Learning perception and planning with deep active inference

Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiments are limited to predefined, often discrete, state spaces. In this paper we use recent advances in deep learning to learn the state space and approximate the necessary probability distributions to engage in active inference

Reducing MOSFET 1/f Noise and Power Consumption by 'switched biasing'

"Switched Biasing" is proposed as a new circuit technique that exploits an intriguing physical effect: cycling a MOS transistor between strong inversion and accumulation reduces its intrinsic 1/f noise. The technique is implemented in a 0.8µm CMOS sawtooth oscillator by periodically off-switching of the bias currents during time intervals that they are not contributing to the circuit operation. Measurements show a reduction of the 1/f noise induced phase noise by more than 8 dB, while the power consumption is reduced by more than 30% as well

Aanpassing voer vergemakkelijkt de toepassing van pluimveemest: Eerste resultaten van pilotstudies naar effect van biologische fytase positief

In Nederland zijn er nu ruim 1 miljoen biologische legkippen die jaarlijks bijna 300 miljoen eieren produceren maar ook 25.000 ton mest. De meeste legkippenhouders hebben zelf weinig grond en moeten de mest afvoeren. Het project ‘Kippenmest en Kringloop’ streeft er naar deze mest zo goed mogelijk in te zetten en te benutten binnen de biologische landbouw. Om de mest aantrekkelijker te maken voor bijvoorbeeld de akkerbouw is het belangrijk dat de mestkwaliteit en daarbij met name de verhouding tussen stikstof en fosfaat verbetert. In 2009 en 2010 zijn pilots uitgevoerd om het effect van aanpassingen in het kippenvoer op de mestkwaliteit te onderzoeken

Bayesian policy selection using active inference

Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a well-known technique for learning such policies. However, current RL algorithms often have to deal with reward shaping, have difficulties generalizing to other environments and are most often sample inefficient. In this paper, we explore active inference and the free energy principle, a normative theory from neuroscience that explains how self-organizing biological systems operate by maintaining a model of the world and casting action selection as an inference problem. We apply this concept to a typical problem known to the RL community, the mountain car problem, and show how active inference encompasses both RL and learning from demonstrations.Comment: ICLR 2019 Workshop on Structure & priors in reinforcement learnin

Intrinsic 1/f device noise reduction and its effect on phase noise in CMOS ring oscillators

This paper gives experimental proof of an intriguing physical effect: periodic on-off switching of MOS transistors in a CMOS ring oscillator reduces their intrinsic 1/f noise and hence the oscillator's close-in phase noise. More specifically, it is shown that the 1/f3 phase noise is dependent on the gate-source voltage of the MOS transistors in the off state. Measurement results, corrected for waveform-dependent upconversion and effective bias, show an 8-dB-lower 1/f3 phase noise than expected. It will be shown that this can be attributed to the intrinsic 1/f noise reduction effect due to periodic on-off switchin

Optimal Positions of Twists in Global On-Chip Differential Interconnects

Abstract—Crosstalk limits the achievable data rate of global on-chip interconnects on large CMOS ICs. This is especially the case, if low-swing signaling is used to reduce power consumption. Differential interconnects provide a solution for most crosstalk and noise sources, but not for neighbor-to-neighbor crosstalk in a data bus. This neighbor-to-neighbor crosstalk can be reduced with twists in the differential interconnect-pairs. To reduce via resistance and metal layer use, we use as few twists as possible by placing only one twist in every even interconnect-pair and only two twists in every odd interconnect-pair. Analysis shows that there are optimal positions for the twists, which depend on the termination impedances of the interconnects. Theory and measurements on a 10 mm long bus in 0.13 μm CMOS show that only one twist at 50% of the even interconnect-pairs, two twists at 30% and 70% of the odd interconnect-pairs and both a low-ohmic source and a low-ohmic load impedance are very effective in mitigating the crosstalk

Interpreting and Correcting Medical Image Classification with PIP-Net

Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we evaluate its accuracy and interpretability for fracture detection and skin cancer diagnosis. We find that PIP-Net's decision making process is in line with medical classification standards, while only provided with image-level class labels. Because of PIP-Net's unsupervised pretraining of prototypes, data quality problems such as undesired text in an X-ray or labelling errors can be easily identified. Additionally, we are the first to show that humans can manually correct the reasoning of PIP-Net by directly disabling undesired prototypes. We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging