Acquiring a new language requires individuals to simultaneously and gradually learn linguistic attributes on multiple levels. Here, we investigated how this learning process changes the neural encoding of natural speech by assessing the encoding of the linguistic feature hierarchy in second-language listeners. Electroencephalography (EEG) signals were recorded from native Mandarin speakers with varied English proficiency and from native English speakers while they listened to audio-stories in English. We measured the temporal response functions (TRFs) for acoustic, phonemic, phonotactic, and semantic features in individual participants and found a main effect of proficiency on linguistic encoding. This effect of second-language proficiency was particularly prominent on the neural encoding of phonemes, showing stronger encoding of "new" phonemic contrasts (i.e., English contrasts that do not exist in Mandarin) with increasing proficiency. Overall, we found that the nonnative listeners with higher proficiency levels had a linguistic feature representation more similar to that of native listeners, which enabled the accurate decoding of language proficiency. This result advances our understanding of the cortical processing of linguistic information in second-language learners and provides an objective measure of language proficiency.

Despite the global mental health crisis, access to screenings, professionals, and treatments remains high. In collaboration with licensed psychotherapists, we propose a C onversational AI T herapist with psychotherapeutic I nterventions (CaiTI), a platform that leverages large language models (LLMs) and smart devices to enable better mental health self-care. CaiTI can screen the day-to-day functioning using natural and psychotherapeutic conversations. CaiTI leverages reinforcement learning to provide personalized conversation flow. CaiTI can accurately understand and interpret user responses. When the user needs further attention during the conversation, CaiTI can provide conversational psychotherapeutic interventions, including cognitive behavioral therapy and motivational interviewing. Leveraging the datasets prepared by the licensed psychotherapists, we experiment and microbenchmark various LLMs’ performance in tasks along CaiTI’s conversation flow and discuss their strengths and weaknesses. With the psychotherapists, we implement CaiTI and conduct 14-day and 24-week studies. The study results, validated by therapists, demonstrate that CaiTI can converse with users naturally, accurately understand and interpret user responses, and provide psychotherapeutic interventions appropriately and effectively. We showcase the potential of CaiTI LLMs to assist the mental therapy diagnosis and treatment and improve day-to-day functioning screening and precautionary psychotherapeutic intervention systems.

Kangaroo Mother Care (KMC), involving chest-to-chest skin contact between an infant and caregiver, is proven to be an effective intervention for preterm and full-term infants. Accurate monitoring of KMC duration and infant's vital signs during KMC is clinically important. Existing monitoring methods, however, rely on manual efforts and require rigid sensors or wires/electrodes on the infant's body. We propose Joey, a fabric-based approach to continuously monitor KMC duration and two vital signs essential to an infant's well-being: heart rate and respiration rate. Joey is a soft fabric necklace worn by the caregiver. It leverages the transmission of electrocardiogram (ECG) signals across individuals during skin-to-skin contact. With a minimalist fabric sensor structure, Joey measures KMC duration via the presence of mixed ECG signals. It then isolates the infant's ECG from this mixture with a proposed signal extraction algorithm and employs a diffusion-based denoising model to mitigate motion artifacts, enabling reliable inference of infant's vital signs. We fabricate Joey prototypes with off-the-shelf hardware and evaluate its performance with user studies. Results demonstrate that Joey achieves an average F1 score of 96% for KMC duration measurement, and clinically-acceptable accuracy in infant's vital sign estimation with a mean absolute error of 2.3 beats per minute and 2.9 breaths per minute in estimating heart rate and respiration rate. Clinical interviews further confirm the usability of Joey's sensing fabric for infant skin. A demonstration video of Joey is available at: mobilex.cs.columbia.edu/joey

There is a lack of automated tools that utilize artificial intelligence to monitor mental health. The mental status examination (MSE) is an important tool used by mental health providers for assessing mental health. Currently, MSEs are conducted by licensed professionals, which is a barrier for patients in low income and remote areas. We propose an AI-based personal online mental status examination (aiMSE), the first interactive MSE platform. Users can use aiMSE to self-administer MSEs at home through a web browser, using only a camera and microphone. aiMSE uses multimodal image, speech, and natural language processing algorithms to detect signs of abnormalities in mental functioning and recommend them for further examination by a mental health specialist. We conducted a 14-person study, which supports the feasibility of detecting a wide range of signs commonly found in patients with changes in mental or cognitive capacity.

Demand for the grid state estimation with partial power network observation is growing rapidly with the increasing amount of distributed energy resources (DER) (such as electric vehicles (EVs)) with incomplete measured information connected to the grid. The grid services that can be provided by these DER, such as bidirectional EV chargers, have the potential to affect the resilience and efficiency of the grid. We propose a constrained optimization solver based on AC power flows to recover incomplete information from the grid [1]. We evaluate the performance of the proposed solver using the IEEE 9/30/57/118-bus systems and show that it excels the state-of-the-art approaches ( pandapower and MATPOWER) in recovering the grid state from partial observations with up to six orders of magnitude smaller mean absolute error. The proposed solver has better scalability to deal with larger systems and with systems with more unknown measurements. Further reactive power constraints are added to the solver following the specifications in IEEE 1547 to explore the effects of adding grid services to the steady-state microgrid. We demonstrate and discuss novel case studies on these four IEEE bus systems. And the reactions of the IEEE bus systems when following the grid services' constraints are introduced. We illustrate a sensitivity analysis that reflects how the system's physical characteristics would impact the implementation of grid services. The proposed solver with grid services constraints can serve as a tool to help evaluate distributed grid services and fine-tune grid services to optimal results.

Mapping 3D airflow fields is important for many HVAC, industrial, medical, and home applications. However, current approaches are expensive and time-consuming. We present Anemoi, a sub-$100 drone-based system for autonomously mapping 3D airflow fields in indoor environments. Anemoi leverages the effects of airflow on motor control signals to estimate the magnitude and direction of wind at any given point in space. We introduce an exploration algorithm for selecting optimal waypoints that minimize overall airflow estimation uncertainty. We demonstrate through microbenchmarks and real deployments that Anemoi is able to estimate wind speed and direction with errors up to 0.41 m/s and 25.1° lower than the existing state of the art and map 3D airflow fields with an average RMS error of 0.73 m/s.