Birk MacLeod (lilacsort9)

state-of-the-art prediction methods. In particular, case studies confirmed that GFPred is able to retrieve more actual drug-disease associations in the top k part of the prediction results. It is helpful for biologists to discover real associations by wet-lab experiments.The human visual system can recognize object categories accurately and efficiently and is robust to complex textures and noises. To mimic the analogy-detail dual-pathway human visual cognitive mechanism revealed in recent cognitive science studies, in this article, we propose a novel convolutional neural network (CNN) architecture named analogy-detail networks (ADNets) for accurate object recognition. ADNets disentangle the visual information and process them separately using two pathways the analogy pathway extracts coarse and global features representing the gist (i.e., shape and topology) of the object, while the detail pathway extracts fine and local features representing the details (i.e., texture and edges) for determining object categories. We modularize the architecture and encapsulate the two pathways into the analogy-detail block as the CNN building block to construct ADNets. For implementation, we propose a general principle that transmutes typical CNN structures into the ADNet architecture and applies the transmutation on representative baseline CNNs. Extensive experiments on CIFAR10, CIFAR100, street view house numbers, and ImageNet data sets demonstrate that ADNets significantly reduce the test error rates of the baseline CNNs by up to 5.76% and outperform other state-of-the-art architectures. Comprehensive analysis and visualizations further demonstrate that ADNets are interpretable and have a better shape-texture tradeoff for recognizing the objects with complex textures.This paper presents a reconfigurable, dual-output, regulating rectifier featuring pulse width modulation (PWM) and dual-mode pulse frequency modulation (PFM) control schemes for single-stage ac-to-dc conversion to provide two independently regulated supply voltages (each in 1.5-3 V) from an input ac voltage. The dual-mode PFM controllers feature event-driven regulation as well as frequency division. The former incorporates stable, fast, digital feedback loops to adaptively adjust the driving frequency of four power transistors, MP1∼4, based on the desired output power level to perform voltage regulation and deliver fast, transient, load currents. The latter sets the driving frequency of MP1∼4 to a user-defined fraction (1/1 ∼ 1/32) of the input frequency (1-10 MHz). The PWM controllers incorporate stable, analog, feedback loops to accurately adjust the conduction duration of MP1∼4 for voltage regulation and can be combined with PFM frequency division for an extended operation dynamic range. Fabricated in 0.18 μm 1P/6M CMOS, the regulating rectifier features power conversion efficiency (PCE) of >83.8% at 2 and 5 MHz, with the first output channel delivering ∼1 mW from VDD of 1.5 V and the second output channel delivering variable power from VDDH of 2.5 V to a load in the range of 0.1 to 1 kΩ. Peak PCE values of 90.75% (2 MHz, 100 Ω) and 90.7% (5 MHz, 200 Ω) are also measured. The regulating rectifier is suitable for the emerging modality of capacitive wireless power transfer to biomedical implants.This paper presents a wearable active concentric electrode for concurrent EEG monitoring and Body-Coupled Communication (BCC) data transmission. A three-layer concentric electrode eliminates the usage of wires. A common mode averaging unit (CMAU) is proposed to cancel not only the continuous common-mode interference (CMI) but also the instantaneous CMI of up to 51Vpp. The localized potential matching technique removes the ground electrode. An open-loop programmable gain amplifier (OPPGA) with the pseudo-resistor-based RC-divider block is presented to save the silicon area. The presented work is the first reported so far to achieve the concurrent EEG signal recording and BCC-based