The Conception of faith in the Christian religion of the New Testament

There Is a unique unity in the Biblical conception of faith. Of course, there are radical differences of time, place, emphasis, and purpose which must give faith a different cast. Basically, however, this unity Is to be found in the idea of revelation and response. God reveals Himself. He takes the Initiative, moving toward men. He did this in the Old Testament in the days of the Law and the Covenant. There was response to that revelation. At Its best, It involved unconditional surrender to Him, and the conviction that He had purpose for the world which would some day be consummated. Men built their lives on that conviction and that faith. With the coming of Christ, however, the action of God was clearly seen. In great Divine Events of Birth, Life, Death, Resurrection, and a promised Parousia God revealed Himself in Christ. To that revelation, the response of faith was demanded. It was a response that involved mental acceptance of certain facts, but it never ended there. To end at that point was fatal, as James indicates. It was a mental attitude that led to a life, a life beginning with an act of unconditional, whole souled surrender to Christ. That act established a new relationship, "union with Christ", which was evident In Christian living. That union brought certain qualities of life and of character which only God could produce. It established a right relationship with God, it brought forgiveness of sin, justification before God, peace with Him. Ultimately, God will complete the Divine action In the world when His purpose is consummated in Parousla. In the kerygmatic preaching of the early Church all this may be found in various ways from various men--Paul, Stephen, Peter, and others. The various components of faith found emphases which appear at times to be contradictory. But there is a basic unity at the heart of these divergent emphases which is neither arbitrarily assumed, nor produced by preconceived prejudice. The more one observes the thought of the early Church, the more the conviction dawns that the unity is there.Thus the Scriptures call us to faith, "The time is fulfilled, the Kingdom is at hand, repent and believe the Gospel" is an unavoidable appeal to men today. Believe God, Believe Christ, give Him your life in complete abandonment—and LIVE

Exercise redox biochemistry:conceptual, methodological and technical recommendations

Exercise redox biochemistry is of considerable interest owing to its translational value in health and disease. However, unaddressed conceptual, methodological and technical issues complicate attempts to unravel how exercise alters redox homeostasis in health and disease. Conceptual issues relate to misunderstandings that arise when the chemical heterogeneity of redox biology is disregarded which often complicate attempts to use redox-active compounds and assess redox signalling. Further, that oxidised macromolecule adduct levels reflect formation and repair is seldom considered. Methodological and technical issues relate to the use of out-dated assays and/or inappropriate sample preparation techniques that confound biochemical redox analysis. After considering each of the aforementioned issues, we outline how each issue can be resolved and provide a unifying set of recommendations. We specifically recommend that investigators: consider chemical heterogeneity, use redox-active compounds judiciously, abandon flawed assays, carefully prepare samples and assay buffers, consider repair/metabolism, use multiple biomarkers to assess oxidative damage and redox signalling

Task-embedded control networks for few-shot imitation learning

Much like humans, robots should have the ability to leverage knowledge from previously learned tasks in order to learn new tasks quickly in new and unfamiliar environments. Despite this, most robot learning approaches have focused on learning a single task, from scratch, with a limited notion of generalisation, and no way of leveraging the knowledge to learn other tasks more efficiently. One possible solution is meta-learning, but many of the related approaches are limited in their ability to scale to a large number of tasks and to learn further tasks without forgetting previously learned ones. With this in mind, we introduce Task-Embedded Control Networks, which employ ideas from metric learning in order to create a task embedding that can be used by a robot to learn new tasks from one or more demonstrations. In the area of visually-guided manipulation, we present simulation results in which we surpass the performance of a state-of-the-art method when using only visual information from each demonstration. Additionally, we demonstrate that our approach can also be used in conjunction with domain randomisation to train our few-shot learning ability in simulation and then deploy in the real world without any additional training. Once deployed, the robot can learn new tasks from a single real-world demonstration

Sim-to-real reinforcement learning for deformable object manipulation

We have seen much recent progress in rigid object manipulation, but in- teraction with deformable objects has notably lagged behind. Due to the large con- figuration space of deformable objects, solutions using traditional modelling ap- proaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their ap- plicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. To- date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed fur- ther, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating de- formable objects (specifically cloth). We evaluate our approach on three tasks — folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects

Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. To-date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed further, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating deformable objects (specifically cloth). We evaluate our approach on three tasks --- folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects.Comment: Published at the Conference on Robot Learning (CoRL) 201