This article presents a 14-bit fully dynamic sensor interface that consists of a switched-capacitor (SC) ΔΣ modulator and a dynamic bandgap reference (BGR). The BGR is implemented by summing the proportional to absolute temperature (PTAT) and complementary to absolute temperature (CTAT) outputs of two PNP-based capacitive DACs. At the sampling rate, the DAC capacitors are pre-charged to the supply and then discharged for a fixed period via PNPs, thus biasing them and simultaneously sampling their base-emitter voltages. By using the modulator's first integrator to sum the DAC outputs, a dynamic BGR can be realized, which does not need additional reference buffers or decoupling capacitors. To make the system fully dynamic, the modulator itself is based on capacitively biased (CB) floating inverter amplifiers (FIAs). Implemented in a standard 130-nm CMOS process, the sensor interface occupies an area of 0.2 mm². It achieves an SNDR of > 84.5 dB over a scalable bandwidth (BW) ranging from 98 Hz to 5.9 kHz while consuming 1.7-50.8 μW. Furthermore, by employing a time-domain temperature-compensation scheme, it achieves a batch-trimmed gain error of ± 0.26% from -40°C to 125 °C.

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End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e.g., instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art.@en