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Intelligent speech interfaces have been developing vastly to support the growing demands for convenient control and interaction with wearable/earable and portable devices. To avoid privacy leakage during speech interactions and strengthen the resistance to ambient noise, silent speech interfaces have been widely explored to enable people's interaction with mobile/wearable devices without audible sounds. However, most existing silent speech solutions require either restricted background illuminations or hand involvement to hold device or perform gestures. In this study, we propose a novel earphone-based, hand-free silent speech interaction approach, named EarCommand. Our technique discovers the relationship between the deformation of the ear canal and the movements of the articulator and takes advantage of this link to recognize different silent speech commands. Our system can achieve a WER (word error rate) of 10.02% for word-level recognition and 12.33% for sentence-level recognition, when tested in human subjects with 32 word-level commands and 25 sentence-level commands, which indicates the effectiveness of inferring silent speech commands. Moreover, EarCommand shows high reliability and robustness in a variety of configuration settings and environmental conditions. It is anticipated that EarCommand can serve as an efficient, intelligent speech interface for hand-free operation, which could significantly improve the quality and convenience of interactions.

Recognition of facial expressions has been widely explored to represent people's emotional states. Existing facial expression recognition systems primarily rely on external cameras which make it less accessible and efficient in many real-life scenarios to monitor an individual's facial expression in a convenient and unobtrusive manner. To this end, we propose PPGface, a ubiquitous, easy-to-use, user-friendly facial expression recognition platform that leverages earable devices with built-in PPG sensor. PPGface understands the facial expressions through the dynamic PPG patterns resulting from facial muscle movements. With the aid of the accelerometer sensor, PPGface can detect and recognize the user's seven universal facial expressions and relevant body posture unobtrusively. We conducted an user study (N=20) using multimodal ResNet to evaluate the performance of PPGface, and showed that PPGface can detect different facial expressions with 93.5 accuracy and 0.93 fl-score. In addition, to explore the robustness and usability of our proposed platform, we conducted several comprehensive experiments under real-world settings. Overall results of this work validate a great potential to be employed in future commodity earable devices.

Accurate recognition of facial expressions and emotional gestures is promising to understand the audience's feedback and engagement on the entertainment content. Existing methods are primarily based on various cameras or wearable sensors, which either raise privacy concerns or demand extra devices. To this aim, we propose a novel ubiquitous sensing system based on the commodity microphone array --- SonicFace, which provides an accessible, unobtrusive, contact-free, and privacy-preserving solution to monitor the user's emotional expressions continuously without playing hearable sound. SonicFace utilizes a pair of speaker and microphone array to recognize various fine-grained facial expressions and emotional hand gestures by emitted ultrasound and received echoes. Based on a set of experimental evaluations, the accuracy of recognizing 6 common facial expressions and 4 emotional gestures can reach around 80%. Besides, the extensive system evaluations with distinct configurations and an extended real-life case study have demonstrated the robustness and generalizability of the proposed SonicFace system.

We propose SonicASL, a real-time gesture recognition system that can recognize sign language gestures on the fly, leveraging front-facing microphones and speakers added to commodity earphones worn by someone facing the person making the gestures. In a user study (N=8), we evaluate the recognition performance of various sign language gestures at both the word and sentence levels. Given 42 frequently used individual words and 30 meaningful sentences, SonicASL can achieve an accuracy of 93.8% and 90.6% for word-level and sentence-level recognition, respectively. The proposed system is tested in two real-world scenarios: indoor (apartment, office, and corridor) and outdoor (sidewalk) environments with pedestrians walking nearby. The results show that our system can provide users with an effective gesture recognition tool with high reliability against environmental factors such as ambient noises and nearby pedestrians.

Very high applied pressure induces superconductivity with the transition temperature (Tc) exceeding 19 K in elemental yttrium, but relatively little is known about the nature of that superconductivity. From point-contact spectroscopy (PCS) measurements in a diamond anvil cell (DAC), strong enhancement in the differential conductance is revealed near the zero-biased voltage owing to Andreev reflection, a hallmark of SC phase. Analysis of PCS spectra based on the extended Blonder-Tinkham-Klapwijk (BTK) model indicates two SC gaps at 48.6 GPa, where the large gap ΔL is 3.63 meV and the small gap ΔS is 0.46 meV. When scaled against the reduced temperature, both small and large SC gaps collapse on a single curve that follows the prediction from the BCS theory. The SC gap-to-Tc ratio is 8.2 for the larger gap and the initial slope of the upper critical field is -1.9 T/K, indicating that Y belongs to a family of the strongly coupled BCS superconductors. The successful application of PCS to Y in DAC environments demonstrates its utility for future research on other pressure-induced high-Tc superconductors.

With the rapid growth of wearable computing and increasing demand for mobile authentication scenarios, voiceprint-based authentication has become one of the prevalent technologies and has already presented tremendous potentials to the public. However, it is vulnerable to voice spoofing attacks (e.g., replay attacks and synthetic voice attacks). To address this threat, we propose a new biometric authentication approach, named EarPrint, which aims to extend voiceprint and build a hidden and secure user authentication scheme on earphones. EarPrint builds on the speaking-induced body sound transmission from the throat to the ear canal, i.e., different users will have different body sound conduction patterns on both sides of ears. As the first exploratory study, extensive experiments on 23 subjects show the EarPrint is robust against ambient noises and body motions. EarPrint achieves an Equal Error Rate (EER) of 3.64% with 75 seconds enrollment data. We also evaluate the resilience of EarPrint against replay attacks. A major contribution of EarPrint is that it leverages two-level uniqueness, including the body sound conduction from the throat to the ear canal and the body asymmetry between the left and the right ears, taking advantage of earphones' paring form-factor. Compared with other mobile and wearable biometric modalities, EarPrint is a low-cost, accurate, and secure authentication solution for earphone users.

