Abstract: The present disclosure relates to compounds of formula (I) and pharmaceutical compositions and their use in reducing Widely Interspaced Zinc Finger Motifs (WIZ) expression levels, or inducing fetal hemoglobin (HbF) expression, and in the treatment of inherited blood disorders (e.g., hemoglobinopathies, e.g., beta-hemoglobinopathies), such as sickle cell disease and beta-thalassemia.

Abstract: A method of delivering a substance to a target location of a subject using an insertion apparatus having a base structure with at least one lumen extending along a length thereof, a cladding layer circumferentially surrounding the base structure and an optically-transmissive coating circumferentially surrounding an exterior of the base layer or an exterior of the cladding layer. The method includes inserting the insertion apparatus into the subject to access the target location, transmitting to tissue adjacent the insertion apparatus, a first optical radiation using the optically-transmissive coating or the cladding layer, receiving, from the tissue, at a processor, data characterizing a second optical radiation via the other of the optically-transmissive coating or the cladding layer based on the first optical radiation and determining whether a tip of the base structure is at the target location, and, if so, releasing a substance into or near the target location.

Abstract: Improved inspection techniques are described herein for checking for the presence of and identifying surface defects on a honeycomb body. The improved inspection utilizes measurement of travel of an outer surface of the honeycomb body to collect images of the outer surface. The images are combined into a composite image showing the outer surface of the honeycomb body. The composite image is analyzed to identify surface defects.

Abstract: A low-order model (LOM) of biological neural networks and its mathematical equivalents including the clusterer interpreter probabilistic associative memory (CIPAM) are disclosed. They are artificial neural networks (ANNs) organized as networks of processing units (PUs), Each PU comprising artificial neuronal encoders, synapses, spiking/nonspiking neurons, and a scheme for maximal generalization. If the weights in the artificial synapses in a PU have been learned (and then fixed) or can be adjusted by the unsupervised accumulation rule and the unsupervised covariance rule (or supervised covariance rule), the PU is called unsupervised (or supervised) PU. The disclosed ANNs, with these Hebbian-type learning rules, can learn large numbers of large input vectors with temporally/spatially hierarchical causes with ease and recognize such causes with maximal generalization despite corruption, distortion and occlusion.

Abstract: A low-order model (LOM) of biological neural networks and its mathematical equivalents including the clusterer interpreter probabilistic associative memory (CIPAM) are disclosed. They are artificial neural networks (ANNs) organized as networks of processing units (PUs), each PU comprising artificial neuronal encoders, synapses, spiking/nonspiking neurons, and a scheme for maximal generalization. If the weights in the artificial synapses in a PU have been learned (and then fixed) or can be adjusted by the unsupervised accumulation rule and the unsupervised covariance rule (or supervised covariance rule), the PU is called unsupervised (or supervised) PU. The disclosed ANNs, with these Hebbian-type learning rules, can learn large numbers of large input vectors with temporally/spatially hierarchical causes with ease and recognize such causes with maximal generalization despite corruption, distortion and occlusion.

Abstract: A cortex-like learning machine, called a probabilistic associative memory (PAM), is disclosed for recognizing spatial and temporal patterns. A PAM is usually a multilayer or recurrent network of processing units (PUs). Each PU expands subvectors of a feature vector input to the PU into orthogonal vectors, and generates a probability distribution of the label of said feature vector, using expansion correlation matrices, which can be adjusted in supervised or unsupervised learning by a Hebbian-type rule. The PU also converts the probability distribution into a ternary vector to be included in feature subvectors that are input to PUs in the same or other layers. A masking matrix in each PU eliminates effect of corrupted components in query feature subvectors and enables maximal generalization by said PU and thereby that by the PAM. PAMs with proper learning can recognize rotated, translated and scaled patterns and are functional models of the cortex.

Abstract: A cortex-like learning machine, called a probabilistic associative memory (PAM), is disclosed for recognizing spatial and temporal patterns. A PAM is usually a multilayer or recurrent network of processing units (PUs). Each PU expands subvectors of a feature subvector input to the PU into orthogonal vectors, and generates a probability distribution of the label of said feature subvector, using expansion correlation matrices, which are adjusted in supervised or unsupervised learning by a Hebb rule. The PU also converts the probability distribution into a ternary vector to be included in feature subvectors that are input to PUs in the same or other layers. A masking matrix in each PU eliminates effect of corrupted components in query feature subvectors and enables maximal generalization by said PU and thereby that by the PAM. PAMs with proper learning can recognize rotated, translated and scaled patterns and are functional models of the cortex.

