Bourns College of Engineering

A magnon is a collective excitation of the spin structure in a magnetic insulator and can transmit spin angular momentum with negligible dissipation. This quantum of a spin wave has always been manipulated through magnetic dipoles (that is, by breaking time-reversal symmetry). Here we report the experimental observation of chiral spin transport in multiferroic BiFeO₃ and its control by reversing the ferroelectric polarization (that is, by breaking spatial inversion symmetry). The ferroelectrically controlled magnons show up to 18% modulation at room temperature. The spin torque that the magnons in BiFeO₃ carry can be used to efficiently switch the magnetization of adjacent magnets, with a spin-torque efficiency comparable to the spin Hall effect in heavy metals. Utilizing such controllable magnon generation and transmission in BiFeO₃, an all-oxide, energy-scalable logic is demonstrated composed of spin-orbit injection, detection and magnetoelectric control. Our observations open a new chapter of multiferroic magnons and pave another path towards low-dissipation nanoelectronics.

Stem cell maintenance in multilayered shoot apical meristems (SAMs) of plants requires strict regulation of cell growth and division. Exactly how the complex milieu of chemical and mechanical signals interact in the central region of the SAM to regulate cell division plane orientation is not well understood. In this paper, simulations using a newly developed multiscale computational model are combined with experimental studies to suggest and test three hypothesized mechanisms for the regulation of cell division plane orientation and the direction of anisotropic cell expansion in the corpus. Simulations predict that in the Apical corpus, WUSCHEL and cytokinin regulate the direction of anisotropic cell expansion, and cells divide according to tensile stress on the cell wall. In the Basal corpus, model simulations suggest dual roles for WUSCHEL and cytokinin in regulating both the direction of anisotropic cell expansion and cell division plane orientation. Simulation results are followed by a detailed analysis of changes in cell characteristics upon manipulation of WUSCHEL and cytokinin in experiments that support model predictions. Moreover, simulations predict that this layer-specific mechanism maintains both the experimentally observed shape and structure of the SAM as well as the distribution of WUSCHEL in the tissue. This provides an additional link between the roles of WUSCHEL, cytokinin, and mechanical stress in regulating SAM growth and proper stem cell maintenance in the SAM.

The recent discovery of ferromagnetism in two-dimensional van der Waals crystals has provoked a surge of interest in the exploration of fundamental spin interaction in reduced dimensions. However, existing material candidates have several limitations, notably lacking intrinsic room-temperature ferromagnetic order and air stability. Here, motivated by the anomalously high Curie temperature observed in bulk diluted magnetic oxides, we demonstrate room-temperature ferromagnetism in Co-doped graphene-like Zinc Oxide, a chemically stable layered material in air, down to single atom thickness. Through the magneto-optic Kerr effect, superconducting quantum interference device and X-ray magnetic circular dichroism measurements, we observe clear evidences of spontaneous magnetization in such exotic material systems at room temperature and above. Transmission electron microscopy and atomic force microscopy results explicitly exclude the existence of metallic Co or cobalt oxides clusters. X-ray characterizations reveal that the substitutional Co atoms form Co²⁺ states in the graphitic lattice of ZnO. By varying the Co doping level, we observe transitions between paramagnetic, ferromagnetic and less ordered phases due to the interplay between impurity-band-exchange and super-exchange interactions. Our discovery opens another path to 2D ferromagnetism at room temperature with the advantage of exceptional tunability and robustness.

We analyze the continuous operation of the nine-qubit error-correcting Bacon-Shor code with all noncommuting gauge operators measured at the same time. The error syndromes are continuously monitored using cross correlations of sets of three measurement signals. We calculate the logical error rates due to X, Y, and Z errors in the physical qubits and compare the continuous implementation with the discrete operation of the code. We find that both modes of operation exhibit similar performances when the measurement strength from continuous measurements is sufficiently strong. We also estimate the value of the crossover error rate of the physical qubits, below which continuous error correction gives smaller logical error rates. Continuous operation has the advantage of passive monitoring of errors and avoids the need for additional circuits involving ancilla qubits.

Spin-transfer torque and spin Hall effects combined with their reciprocal phenomena, spin pumping and inverse spin Hall effects (ISHEs), enable the reading and control of magnetic moments in spintronics. The direct observation of these effects remains elusive in antiferromagnetic-based devices. We report subterahertz spin pumping at the interface of the uniaxial insulating antiferromagnet manganese difluoride and platinum. The measured ISHE voltage arising from spin-charge conversion in the platinum layer depends on the chirality of the dynamical modes of the antiferromagnet, which is selectively excited and modulated by the handedness of the circularly polarized subterahertz irradiation. Our results open the door to the controlled generation of coherent, pure spin currents at terahertz frequencies.

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts, such as JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency-domain correlation to analyze the discriminative characteristics between the manipulated and non-manipulated regions by incorporating the encoder and LSTM network. Finally, the decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With the predicted mask provided by the final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using the ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at the pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets.

Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.

Human action recognition (HAR) is an important topic in computer vision having a wide range of applications: health care, assisted living, surveillance, security, gaming, etc. Despite significant amount of work having been conducted in this area in recent years, the execution speed still limits real-time applications. Moreover, it is highly desirable to have the compute-intensive feature extraction stage done right at the output of the camera to extract and transfer only action feature in multi-camera network setting and hence reduce network bandwidth requirement. In this work, we first evaluate the possibility to perform feature extraction under reduced precision fixed-point arithmetic to ease hardware resource requirements. We compared the Histogram of Oriented Gradient in 3D (HOG3D) feature extraction with state-of-the-art Convolutional Neural Networks (CNNs) methods and shown the later to be 75× slower than the former. Our experiment shows that by re-training the classifier with reduced data precision, the classification performs as well as the original double-precision floating-point. Based on this result, we implement an FPGA-based HAR feature extraction for near camera processing using fixed-point data representation and arithmetic. This implementation, using a single Xilinx Virtex 6 FPGA, achieves about 70× speedup over multicore CPU. Furthermore, a GPU implementation of HAR is introduced with 80× speedup over CPU (on an Nvidia Tesla K20). Last but not least, a power comparison is presented for the three platforms.

Energy proportionality of data center severs have improved drastically over the past decade to the point where near ideal energy proportional servers are now common. These highly energy proportional servers exhibit the unique property where peak efficiency no longer coincides with peak utilization. In this paper, we explore the implications of this property on data center scheduling. We identified that current state of the art data center schedulers does not efficiently leverage these properties, leading to inefficient scheduling decisions. We propose Peak Efficiency Aware Scheduling (PEAS) which can achieve better-than-ideal energy proportionality at the data center level. We demonstrate that PEAS can reduce average power by 25.5% with 3.0% improvement to TCO compared to state-of-the-art scheduling policies.

In the process of pollination, a pollen tube grows from a pollen grain that has fallen on the stigma of a flower. This tube grows towards the ovary of the flower where it will deliver male reproductive material. Knowledge of the dynamics of pollen tube growth will provide a basis for understanding more complex cells that exhibit similar growth behavior. Current pollen tube growth models are a collection of differential equations that represent the level of understanding that biologists have concerning apical growth. Due to their complex nature, these models are not used to verify observed behavior in living cells as seen under a microscope. We present a model that can be used to describe the behavior of growing pollen tube cells in actual experiments. We propose biologically relevant functions based on knowledge of the growth process to explain the dynamics of model parameters. Our model uses an affine transformation to propagate the tip of the cell and statistical parameter estimation to measure necessary parameters during growth. Using experimental videos of pollen tube growth, we show that our model can adapt to various growth scenarios while extracting growth parameters from the videos.