An adaptive model checking test for functional linear model

Numerous studies have been devoted to the estimation and inference problems for functional linear models (FLM). However, few works focus on model checking problem that ensures the reliability of results. Limited tests in this area do not have tractable null distributions or asymptotic analysis under alternatives. Also, the functional predictor is usually assumed to be fully observed, which is impractical. To address these problems, we propose an adaptive model checking test for FLM. It combines regular moment-based and conditional moment-based tests, and achieves model adaptivity via the dimension of a residual-based subspace. The advantages of our test are manifold. First, it has a tractable chi-squared null distribution and higher powers under the alternatives than its components. Second, asymptotic properties under different underlying models are developed, including the unvisited local alternatives. Third, the test statistic is constructed upon finite grid points, which incorporates the discrete nature of collected data. We develop the desirable relationship between sample size and number of grid points to maintain the asymptotic properties. Besides, we provide a data-driven approach to estimate the dimension leading to model adaptivity, which is promising in sufficient dimension reduction. We conduct comprehensive numerical experiments to demonstrate the advantages the test inherits from its two simple components

FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas

International audienceThe past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense-the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications

Interpreting Distributional Reinforcement Learning: A Regularization Perspective

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we attribute the superiority of distributional RL to its regularization effect in terms of the value distribution information regardless of its expectation. Firstly, by leverage of a variant of the gross error model in robust statistics, we decompose the value distribution into its expectation and the remaining distribution part. As such, the extra benefit of distributional RL compared with expectation-based RL is mainly interpreted as the impact of a \textit{risk-sensitive entropy regularization} within the Neural Fitted Z-Iteration framework. Meanwhile, we establish a bridge between the risk-sensitive entropy regularization of distributional RL and the vanilla entropy in maximum entropy RL, focusing specifically on actor-critic algorithms. It reveals that distributional RL induces a corrected reward function and thus promotes a risk-sensitive exploration against the intrinsic uncertainty of the environment. Finally, extensive experiments corroborate the role of the regularization effect of distributional RL and uncover mutual impacts of different entropy regularization. Our research paves a way towards better interpreting the efficacy of distributional RL algorithms, especially through the lens of regularization

Evaluating EYM amplitudes in four dimensions by refined graphic expansion

The recursive expansion of tree level multitrace Einstein-Yang-Mills (EYM) amplitudes induces a refined graphic expansion, by which any tree-level EYM amplitude can be expressed as a summation over all possible refined graphs. Each graph contributes a unique coefficient as well as a proper combination of color-ordered Yang-Mills (YM) amplitudes. This expansion allows one to evaluate EYM amplitudes through YM amplitudes, the latter have much simpler structures in four dimensions than the former. In this paper, we classify the refined graphs for the expansion of EYM amplitudes into N k MHV sectors. Amplitudes in four dimensions, which involve k + 2 negative-helicity particles, at most get non-vanishing contribution from graphs in N k′ (k′ ≤ k) MHV sectors. By the help of this classification, we evaluate the non-vanishing amplitudes with two negative-helicity particles in four dimensions. We establish a correspondence between the refined graphs for single-trace amplitudes with (g−i,g−j) or (h−i,g−j) configuration and the spanning forests of the known Hodges determinant form. Inspired by this correspondence, we further propose a symmetric formula of double-trace amplitudes with (g−i,g−j) configuration. By analyzing the cancellation between refined graphs in four dimensions, we prove that any other tree amplitude with two negative-helicity particles has to vanish

An Overview of Flexible Sensors: Development, Application, and Challenges

The emergence and advancement of flexible electronics have great potential to lead development trends in many fields, such as “smart electronic skin” and wearable electronics. By acting as intermediates to detect a variety of external stimuli or physiological parameters, flexible sensors are regarded as a core component of flexible electronic systems and have been extensively studied. Unlike conventional rigid sensors requiring costly instruments and complicated fabrication processes, flexible sensors can be manufactured by simple procedures with excellent production efficiency, reliable output performance, and superior adaptability to the irregular surface of the surroundings where they are applied. Here, recent studies on flexible sensors for sensing humidity and strain/pressure are outlined, emphasizing their sensory materials, working mechanisms, structures, fabrication methods, and particular applications. Furthermore, a conclusion, including future perspectives and a short overview of the market share in this field, is given for further advancing this field of research