Abstract: According to one embodiment of the present invention, a device may receive one of multiple frequencies at a clock input of the device; determine which of the multiple frequencies is received; and generate at least one internal clock frequency for operation of the device using the received frequency. For example, such a device may be embedded within a system having a receiver to receive a satellite signal spectrum and to tune a signal channel and baseband circuitry coupled to the receiver to process digital data corresponding to the signal channel, where the baseband circuitry operates at the frequency provided to the device.

Abstract: A game machine includes a bed, a marble launching device fitted to a front end of the bed, a game plate supported on a rear section of the bed, and a bottom plate positioned under the bed; the game plate slopes up rearwards, and has several through holes thereon for allowing marbles to pass through; the marble launching device is used for launching marbles onto the game plate through a front section of the bed; the bottom plate has a marble receiving area, which projects out beyond a front end of the bed; the bottom plate has several lengthways extending trenches thereon therefore marbles will roll to the marble receiving area along the trenches when they fall through the through holes of the game plate.

Abstract: A game machine includes a bed, a marble launching device fitted to a front end of the bed, a game plate supported on a rear section of the bed, and a bottom plate positioned under the bed; the game plate slopes up rearwards, and has several through holes thereon for allowing marbles to pass through; the marble launching device is used for launching marbles onto the game plate through a front section of the bed; the bottom plate has a marble receiving area, which projects out beyond a front end of the bed; the bottom plate has several lengthways extending trenches thereon therefore marbles will roll to the marble receiving area along the trenches when they fall through the through holes of the game plate.

Abstract: An indoor putting game device includes a main body, a plurality of goal boards with different patterns and ball holes of different numbers optionally selected to be mounted on the main body, and a flexible ball course member wound on a shaft in the main body possible to be pulled out from a front end of the main body. The main body has the shaft for winding the ball course member while not used or releasing it out for use. The goal board can be adjusted in its inclination by optionally inserting position pins in a pair of pinholes in two sidewalls of the main body. So the device can be used in playing putting game in various modes, boosting users' interest in playing and achieving game and exercise effect.

Abstract: A game table includes a panel, several pushing rods, pushing elements, pressing rods, and wheels; the panel has several ball receiving holes thereon; the pushing rods are positioned under the panel, next to edges of the receiving holes; the pushing elements are supported on the pushing rods in an adjustable manner, and face the receiving holes; the pressing rods stick upwards through the panel, and each have an elastic element around it, and a rack secured thereon; the wheels each are connected to one of the pushing rods, and have a gear secured thereon, which engages a respective one of the racks; the elastic elements will move the pressing rods upwards to their original position immediately after the players stop pressing the pressing rods, which will cause horizontal displacement of the pushing rods for the pushing elements to prop balls out of the receiving holes when depressed.

Abstract: An indoor putting game device includes a main body, a plurality of goal boards with different patterns and ball holes of different numbers optionally selected to be mounted on the main body, and a flexible ball course member wound on a shaft in the main body possible to be pulled out from a front end of the main body. The main body has the shaft for winding the ball course member while not used or releasing it out for use. The goal board can be adjusted in its inclination by optionally inserting position pins in a pair of pinholes in two sidewalls of the main body. So the device can be used in playing putting game in various modes, boosting users' interest in playing and achieving game and exercise effect.

Abstract: A carpet includes a mat body of a certain thickness and a plurality of pseudo-hole members. The mat body has its surface provided with a ballgame design such as a golf course, a billiard table, a bowling course, a cricket ground, a croquet ground, a football ground, etc. The pseudo-hole members are immovably or movably positioned on the ballgame design, and the surface of the mat body and the pseudo-hole members have height difference so that the carpet can be used as a real carpet to be treaded upon, and also used as a mini-ballgame ground or course to boost its function for a family.

