Combination of lactulose plus rifaximin is more effective than lactulose alone in the treatment of overt HE.

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The ALFED model accurately predicted outcome in patients with ALF, which may be useful in clinical decision-making.

Constructive Solid Geometry (CSG) is a geometric modeling technique that defines complex shapes by recursively applying boolean operations on primitives such as spheres and cylinders. We present CSGNET, a deep network architecture that takes as input a 2D or 3D shape and outputs a CSG program that models it. Parsing shapes into CSG programs is desirable as it yields a compact and interpretable generative model. However, the task is challenging since the space of primitives and their combinations can be prohibitively large. CSGNET uses a convolutional encoder and recurrent decoder based on deep networks to map shapes to modeling instructions in a feed-forward manner and is significantly faster than bottom-up approaches. We investigate two architectures for this task -a vanilla encoder (CNN) -decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack. The stack augmentation improves the reconstruction quality of the generated shape and learning efficiency. Our approach is also more effective as a shape primitive detector compared to a state-of-the-art object detector. Finally, we demonstrate CSGNET can be trained on novel datasets without program annotations through policy gradient techniques.

We propose an explicit rate indication scheme for congestion avoidance in ATM networks. In this scheme, the network switches monitor their load on each link, determining a load factor, the available capacity, and the number of currently active virtual channels. This information is used to advise the sources about the rates at which they should transmit. The algorithm is designed to achieve e ciency, fairness, controlled queueing delays, and fast transient response. The algorithm is also robust to measurement errors caused due to variation in ABR demand and capacity. We present performance analysis of the scheme using both analytical arguments and simulation results. The scheme is being implemented by several ATM switch manufacturers. 1We begin by brie y examining the ABR service. In section 3, we describe basic concepts such as the switch model and design goals. Section 4 describes the algorithm. Section 5 presents representative simulations to show that the scheme works under stressful conditions; we also present analytical arguments in appendix A. Finally, our conclusions are presented in section 6.2 The ABR Control Mechanism ATM networks o er ve classes of service: constant bit rate CBR, real-time variable bit rate rt-VBR, non-real time variable bit rate nrt-VBR, available bit rate ABR, and unspeci ed bit rate UBR. Of these, ABR and UBR are designed for data tra c, which has a bursty unpredictable behavior.The UBR service is simple in the sense that users negotiate only their peak cell rates PCR when setting up the connection. If many sources send tra c at the same time, the total tra c at a switch may exceed the output capacity causing delays, bu er over ows, and loss. The network tries to minimize the delay and loss using intelligent bu er allocation 15 , cell drop 16 and scheduling, but makes no guarantees to the application.The ABR service provides better service for data tra c by periodically advising sources about the rate at which they should be transmitting. The switches monitor their load, compute the available bandwidth and divide it fairly among active o ws. This allows competing sources to get a fair share of the bandwidth and not be starved due to a small set of rogue sources. The feedback from the switches to the sources is sent in Resource Management RM cells which are sent periodically by the sources and turned around by the destinations see gure 1.The RM cells contain the source current cell rate CCR, and several other elds that can be used by the switches to provide feedback to the source. These elds are: Explicit Rate ER, Congestion Indication CI Flag, and No Increase NI Flag. The ER eld indicates the rate that the network can support at the particular instant in time. When starting at the source, the ER eld is usually set to the PCR, and the CI and NI ags are clear. On the path, each switch reduces the ER eld to the maximum rate it can support and sets CI or NI if necessary 12 .The RM cells owing from the source to the destination are called forward RM cells FRMs while those