The advent of single-cell transcriptomics has revolutionized our ability to analyze cellular heterogeneity and dynamics at a fine resolution, yet covering the vast array of potential perturbations remains challenging due to biological variability. To address this, we propose scCADE, a novel computational approach utilizing contrastive learning and an attention mechanism to decouple gene expression signatures and predict cellular responses to perturbations. scCADE excels in predicting responses in cells to perturbations observed in other cells but not yet seen in the target cells. Through rigorous ablation studies and validation across three datasets involving drug and gene editing perturbations, scCADE consistently outperformed existing methods, underscoring its efficacy and potential to advance genomics and personalized medicine by accurately forecasting responses to novel perturbations.

Privacy concerns over the security of personal information have grown in tandem with the spread of smartwatches. However, effective methods for protecting private data on smartwatches are very limited. Personal identity number (PIN) input is the only privacy protection method on off-the-shelf smartwatches, which requires tedious user effort. This is ineffective at securing information such as notifications and attention-grabbing alerts, which may leak personal data to passersby and adversaries, causing embarrassment or revealing sensitive communications. In this work, we propose a novel privacy protection system, TouchTone, that verifies users and secure personal data in a convenient and low-effort manner. Our system employs a challenge-response process to passively capture finger biometrics from an unobtrusive touch gesture using only microphones, speakers, and accelerometer sensors already built in smartwatches. To address smartwatch incompatibility with traditional high-frequency sensing techniques, we develop non-intrusive low-frequency challenge signals and cross-domain sensing techniques (i.e., measuring acoustic signals in the vibration domain) to capture robust and effective features specific to user fingers. A low-cost profile matching-based classifier is designed to enable stand-alone privacy protection on smartwatches. We conduct extensive experiments with 54 participants using varied hardware, environments, noise levels, user motions, and other impact factors, achieving around 97% true positive rate and 2% false positive rate in recognizing participants' identities for privacy protection.

The two-factor authentication (2FA) has become pervasive as the mobile devices become prevalent. Existing 2FA solutions usually require some form of user involvement, which could severely affect user experience and bring extra burdens to users. In this work, we propose a secure 2FA that utilizes the individual acoustic fingerprint of the speaker/microphone on enrolled device as the second proof. The main idea behind our system is to use both magnitude and phase fingerprints derived from the frequency response of the enrolled device by emitting acoustic beep signals alternately from both enrolled and login devices and receiving their direct arrivals for 2FA. Given the input microphone samplings, our system designs an arrival time detection scheme to accurately identify the beginning point of the beep signal from the received signal. To achieve a robust authentication, we develop a new distance mitigation scheme to eliminate the impact of transmission distances from the sound propagation model for extracting stable fingerprint in both magnitude and phase domain. Our device authentication component then calculates a weighted correlation value between the device profile and fingerprints extracted from run-time measurements to conduct the device authentication for 2FA. Moreover, to thwart the possible co-located attacks, our proximity detection component further makes the enrolled phone to generate an active random vibration signal by its built-in motor, and then matches the signal received by the microphone of login device with the signal received by the accelerometer of enrolled phone to verify the proximity of two devices. Our experimental results show that our proposed system is accurate and robust to various attacks across different scenarios and device models.

The indoor location awareness has drawn increasing attention for smart speakers as they become essential to provide function-location services. Existing indoor localization solutions either require add-on equipment or could only achieve room-level accuracy, which could not provide a function-location service for smart speakers. In this work, we propose a location identification system utilizing 3-D acoustic images, which are derived from the smart speaker by emitting a beep signal and sensing echoes created by objects in the surrounding environment with its microphone array, as the proof to identify some pre-defined indoor locations. Given the recorded acoustic samplings captured by the microphone array, our image construction component constructs a virtual imaging hemisphere and steers the array towards each grid of the hemisphere to generate a 3-D acoustic image of the surrounding environment. Moreover, we design a transfer-learning based model to derive effective features from the constructed images, and propose a data augmentation scheme for generating synthesized training images. To achieve a more accurate location identification, we further design a distance estimation scheme to identify the distances between the smart speaker and some major surrounding objects by utilizing the constructed 3-D acoustic image, and then adopt such distance information for location identification. Our experimental results show that our proposed system is accurate and robust for location identification under various real world scenarios.

The user authentication has drawn increasingly attention as the smart speaker becomes more prevalent. For example, smart speakers that can verify who is sending voice commands can mitigate various types of attacks such as replay attack or impersonation attack. Existing user authentication solutions either cannot be applicable to smart speakers directly or require certain additional user-device interaction or pre-installed infrastructure, which may severely affect the user experience and create extra burdens to users. In this work, we propose a user authentication system EchoImage utilizing acoustic images, which are derived from the smart speaker by emitting beep signals and sensing echoes from the user's body with its microphone array, as the proof for user authentication. Given the acoustic samplings of the reflected beep signal, our system designs a distance estimation component by applying a correlation based technique on the beamformed signal to estimate the distance between the user and microphone array. Our image construction component then constructs a virtual imaging plane using the estimated distance and steers the array towards each grid of the plane to generate an acoustic image of the user. Moreover, we propose a transfer learning-based method to derive efficient features from the constructed images, and employ SVM classifiers for accurate user authentication. Our extensive experiments demonstrate that our system is robust and accurate across various scenarios.

The two-factor authentication (2FA) has become pervasive as the mobile devices become prevalent. Existing 2FA solutions usually require some form of user involvement, which could severely affect user experience and bring extra burdens to users. In this work, we propose a secure 2FA that utilizes the individual acoustic fingerprint of the speaker/microphone on enrolled device as the second proof. The main idea behind our system is to use both magnitude and phase fingerprints derived from the frequency response of the enrolled device by emitting acoustic beep signals alternately from both enrolled and login devices and receiving their direct arrivals for 2FA. Given the input microphone samplings, our system designs an arrival time detection scheme to accurately identify the beginning point of the beep signal from the received signal. To achieve a robust authentication, we develop a new distance mitigation scheme to eliminate the impact of transmission distances from the sound propagation model for extracting stable fingerprint in both magnitude and phase domain. Our device authentication component then calculates a weighted correlation value between the device profile and fingerprints extracted from run-time measurements to conduct the device authentication for 2FA. Our experimental results show that our proposed system is accurate and robust to both random impersonation and Man-in-the-middle (MiM) attack across different scenarios and device models.