Abstract: A method of training neural systems and estimating regression coefficients of regression models with respect to an error criterion is disclosed. If the error criterion is a risk-averting error criterion, the invented method performs the training/estimation by starting with a small value of the risk-sensitivity index of the risk-averting error criterion and gradually increasing it to ensure numerical feasibility. If the error criterion is a risk-neutral error criterion such as a standard sum-of-squares error criterion, the invented method performs the training/estimation first with respect to a risk-averting error criterion associated with the risk-neutral error criterion. If the result is not satisfactory for the risk-neutral error criterion, further training/estimation is performed either by continuing risk-averting training/estimation with decreasing values of the associated risk-averting error criterion or by training/estimation with respect to the given risk-neutral error criterion or by both.

Abstract: A method of training neural systems and estimating regression coefficients of regression models with respect to an error criterion is disclosed. If the error criterion is a risk-averting error cri- terion, the invented method performs the training/estimation by starting with a small value of the risk-sensitivity index of the risk-averting error criterion and gradually increasing it to ensure numerical feasibility. If the error criterion is a risk-neutral error criterion such as a standard sum- of-squares error criterion, the invented method performs the training/estimation first with respect to a risk-averting error criterion associated with the risk-neutral error criterion. If the result is not satisfactory for the risk-neutral error criterion, further training/estimation is performed either by continuing risk-averting training/estimation with decreasing values of the associated risk-averting error criterion or by training/estimation with respect to the given risk-neutral error criterion or by both.

Abstract: A neural system is disclosed for processing an exogenous input process to produce a good outward output process with respect to a performance criterion, even if the range of one or both of these processes is necessarily large and/or keeps necessarily expanding during the operation of the neural system. The disclosed neural system comprises a recurrent neural network (RNN) and at least one range extender or reducer, each of which is a dynamic transformer. A range reducer transforms dynamically at least one component of the exogenous input process into inputs to at least one input neuron of said RNN. A range extender transforms dynamically outputs of at least one output neuron of said RNN into at least one component of the outward output process. There are many types of range extender and reducer, which have different degrees of effectiveness and computational costs.

Abstract: To enhance the safety and security of the operation of a railway network, a railway operation monitoring and diagnosing system is disclosed that monitors and diagnoses the entire railway network as an integrated system. The railway operation monitoring and diagnosing system comprises a railway operation predictor and a diagnosing means. The railway operation predictor generates anticipated values of selected railway operation state (ROS) variables. ROS variables may discrete or continuous. If there are continuous ROS variables selected, the railway operation predictor also determines the safety intervals of these continuous ROS variables. The diagnosing means examines the measured values of the selected ROS variables versus their anticipated values and/or safety intervals to detect and diagnose their discrepancies. A heuristics, statistics, fuzzy logic, artificial intelligence, neural network, or/and expert system is included in the diagnosing means for diagnosing the records of such discrepancies.

Abstract: A robust neural system for robust processing is disclosed for averting unacceptable or disastrous processing performances. This robust neural system either comprises a neural network or comprises a neural network and at least one range transformer. At least one adjustable weight of the robust neural system is a nonlinear weight of the neural work determined in a nonadaptive training of the robust neural system with respect to a nonadaptive risk-sensitive training criterion.If all the adjustable weights of the robust neural system are nonadaptively adjustable, all these weights are held fixed during the robust neural system's operation. If said neural network is recursive, and the nonadaptive training data used to construct said nonadaptive risk-sensitive training criterion contain data for each of a number of typical values of an environmental parameter, the robust neural system is not only robust but also adaptive to the environmental parameter.

Abstract: A recursive neurofilter comprising a recursive neural network (NN) is disclosed for processing an information process to estimate a signal process with respect to an estimation error criterion. The information process either consists of a measurement process, or if the signal and measurement processes are time-variant, consists of the measurement process as well as a time variance process, that describes the time-variant properties of the signal and measurement processes. The recursive neurofilter is synthesized from exemplary realizations of the signal and information processes. No assumptions such as Gaussian distribution, linear dynamics, additive noise, and Markov property are required. The synthesis is performed essentially through training recursive NNs. The training criterion is constructed to reflect the mentioned estimation error criterion with the exemplary realizations.