Very high applied pressure induces superconductivity with the transition temperature ($T_c$) exceeding 19 K in elemental yttrium, but relatively little is known about the nature of that superconductivity. From point-contact spectroscopy (PCS) measurements in a diamond anvil cell (DAC), a strong enhancement in the differential conductance is revealed near the zero-biased voltage owing to Andreev reflection, a hallmark of the superconducting (SC) phase. Analysis of the PCS spectra based on the extended Blonder-Tinkham-Klapwijk (BTK) model indicates two SC gaps at 48.6 GPa, where the large gap $\Delta_L$ is 3.63 meV and the small gap $\Delta_S$ is 0.46 meV. When scaled against a reduced temperature, both small and large SC gaps collapse on a single curve that follows the prediction from BCS theory. The SC gap-to-$T_c$ ratio is 8.2 for the larger gap, and the initial slope of the upper critical field is -1.9 T/K, indicating that Y belongs to a family of strongly coupled BCS superconductors. The successful application of PCS to Y in DAC environments demonstrates its utility for future research on other pressure-induced high-$T_c$ superconductors., Comment: 4 figures

Touch-stroke dynamics is an emerging behavioral biometrics justified feasible for mobile identity management. A touch-stroke dynamics authentication system is composed of a hand-engineered feature extractor and a classifier separately. In this letter, we propose a stacking-based deep learning network that performs feature extraction and classification, collectively dubbed Kernel Deep Regression Network (KDRN). The KDRN is built on multiple kernel ridge regressions (KRR) hierarchically, where each is trained analytically and independently. In principal, KDRN does not mean to learn directly from the raw touch-stroke data like other deep learning models, but it relearns from the pre-extracted features to yield a richer and a relatively more discriminative feature set. Subsequent to that, the authentication is carried out by KRR. Overall, KDRN achieves an equal error rate of 0.013% for intrasession authentication, 0.023% for intersession authentication, and 0.121% for interweek authentication on the Touchlaytics dataset.

Continuous authentication (CA) with touch stroke dynamics is an emerging problem for mobile identity management. In this paper, we focus on one of the essential problems in CA namely one-class classification problem. We propose a novel analytic probabilistic one-class classifier coined One-Class Random MaxOut Probabilistic Network (OC-RMPNet). The OC-RMPNet is a single hidden layer network that is tailored to capture individual users' touch-stroke profiles. The input-hidden layer of the network is meant to project the input vector onto the high dimensional random maxout feature space and the hidden-output layer acts as an OC probabilistic predictor that trained by means of least-square principle, hence require no iterative learning. We also put forward a feature sequential fusion mechanism for accuracy improvement. We scrutinize and compare the proposed methods with existing works on touchanalytics and HMOG datasets. The empirical results reveal that the OC-RMPNet prevails over its predecessor in touch-stroke authentication tasks on mobile phones.

Combined superficial temporal artery-middle cerebral artery anastomosis and encephalo-duro-arterio-galeo-synangiosis (EDAGS) were retrospectively compared with indirect bypass, EDAGS with or without inversion, in 134 hemispheres of 96 adult patients with non-hemorrhagic moyamoya disease (MMD) in terms of angiographic findings, perioperative complications, and clinical outcome. Angiographic revascularization seemed to be better in the combined bypass group compared with the EDAGS group (p = 0.045), but perioperative complication tended to be slightly more common in the combined bypass group. No statistical differences were found in clinical outcome. EDAGS is a very reliable alternative to combined bypass in adult MMD. However, randomized clinical trials are needed to assess the long-term efficacy of any bypass surgery in adult patients with MMD.

Even though cervical ossification of the posterior longitudinal ligament (OPLL) has several unique clinical features compared with spondylotic myelopathy or cervical disc disease, there have been few reports about factors affecting prognosis after decompression using corpectomy. To clarify the prognostic factors for cervical OPLL, the authors analyzed the clinical and radiologic parameters of 47 patients retrospectively.The patients were classified into a good-prognosis group and a poor-prognosis group according to the changes of Nurick grade after operation. Age at operation, gender, preoperative Nurick grade, duration of symptoms, snake-eye appearance, occupying ratio, type of OPLL, Pavlov ratio, and double-layer sign did not affect the prognosis significantly.Multiple logistic regression analysis revealed that diabetes mellitus (DM) was the only statistically significant factor predicting poor prognosis for the patients with cervical OPLL who underwent corpectomy and fusion.Surgeons do not have to be discouraged from performing anterior decompression for the patients with cervical OPLL on the basis of age, severity of disease (preoperative Nurick grade or occupying ratio), irreversible changes in gray matter of the spinal cord (snake-eye appearance), or duration of symptoms. We should direct our attention to DM as a potent risk factor for cervical OPLL and try to clarify the mechanism by which DM possibly affected the functional recovery of the patients.