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Wild giant pandas, an endangered species exclusive to China, are a focus of conservation efforts. The behavior of giant pandas reflects their health conditions and activity capabilities, which play an important role in formulating and implementing conservation measures. Researching and developing efficient behavior recognition methods based on deep learning can significantly advance the study of wild giant panda behavior. This study introduces, for the first time, a transformer-based behavior recognition method termed PandaFormer, which employs time-spatial attention to analyze behavioral temporal patterns and estimate activity spaces. The method integrates advanced techniques such as cross-fusion recurrent time encoding and transformer modules, which handle both temporal dynamics and spatial relationships within panda behavior videos. First, we design cross-fusion recurrent time encoding to represent the occurrence time of behaviors effectively. By leveraging the multimodal processing capability of the transformer, we input time and video tokens into the transformer module to explore the relation between behavior and occurrence time. Second, we introduce relative temporal weights between video frames to enable the model to learn sequential relationships. Finally, considering the fixed position of the camera during recording, we propose a spatial attention mechanism based on the estimation of the panda's activity area. To validate the effectiveness of the model, a video dataset of wild giant pandas, encompassing five typical behaviors, was constructed. The proposed method is evaluated on this video-level annotated dataset. It achieves a Top-1 accuracy of 92.25 % and a mean class precision of 91.19 %, surpassing state-of-the-art behavior recognition algorithms by a large margin. Furthermore, the ablation experiments validate the effectiveness of the proposed temporal and spatial attention mechanisms. In conclusion, the proposed method offers an effective way of studying panda behavior and holds potential for application to other wildlife species. • Time and video are integrated for wild panda behavior recognition. • The relative order of video frames enhances model performance. • An active region attention module for background noise suppression is presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • A hybrid deep learning model is developed to forecast active and reactive power. • Concept of explicit time encoding is proposed as a factor to improve forecasting. • SHapley Additive exPlanations framework is employed to enhance model interpretability. • Power management is formulated based on SHapley Additive exPlanations findings. Nowadays, residential households, including both consumers and emerging prosumers, have exhibited a growing demand for active/reactive power. This demand surge arises from activities such as charging electrical devices, leveraging flexible resources, and integrating renewable energy sources. To meet this escalating demand effectively, operators must ensure the provision of an ample supply of active/reactive power. Achieving this necessitates the identification of influential factors and the generation of precise forecasts for active/reactive power demand. Hence, this work proposes an efficient hybrid deep learning model consisting of long short-term memory and self-Attention (LSTM-Attention). This model incorporates explicit time encoding to forecast one-hour-ahead consumption of active and reactive power using real-time data from residential units. The integration of both models represents a strategic development for model robustness. Leveraging the inherent strengths of both architectures allows for a synergistic compensation that addresses limitations within each, contributing to an overall effective forecasting model. Moreover, the Shapley Additive Explanations (SHAP) framework was employed for model interpretability, and the investigation underscores the pivotal role of incorporating temporal features into active and reactive power forecasting. SHAP findings can be effectively applied in power management strategies to optimally enhance demand response. Finally, to evaluate the effectiveness of the proposed model, a comprehensive array of performance metrics was employed. The results demonstrate a superior forecast accuracy of the proposed model compared to alternative forecasting models. The proposed model achieved the lowest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for active power with a value of 0.0256, 0.0181, and 14.255 %, respectively. The formulated forecasting method can also significantly contribute to the industrial sector by improving the accuracy of active/reactive power forecasting, thereby enhancing model interpretability and identifying the most critical factors. [ABSTRACT FROM AUTHOR]

The prediction of future consumption is vital for building management in general and green buildings in particular. A certified green building should meet an evaluation criterion of resource efficiency throughout its life cycle. This paper presents a hybrid model that combines a profile averaging model and a machine learning model, namely random forest regressor method, for improved far horizon forecasting (10% improvement when the prediction horizon is 1 week). The proposed hybrid model is modular, explainable, and does not entail a large dataset. The feature analysis highlighted the importance of explicit time encoding by feeding the time as an input feature to the forecasting model (importance factor of 35%). [ABSTRACT FROM AUTHOR]