Simulation of Gas Fracturing in Reservoirs Based on a Coupled Thermo-Hydro-Mechanical-Damage Model

Gas fracturing technology for enhancing rock permeability is an area with considerable potential for development. However, the complexity and variability of underground conditions mean that a variety of rock physical parameters can affect the outcome of gas fracturing, with temperature being a critical factor that cannot be overlooked. The presence of a temperature field adds further complexity to the process of gas-induced rock fracturing. To explore the effects of temperature fields on gas fracturing technology, this paper employs numerical simulation software to model the extraction of shale gas under different temperature conditions using gas fracturing techniques. The computer simulations monitor variations in the mechanical characteristics of rocks during the process of gas fracturing. This analysis is performed both prior to and following the implementation of a temperature field. The results demonstrate that gas fracturing technology significantly improves rock permeability; temperature has an impact on the effectiveness of gas fracturing, with appropriately high temperatures capable of enhancing the fracturing effect. The temperature distribution plays a crucial role in influencing the results of gas fracturing. When the temperature is low, the fracturing effect is diminished, resulting in a lower efficiency of shale gas extraction. Conversely, when the temperature is high, the fracturing effect is more pronounced, leading to a higher shale gas production efficiency. Optimal temperatures can enhance the efficacy of gas fracturing and consequently boost the efficiency of shale gas extraction. Changes in the parameters of the rock have a substantial impact on the efficiency of gas extraction, and selecting suitable rock parameters can enhance the recovery rate of shale gas. This paper, through numerical simulation, investigates the influence of temperature on gas fracturing technology, with the aim of contributing to its improved application in engineering practices

Towards position-independent sensing for gesture recognition with Wi-Fi

International audiencePast decades have witnessed the extension of the Wi-Fi signals as a useful tool sensing human activities. One common assumption behind it is that there is a one-to-one mapping between human activities and Wi-Fi received signal patterns. However, this assumption does not hold when the user conducts activities in different locations and orientations. Actually, the received signal patterns of the same activity would become inconsistent when the relative location and orientation of the user with respect to transceivers change, leading to unstable sensing performance. This problem is known as the position-dependent problem, hindering the actual deployment of Wi-Fi-based sensing applications. In this paper, to tackle this fundamental problem, we develop a new position-independent sensing strategy and use gesture recognition as an application example to demonstrate its effectiveness. The key idea is to shift our observation from the traditional transceiver view to the hand-oriented view, and extract features that are irrespective of position-specific factors. Following the strategy, we design a position-independent feature, denoted as Motion Navigation Primitive(MNP). MNP captures the pattern of moving direction changes of the hand, which shares consistent patterns when the user performs the same gesture with different position-specific factors. By analyzing the pattern of MNP, we convert gestures into sequences of strokes (e.g, line, arc and corner) which makes them easy to be recognized. We build a prototype WiFi gesture recognition system, i.e., WiGesture to validate the effectiveness of the proposed strategy. Experiments show that our system can outperform the start-of-arts significantly in different settings. Given its novelty and superiority, we believe the proposed method symbolizes a major step towards gesture recognition and would inspire other solutions to position-independent activity recognition in the future

Advanced synaptic devices and their applications in biomimetic sensory neural system

ABSTRACT: Human nervous system, which is composed of neuron and synapse networks, is capable of processing information in a plastic, data-parallel, fault-tolerant, and energy-efficient approach. Inspired by the ingenious working mechanism of this miraculous biological data processing system, scientists have been devoting great efforts to artificial neural systems based on synaptic devices in recent decades. The continuous development of bioinspired sensors and synaptic devices in recent years have made it possible that artificial sensory neural systems are capable of capturing and processing stimuli information in real time. The progress of biomimetic sensory neural systems could provide new methods for next-generation humanoid robotics, human-machine interfaces, and other frontier applications. Herein, this review summarized the recent progress of synaptic devices and biomimetic sensory neural systems. Additionally, the opportunities and remaining challenges in the further development of biomimetic sensory neural systems were also outlined