accuracy and several variables. Increased interest in Swedish stocks intensifies the problem on the Swedish stock market. The lack of accuracy is a relatively unexplored area on Swedish listed companies, which makes it relevant to study. The knowledge contribution can further benefit private and institutional investors with an interest in Swedish stocks. Purpose: The purpose of the study is to examine and analyze the accuracy of equity analysts in predicting target prices for Swedish listed companies. The study also aims to examine how different variables such as size, growth, beta value, capital structure, industry, and implied return correlates to forecast error of target prices. Methodology: By employing a quantitative method based on a deductive approach, relationships between equity analysts' accuracy and a set of variables previously identified in the literature are tested. Panel data from a secondary source constitute the empirical material of the study, and potential correlations are measured using regression analysis for three different time horizons. The dependent variable is the absolute forecast error of published target prices, and the explanatory variables are size, growth, beta value, capital structure, industry, and absolute implied return. Furthermore, potential over-optimism among equity analysts is analyzed using the relative forecast error and t-tests. Results: The study finds that equity analysts consistently fail to accurately forecast target prices for Swedish listed companies, with an average forecast error of 28% after three months, 31% after six months, and 36% after twelve months. Furthermore, the study finds that accuracy is higher for large companies, companies with low growth, and when the absolute implied return is low. The results indicate a systematic over-optimism, as stock prices are significantly overvalued.

This paper explores the effect of color on working memory performance. Interest in Baker-Miller pink surged with the finding by Schauss (1979) that it reduced aggression in aggravated detainees. However, research behind Baker-Miller pink has been influenced by biases and methodological errors. Its effects are likely overstated. Red and blue have also been studied for their effects on creativity, approach-avoidance conflict, detail-orientation, and most importantly, stress. Further research has been conducted on the effects of relaxation on cognition, with the conclusion that increased relaxation leads to improvements in working memory performance. This paper tests the effect of color on working memory performance. Accuracy on the n-back was compared across 4 colors: pink, red, blue, and black. No significant differences in accuracy were found in any of the color conditions, though the main effect of load was significant. Future research can increase the sample size and utilize a cognitive task that is inherently stressful.

Despite the increased use of criminal profiling by law enforcement agencies, few studies examine factors impacting profile accuracy, and only one has evaluated profiler training. The present study examined the efficacy of profiler training on profile accuracy for sexual homicide offenders. Participants (N = 198) were randomly assigned to the training or control condition. Participants in the training condition learned inductive and deductive profiling techniques for sexual homicide offenders, whereas control participants learned about sexual violence prevention. Results indicated that participants’ self-reported use of combined profiling methods produced more accurate profiles. Differences between gender and training groups were also present. These findings suggest brief psychology-based training can be used to teach police officers and detectives to more accurately identify and apprehend criminals.

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