2,945 research outputs found

    A Model of Low-lying States in Strongly Interacting Electroweak Symmetry-Breaking Sector

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    It is proposed that, in a strongly-interacting electroweak sector, besides the Goldstone bosons, the coexistence of a scalar state (HH) and vector resonances such as A1A_1 [IG(JP)=1−(1+I^G(J^P)=1^-(1^+)], VV [1+(1−)1^+(1^-)] and ωH\omega_H^{} [0−(1−)0^-(1^-)] is required by the proper Regge behavior of the forward scattering amplitudes. This is a consequence of the following well-motivated assumptions: (a). Adler-Weisberger-type sum rules and the superconvergence relations for scattering amplitudes hold in this strongly interacting sector; (b). the sum rules at t=0t=0 are saturated by a minimal set of low-lying states with appropriate quantum numbers. It therefore suggests that a complete description should include all these resonances. These states may lead to distinctive experimental signatures at future colliders.Comment: revised version, to appear in Modern Physics Letters A; file also available via anonymous ftp at ftp://ucdhep.ucdavis.edu/han/sews/lowlying.p

    IDENTIFYING MECHANISMS OF INSULIN PRODUCTION AND SECRETION IN SMALL AND LARGE RAT ISLETS

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    The existence of islet subpopulations according to size difference has been described since 1869 when Dr. Paul Langerhans first discovered the islets in the pancreas. Unfortunately, little is known about the functional differences between islet subpopulations until recently. Small islets have been shown to secret more insulin than large islets per volume (islet equivalent; IE) and led to better transplantation outcome both in rodents and in humans. Insulin is produced and released from the beta cells in islets through a cascading pathway from insulin gene transcription to proinsulin biosynthesis to insulin secretion. The central hypothesis of this dissertation is that small and large islets have different characteristics in insulin production and secretion that lead to different transplantation outcomes. More than ten thousands small (diameter less or equal 100µm) and large (diameter above or equal 200µm) islets from healthy rats were investigated. First, the same percentage of beta cells was identified in small and large islets, but small islets had higher density both in vitro and in situ. Next, a new regression model was established to better estimate the islet volume by cell number based on size (diameter), since an overestimation was seen when using conventional IE measurement to normalize islet volume. By applying this new normalization method, a superior glucose-stimulated proinsulin biosynthesis was identified in large islets. However, when normalized to cell number, insulin secretion was not different between small and large islets, unlike the results in literature when normalized to IE. While small and large islets showed no difference in total protein content per cell, large islets showed higher protein levels of prosinulin, NeuroD/Beta2 and MafA with a lower PDX-1 level under basal conditions suggesting that the different characteristics between small and large islets in the insulin production pathway may not correspond to measured insulin secretion. All the findings will not only elucidate new intricacies concerning islet biology research, but also will have significant implications to current islet transplantation research to optimize the success for curing type 1 diabetes

    Why We Should Report the Details in Subjective Evaluation of TTS More Rigorously

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    This paper emphasizes the importance of reporting experiment details in subjective evaluations and demonstrates how such details can significantly impact evaluation results in the field of speech synthesis. Through an analysis of 80 papers presented at INTERSPEECH 2022, we find a lack of thorough reporting on critical details such as evaluator recruitment and filtering, instructions and payments, and the geographic and linguistic backgrounds of evaluators. To illustrate the effect of these details on evaluation outcomes, we conducted mean opinion score (MOS) tests on three well-known TTS systems under different evaluation settings and we obtain at least three distinct rankings of TTS models. We urge the community to report experiment details in subjective evaluations to improve the reliability and interpretability of experimental results.Comment: Interspeech 2023 camera-ready versio

    Patient Deception in Health Care: Physical Therapy Education, Beliefs, and Attitudes

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    A good professional-patient relationship is important to clinical practice, which may be compromised by deception. Deception research in physical therapy is scant. The current study investigated how the topic of patient deception is addressed in Doctor of Physical Therapy (DPT) educational curriculum, explore DPT students’ beliefs about deception and attitudes toward patient deception, and examine the effects of a pedagogical intervention on DPT students’ beliefs about deception and attitudes toward patient deception. The first objective was pursued by a descriptive survey sent to 217 DPT programs in the US. The second and third objectives were achieved by one-group pretest-posttest design provided to 17 DPT students before and after an educational workshop. Most DPT programs minimally include the topic of patient deception within their curriculum. DPT students held several inaccurate beliefs about the indicators of deception and negative attitudes toward patients who lied. After the educational intervention, students’ inaccurate beliefs were corrected and negative attitudes were reduced. Patient deception seems to be an under-addressed topic in current physical therapy education. An education workshop improved students’ beliefs about deception and attitudes toward to patient deception. Implications of deception research and theory in the applied practice of physical therapy are discussed

    When Social Influence Meets Item Inference

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    Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page

    The Physiological and Psychological Benefits of CrossFit Training – A Pilot Study

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    CrossFit has been one of the fastest growing training methods in the fitness industry since its inception in 2000. CrossFit combines classic strength and conditioning along with gymnastics movements, Olympic weightlifting, and other functional movements into a constantly varied, high intensity workout. The success of CrossFit and what seems to be exponential growth of their over 10,000 affiliated gyms is undeniable. This popularity might be stem from two main factors: the physiological changes of training and the psychological benefits of a community emphasized, social atmosphere. However, there is very limited research evidence supporting the potential benefits of CrossFit . This study was conducted to investigate the physiological and psychological benefits of CrossFit training in a healthy adult population undergoing their first exposure to the training method. Sixteen participants were recruited from a local CrossFit affiliate in San Angelo, Texas. Participants completed a series of self-report psychological questionnaires including the Motives for Physical Activity Measures (MPAM), Mental Health Inventory 38 (MHI-38), and the Group Environment Questionnaire (GEQ). Following these questionnaires, physical metrics including: heart rate, blood pressure, height, body weight, body composition via Dual-energy X-ray Absorptiometry (DXA), along with performance measures including 1-RM back squat, 1-RM bench press, vertical jump test, and a Wingate Anaerobic Power Test were conducted. The CrossFit program was conducted for 8 weeks by certified CrossFit coaches at the local affiliate gym. After the 8-week training, the participants were reassessed using the same measures. Over the course of the study, 6 participants completed the program (2 males, 4 females, 36.2 ± 10.8 years of age, 73.6 ± 7.4 kg, 167.6 ± 5.5 cm, and 31.0 ± 9.2% body fat). Despite the large attrition rate, there were statistically significant increase of lean mass (1.44 ± 1.26 kg; p= 0.039), decrease of mean fat (1.67 ± 1.17 kg ; p= 0.017) and changes in interest subset of motivation from MPAM motivational test (p \u3c 0.05). In conclusion, this pilot study suggests that CrossFit training might be beneficial for improving body composition and concurrently changes certain motivational factors to continue engaging in the fitness activity. Further studies with a longer intervention period and a larger sample size are needed to support these findings

    Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

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    The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models
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