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Transition metal dichalcogenide (TMD) moiré superlattices have emerged as a significant area of study in condensed matter physics. Thanks to their superior optical properties, tunable electronic band structure, strong Coulomb interactions, and quenched electron kinetic energy, they offer exciting avenues to explore correlated quantum phenomena, topological properties, and light–matter interactions. In recent years, scanning tunneling microscopy (STM) has made significant impacts on the study of these fields by enabling intrinsic surface visualization and spectroscopic measurements with unprecedented atomic scale detail. Here, we spotlight the key findings and innovative developments in imaging and characterization of TMD heterostructures via STM, from its initial implementation on the in situ grown sample to the latest photocurrent tunneling microscopy. The evolution in sample design, progressing from a conductive to an insulating substrate, has not only expanded our control over TMD moiré superlattices but also promoted an understanding of their structures and strongly correlated properties, such as the structural reconstruction and formation of generalized two-dimensional Wigner crystal states. In addition to highlighting recent advancements, we outline upcoming challenges, suggest the direction of future research, and advocate for the versatile use of STM to further comprehend and manipulate the quantum dynamics in TMD moiré superlattices. 

This paper presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user-interfaces, AR/VR applications, affective computing, accessibility, etc. While conventional vision-based solutions break down under poor lighting, occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions, while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form-factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response (CFR) of the inner ear, ultimately resulting in tracking of the facial motion. A transformer based machine learning (ML) model is designed to exploit spectral and temporal relationships in the ultrasonic CFR data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer based ML model runs on smartphone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form-factors. 

Solid-state control of the thermal conductivity of materials is of exceptional interest for novel devices such as thermal diodes and switches. Here, we demonstrate the ability tocontinuouslytune the thermal conductivity of nanoscale films of La_0.5Sr_0.5CoO_3-δ(LSCO) by a factor of over 5, via a room-temperature electrolyte-gate-induced non-volatile topotactic phase transformation from perovskite (withδ≈ 0.1) to an oxygen-vacancy-ordered brownmillerite phase (withδ= 0.5), accompanied by a metal-insulator transition. Combining time-domain thermoreflectance and electronic transport measurements, model analyses based on molecular dynamics and Boltzmann transport equation, and structural characterization by X-ray diffraction, we uncover and deconvolve the effects of these transitions on heat carriers, including electrons and lattice vibrations. The wide-range continuous tunability of LSCO thermal conductivity enabled by low-voltage (below 4 V) room-temperature electrolyte gating opens the door to non-volatile dynamic control of thermal transport in perovskite-based functional materials, for thermal regulation and management in device applications.

 

This paper presents ssLOTR (self-supervised learning on the rings), a system that shows the feasibility of designing self-supervised learning based techniques for 3D finger motion tracking using a custom-designed wearable inertial measurement unit (IMU) sensor with a minimal overhead of labeled training data. Ubiquitous finger motion tracking enables a number of applications in augmented and virtual reality, sign language recognition, rehabilitation healthcare, sports analytics, etc. However, unlike vision, there are no large-scale training datasets for developing robust machine learning (ML) models on wearable devices. ssLOTR designs ML models based on data augmentation and self-supervised learning to first extract efficient representations from raw IMU data without the need for any training labels. The extracted representations are further trained with small-scale labeled training data. In comparison to fully supervised learning, we show that only 15% of labeled training data is sufficient with self-supervised learning to achieve similar accuracy. Our sensor device is designed using a two-layer printed circuit board (PCB) to minimize the footprint and uses a combination of Polylactic acid (PLA) and Thermoplastic polyurethane (TPU) as housing materials for sturdiness and flexibility. It incorporates a system-on-chip (SoC) microcontroller with integrated WiFi/Bluetooth Low Energy (BLE) modules for real-time wireless communication, portability, and ubiquity. In contrast to gloves, our device is worn like rings on fingers, and therefore, does not impede dexterous finger motion. Extensive evaluation with 12 users depicts a 3D joint angle tracking accuracy of 9.07° (joint position accuracy of 6.55mm) with robustness to natural variation in sensor positions, wrist motion, etc, with low overhead in latency and power consumption on embedded platforms.