Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer Learning between Different Levels in the Same Game

Improving generalizability has been a hot topic in reinforcement learning. In this report, we experimented transfer learning tasks between two gaming scenario that share the same gaming mechanism and strategy but have different raw pixel image representation. We found that reinitializing first convolutional layer to learn new raw pixel image representation and retraining on all transferred layers to associate image representation to previously trained gaming mechanism and strategy can boost the performance the most.
Real and Virtual Bound States in Lüscher Corrections for CP 3 Magnons

We study classical and quantum finite-size corrections to giant magnons in AdS4 × CP 3 using generalised Lüscher formulae. Lüscher F-terms are organised in powers of the exponential suppression factor (e−∆/2h)m, and we calculate all terms in this series, matching one-loop algebraic curve results from our previous paper [1]. Starting with the second term, the structure of these terms is different to those in AdS5 × S thanks to the appearance of heavy modes in the loop, which can here be interpreted as two-particle bound states in the mirror theory. By contrast, physical bound states can represent dyonic giant magnons, and we also calculate F-terms for these solutions. Lüscher μ-terms, suppressed by e−∆/E , instead give at leading order the classical finite-size correction. For the elementary dyonic giant magnon we recover the correction given by [2]. We then extend this to calculate the next term in 1/h, giving a one-loop prediction. Finally we also calculate F-terms for the various composite giant magnons, RP 3 and ‘big’, again finding agreement to all orders.
This is a repository copy of Wearable Sensing for Solid Biomechanics : A Review

Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in healthcare, sport, wellbeing, and workflow analysis. Conventional laboratorybased biomechanical analysis systems and observation-based tests are only designed to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing upon sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations that can affect the accuracy and robustness of existing methods, and different methods for reducing the impact of these sources of errors are described in this review. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, as well as the future direction of sensing for biomechanics are also discussed.
New Zealand Society of Animal Production online archive

Future farming practices need to be assessed by the dairy industry for any impacts they may have on the detailed composition and processability of milk. These impacts may change the milk price a farmer receives and the milk processability, product range and product quality. The use of sodium fumarate (an organic acid) to reduce methane production from dairy cows has previously been reported. This experiment investigated the effect of intra-ruminal infusions of sodium fumarate on the detailed composition of milk from dairy cows. Fumarate (5% of daily dry matter intake) was infused into 8 lactating cows while 8 cows received infusions of water as a control. Milk samples were collected during a covariate period, then again following a 16-day treatment period, of which cows were housed indoors for the last 9 days. Gross composition of milk and other production parameters have been presented previously. Milk yield, fat and protein were not affected but lactose concentrations were significantly higher in the milk of cows receiving the fumarate. In the current study, none of the individual casein proteins measured showed an effect of treatment. Of the whey proteins examined, concentrations of α-lactalbumin (α-La) were higher (1.07 vs. 0.98 mg/mL; sed 0.038; P < 0.05) and bovine serum albumin (BSA) concentrations were lower (214.6 vs. 251.0 mg/L; sed 15.1; P < 0.05) in the milk of cows receiving fumarate. None of the individual fatty acids or minerals examined showed a treatment effect. The addition of sodium fumarate to the diet of dairy cows in early lactation does not have a major influence on the composition of milk, suggesting that the product could be used with minimal impact on milk composition and product quality at this early stage of lactation.
Non-linear MHD Simulations of Pellet Triggered ELMs

ITER operation relies on the achievement of the H-mode confinement regime, which is expected to lead to the quasi-periodic triggering of ELMs (Edge Localized Modes). The energy fluxes associated with natural ELMs will produce excessive erosion and/or damage on the plasma facing component. Controlled triggering of ELMs by the injection of small pellets at frequencies exceeding those of natural ELMs is one of the foreseen schemes to control ELMs in ITER. Although the technique has been demonstrated to decrease ELM size successfully in ASDEX Upgrade [1], JET [2], and DIII-D [3], uncertainties still remain regarding the physics understanding as well as of the consequence of its application, such as localised power loads associated with this technique [4].
Modeling sensory-motor decisions in natural behavior Supporting Information Appendix 1 : Simulation Results

Using a canonical 2D gridworld in reinforcement learning (RL) research, the goals are to empirically prove that modular IRL algorithm can estimate rewards and discount factors correctly, demonstrate its advantages over standard IRL, and show an example of sparse modular IRL. Part of the gridworld is shown in Fig 1. Different module objects are indicated by different colors and shapes. Behavioral data (state-action pair samples) are collected from a modular RL agent. We first show that modular IRL is able to recover module rewards and discount factors correctly. The environment contains six modules each with ten objects. Three of them have positive rewards and the other three have negative rewards. 10 gridworlds are generated with random layouts of objects. The agent navigates each world for 6,000 steps. Non-sparse modular IRL (Eq (9)) is used to estimate r and γ and we calculate the mean estimation and standard error. The results are shown in Table 1, it is evident that modular IRL is highly accurate in recovering the true rewards and discount factors given a large amount of data.
Title Geodetic constraints on frictional properties and earthquake hazard in the Imperial Valley