A new technique to mitigate the non-linearity of the ring oscillator is introduced and described for open loop ring oscillator based analog-to-digital conversion. The idea is based on limiting the input signal amplitude to make the ring oscillator work in a more linear range, thus improving the SNDR p e a k. The dynamic range is preserved by means of several parallel digitization channels injected in phase. Passive interpolation between the channels is exploited to ensure a uniform distribution of the output phases. The idea is validated through system and schematic simulations using a 65 nm CMOS technology node, towards high bandwidth applications such as new communication protocols or Internet-of-Things environments. A SNDR of 63 dB is achieved with four digitization parallel channels with an input signal swing of differential 600 mV p p in a bandwidth of 50 MHz. PVT variations and mismatch effects are checked and no degradation in the final resolution is observed. Therefore, the proposal shows a high robustness without requiring any type of calibration. Additionally it is proven that the architecture consumes less than half of the power consumed by the conventional approach, leading to a higher power efficiency. [Display omitted] • Non-linearity compensation in ring oscillators-based analog-to-digital conversion by input signal attenuation without compromising the dynamic range of the data converter. • Several parallel digitization paths with uniform distribution of the ring oscillator phases by means of passive phase interpolation. • Excellent performance achieved compared to equivalent works, achieving a SNDR of 63 dB for an input swing of 600 mVpp in a pseudo-differential configuration over 50 MHz of bandwidth. • Great robustness against PVT variations and mismatch effects with no need of any calibration technique. • Better power efficiency leading to power savings higher than 50%. [ABSTRACT FROM AUTHOR]

• This article reports recent developments in time-to-digital conversion (TDC) techniques. • A comprehensive picture of major trends and advancements of the TDC design is presented. • The architectures, implementations and performance of TDCs including time resolution, conversion range, conversion time, power consumption, and converter nonlinearities are discussed in details. Time-to-digital converters (TDCs) are key components of time-mode circuits and enablers for digital processing of analog signals encoded in time. Since design of time-mode circuits facilitates replacing analog blocks with digital circuitry, TDCs pave the way to hardware-efficient and purely digital architectures in deep-submicron semiconductor technology. The design and implementation of modern TDCs is heterogeneous, shaped in multiple directions, and driven by CMOS process downscaling along with application demands. The substantial research effort is made in more intensive digital implementations, optimization of techniques for high resolution, increasing input range and linearity, reduced conversion time, power consumption and silicon area. The calibration and mismatch-tolerant, as well as anti-PVT-variation and anti-metastability design techniques are of growing importance in order to alleviate imperfections of nanoscale CMOS technologies. The paper surveys recent developments of time-to-digital conversion techniques to give a possibly comprehensive picture of major trends and design advancements. Finally, we highlight TDC design challenges for cutting-edge applications such as All-Digital Phase Locked Loops for high data rate wirelesss communication systems operating in the millimeter-wave band. [ABSTRACT FROM AUTHOR]

Abstract: The parity-violation experiment at Jefferson Lab (Newport News, VA) is designed to determine the contribution of strange/anti-strange quark pairs to the intrinsic properties of the proton. In the forward-angle part of the experiment, the asymmetry in the cross-section was measured for elastic scattering by counting the recoil protons corresponding to the two beam-helicity states. Due to the high accuracy required to measure the few-part-per-million asymmetry, the experiment was based on a custom experimental setup with its own associated electronics and data acquisition (DAQ) system. Highly specialized time-encoding electronics provided time-of-flight spectra for each detector for each helicity state. More conventional electronics, processing only a small fraction of the events, was used for monitoring (mainly FastBus). The time-encoding electronics and the DAQ system have been designed to handle events from the 128 detector pairs at a mean rate of 2MHz per detector pair with low deadtime and with minimal helicity-correlated systematic errors. In this paper, we outline the general architecture and the main features of the electronics and the DAQ system dedicated to forward-angle measurements. [Copyright &y& Elsevier]

Abstract: Time encoding is a mechanism of mapping amplitude information into a time sequence. We show that multichannel time encoding using filter banks and integrate-and-fire neurons provides, under natural conditions, an invertible representation of information, i.e., a sensory stimulus can be recovered from its multidimensional spike train representation loss-free. We describe an algorithm for perfect stimulus recovery and derive conditions that guarantee its convergence. [Copyright &y& Elsevier]

Time encoding is a formal method of mapping amplitude information into a time sequence. We show that under simple conditions, bandlimited stimuli encoded with an integrate-and-fire neuron with an absolute refractory period can be recovered loss-free from the neural spike train at its output. We provide an algorithm for perfect recovery and derive conditions for its convergence. [Copyright &y& Elsevier]