We analyze a suite of geodetic observations across the Imperial Fault in southern California that span all parts of the earthquake cycle. Coseismic and postseismic surface slips due to the 1979 M 6.6 Imperial Valley earthquake were recorded with trilateration and alignment surveys by Harsh (1982) and Crook et al. (1982), and interseismic deformation is measured using a combination of multiple interferometric synthetic aperture radar (InSAR)-viewing geometries and continuous and survey-mode GPS. In particular, we combine more than 100 survey-mode GPS velocities with InSAR data from Envisat descending tracks 84 and 356 and ascending tracks 77 and 306 (149 total acquisitions), processed using a persistent scatterers method. The result is a dense map of interseismic velocities across the Imperial Fault and surrounding areas that allows us to evaluate the rate of interseismic loading and along-strike variations in surface creep. We compare available geodetic data to models of the earthquake cycle with rateand state-dependent friction and find that a complete record of the earthquake cycle is required to constrain key fault properties including the rate-dependence parameter (a − b) as a function of depth, the extent of shallow creep, and the recurrence interval of large events. We find that the data are inconsistent with a high (>30 mm/yr) slip rate on the Imperial Fault and investigate the possibility that an extension of the San Jacinto-Superstition Hills Fault system through the town of El Centro may accommodate a significant portion of the slip previously attributed to the Imperial Fault. Models including this additional fault are in better agreement with the available observations, suggesting that the long-term slip rate of the Imperial Fault is lower than previously suggested and that there may be a significant unmapped hazard in the western Imperial Valley.
PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks

Federated Learning (FL) enables a large number of users to jointly learn a shared machine learning (ML) model, coordinated by a centralized server, where the data is distributed across multiple devices. This approach enables the server or users to train and learn an ML model using gradient descent, while keeping all the training data on users' devices. We consider training an ML model over a mobile network where user dropout is a common phenomenon. Although federated learning was aimed at reducing data privacy risks, the ML model privacy has not received much attention. In this work, we present PrivFL, a privacy-preserving system for training (predictive) linear and logistic regression models and oblivious predictions in the federated setting, while guaranteeing data and model privacy as well as ensuring robustness to users dropping out in the network. We design two privacy-preserving protocols for training linear and logistic regression models based on an additive homomorphic encryption (HE) scheme and an aggregation protocol. Exploiting the training algorithm of federated learning, at the core of our training protocols is a secure multiparty global gradient computation on alive users' data. We analyze the security of our training protocols against semi-honest adversaries. As long as the aggregation protocol is secure under the aggregation privacy game and the additive HE scheme is semantically secure, PrivFL guarantees the users' data privacy against the server, and the server's regression model privacy against the users. We demonstrate the performance of PrivFL on real-world datasets and show its applicability in the federated learning system.
Writing To Connect: Can Creating A Personal Website Improve Adjustment To Breast Cancer?

Writing to Connect CAN CREATING A PERSONAL WEBSITE IMPROVE ADJUSTMENT TO BREAST CANCER? BY LAUREN HARRIS A PPROXIMATELY one in eight women in the United States will be diagnosed with breast can- cer at some point during her life (Howlader et al., 2012). In 2014 alone, more than 232,000 Ameri- can women were diagnosed with the disease (DeSantis et al., 2014). Diagnosis and treatment of breast cancer, which can include surgery, radiation, chemotherapy, and/or endocrine therapy, can profoundly impact a woman’s physical, psy- chological, and social functioning. Although most women diag- nosed with breast cancer adjust well over time, breast cancer patients do have elevated rates of depression and anxiety disorders compared with the general popu- lation (Mitchell et al., 2011; Fann et al., 2008). Depression in cancer patients is associated with low- er participation in medical care, longer hospital stays, and, perhaps, lower survival (Colleoni et al., 2000; Prieto et al., 2002; Fann et al., 2008; Pinquart & Duberstein, 2010a). Breast cancer patients are UCLA Center for the Study of Women also faced with social concerns, in- cluding managing communication with loved ones about their health and having less energy to engage in valued social activities. Many studies demonstrate that traditional types of therapy, such as cognitive behavioral therapy and supportive-expressive therapy, can improve emotional distress and quality of life in cancer patients (Faller et al., 2013; Naaman et al., 2009). However, some breast cancer patients may be unable to participate in therapy in a tradi- tional, face-to-face setting due to side effects of treatment (for ex- ample, fatigue and pain), intensive medical treatment schedules, and unavailability of therapy services in their communities. Therefore, it is crucial to identify resources that are both effective and accessible to address the difficulties women face following a breast cancer diagnosis. A large body of research demon- strates that strong social ties can improve both psychological and physical health during the cancer experience (Nosarti et al., 2002; Pinquart & Duberstein, 2010b) and that social isolation is associ- ated with poor health outcomes (Widows et al., 2000; Lutgendorf & Sood, 2011). Despite the impor- tance of social support, fostering communication between women with breast cancer and their social network can be challenging. Wom- en may be hesitant to ask others for help, lack the energy to seek support, or feel burdened by hav- ing to repeat the same information over and over. Family and friends may want to offer support, but may not know what to say or do. Personal websites provide a central space for women to share their cancer experience and com- municate their needs, bridging the potential gap in communication between the patient and her sup- port system. Online journaling may help women to create a story of their experience with breast can- cer, express emotions, and boost confidence in their ability to cope with the cancer experience, factors
The Impact of Discretization Method on the Detection of Six Types of Anomalies in Datasets

Anomaly detection is the process of identifying cases, or groups of cases, that are in some way unusual and do not fit the general patterns present in the dataset. Numerous algorithms use discretization of numerical data in their detection processes. This study investigates the effect of the discretization method on the unsupervised detection of each of the six anomaly types acknowledged in a recent typology of data anomalies. To this end, experiments are conducted with various datasets and SECODA, a general-purpose algorithm for unsupervised non-parametric anomaly detection in datasets with numerical and categorical attributes. This algorithm employs discretization of continuous attributes, exponentially increasing weights and discretization cut points, and a pruning heuristic to detect anomalies with an optimal number of iterations. The results demonstrate that standard SECODA can detect all six types, but that different discretization methods favor the discovery of certain anomaly types. The main findings also hold for other detection techniques using discretization.
CLINICAL PSYCHOLOGY RESEARCH

The hallmarks of Attention-Deficit/Hyperactivity Disorder (ADHD) are reflected in the symptom areas of inattention, impulsivity and hyperactivity. ADHD was long regarded as a childhood disease, but various studies have since found that adults are equally affected by the disorder. Differential diagnostic considerations, therefore, as well as the need for regard to potential comorbidity are of great importance for the diagnostic process. As possible examples are mentioned autism spectrum disorder, substance use disorders, schizophrenia, affective disorders, anxiety disorders and personality disorders. These groups of disorder form the subject of discussion in this chapter.
Fermentation of Phelinus baumii MK818502 for polysaccha rides and flavonoids production using defatted silkworm pu pa hydrolysates as a nitrogen source

The hydrolysates of defatted silkworm pupa were prepared from abandoned silkworm pupa to culture Phelinus baumii MK818502. The ratio of raw material dried discard pupa to hydrolysates products was 1: 0.37 and the product nitrogen content was 3.18%. The optimum culture conditions for P. baumii cultivation were obtained, fermentation time was 5 d. defatted silkworm pupa hydrolysates content was 32 g/L, temperature was 26 °C, pH=7 and the revolving speed was 120 rpm. Fermentation experiment confirmed that the substrate consumption of reducing sugar accounted for 75.61% of the total reducing sugar and nitrogen consumption accounted for 35.19% of the total nitrogen content. Active metabolites production experiment showed that extracellular polysaccharides concentration was 7.71 g/L, and the extracellular flavonoid concentration was 0.4 g/L. Compared with other nitrogen sources, the low-cost hydrolysates nitrogen content was 0.044 g/L with great fermentation performance, mycelia biomass reached to 0.36 g/L and produced 0.376 g/L polysaccharides, 0.22 g/L flavonoids.
The Arts in Psychotherapy esearch article rt therapy for PTSD and TBI : A senior active duty military service ember ’ s therapeutic journey

Art therapy is increasingly being accepted as a form of complementary and integrative care for military veterans affected by trauma and injuries in the line of duty. Less is known, however, about the applications of art therapy for co-morbid traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD). vailable online 20 May 2016 In addition, most studies to date have focused on art therapy with veterans (former military service members) rather than with active duty service members; furthermore, there are no studies that have examined the unique context of PTSD in senior military personnel. This case study presents the therapeutic process through art therapy in the case of a senior active duty military service member (with chronic PTSD and TBI), in the context of an integrated model of care that included medical and complementary therapies. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Weak solutions for one-dimensional aggregation equations March 22 , 2013

Existence and uniqueness of global in time measure solution for a one dimensional nonlinear aggregation equation is considered. Such a system can be written as a conservation law with a velocity field computed through a selfconsistant interaction potential. Blow up of regular solutions is now well established for such system. In Carrillo et al. (Duke Math J (2011)) [14], a theory of existence and uniqueness based on the geometric approach of gradient flows on Wasserstein space has been developped. We propose in this work to establish the link between this approach and duality solutions. This latter concept of solutions allows in particular to define a flow associated to the velocity field. Then an existence and uniqueness theory for duality solutions is developped in the spirit of James and Vauchelet (NoDEA (2013)) [21]. However, since duality solutions are only known in one dimension, we restrict our study to the one dimensional case.
CHAPTER 40 Kinematic Predictions in Large Shallow Water Waves

The present paper concerns the description of extreme waves in shallow water, and in particular contrasts the success of two recently developed wave theories (Sobey, 1992 and Baldock and Swan, 1994) to model the maximum water particle velocities arising beneath a highly nonlinear and transient (or unsteady) wave event. To achieve these comparisons exact numerical calculations were undertaken using the time-stepping procedure outlined by Dold and Peregrine (1984). Comparisons between this numerical "data" and the kinematics models confirm the importance of both the non-linearity and the unsteadiness, and further suggest that the nature of the wave-wave interactions has important implications for the accuracy of the kinematics predictions. In particular, the local Fourier series solution (Sobey, 1992) is shown to be unable to model the global wave frequency-difference terms, and therefore tends to over-predict the fluid velocities beneath the still water level. In contrast, the double Fourier series solution (Baldock and Swan, 1994) explicitly incorporates both the nonlinearity and the unsteadiness of the wave, and typically provides a good description of the flow field beneath the still water level. However, this solution is limited in terms of the total number of Fourier components included, and consequently the largest velocities arising close to the water surface are typically under-predicted in the largest wave events. Nevertheless, both kinematics models provide a significant improvement over the existing design solutions.
Deposited in DRO : 08 May 2014 Version of attached le : Other Peer-review status of attached

We analyse modelling techniques for the large-scale structure formed in scalartensor theories of constant Brans-Dicke parameter which match the concordance model background expansion history and produce a chameleon suppression of the gravitational modification in high-density regions. Thereby, we use a mass and environment dependent chameleon spherical collapse model, the Sheth-Tormen halo mass function and linear halo bias, the Navarro-Frenk-White halo density profile, and the halo model. Furthermore, using the spherical collapse model, we extrapolate a chameleon mass-concentration scaling relation from a ΛCDM prescription calibrated to N -body simulations. We also provide constraints on the model parameters to ensure viability on local scales. We test our description of the halo mass function and nonlinear matter power spectrum against the respective observables extracted from large-volume and high-resolution N -body simulations in the limiting case of f(R) gravity, corresponding to a vanishing Brans-Dicke parameter. We find good agreement between the two; the halo model provides a good qualitative description of the shape of the relative enhancement of the f(R) matter power spectrum with respect to ΛCDM caused by the extra attractive gravitational force but fails to recover the correct amplitude. Introducing an effective linear power spectrum in the computation of the two-halo term to account for an underestimation of the chameleon suppression at intermediate scales in our approach, we accurately reproduce the measurements from the N -body simulations. ar X iv :1 31 2. 12 92 v1 [ as tr oph . C O ] 4 D ec 2 01 3
SYNTHESIS USING STFT SPECTRA BASED ON LOW-/ MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS

This paper proposes novel training algorithms for vocoder-free statistical parametric speech synthesis (SPSS) using short-term Fourier transform (STFT) spectra. Recently, text-to-speech synthesis using STFT spectra has been investigated since it can avoid quality degradation caused by the vocoder-based parameterization in conventional SPSS using a vocoder. In conventional SPSS using a vocoder, we previously proposed a training algorithm for integrating generative adversarial network (GAN)-based distribution compensation. To extend the algorithm to vocoder-free SPSS, we propose lowand multi-resolution GAN-based training algorithms for vocoder-free SPSS. In our algorithm that uses the low-resolution GAN, acoustic models are trained to minimize the weighted sum of the mean squared error between natural and generated spectra in the original resolution and adversarial loss to deceive discriminative models in the lower resolution. Since the low-resolution spectra are close to filter banks and their distribution becomes simpler, GAN-based distribution compensation works well. Furthermore, we propose an algorithm using multi-resolution GANs, which uses both the low-resolution GAN and original-resolution GAN. Experimental results demonstrate that 1) the low-resolution GAN works robustly to the setting of its frequency resolution and hyperparameter, and 2) compared the low-, original-, and multi-resolution GANs, the low-resolution GAN works the best to improve synthetic speech quality.