2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021
Our objective is to detect anomalies in video while also automatically explaining the reason behi... more Our objective is to detect anomalies in video while also automatically explaining the reason behind the detector's response. In a practical sense, explainability is crucial for this task as the required response to an anomaly depends on its nature and severity. However, most leading methods (based on deep neural networks) are not interpretable and hide the decision making process in uninterpretable feature representations. In an effort to tackle this problem we make the following contributions: (1) we show how to build interpretable feature representations suitable for detecting anomalies with state of the art performance, (2) we propose an interpretable probabilistic anomaly detector which can describe the reason behind it's response using high level concepts, (3) we are the first to directly consider object interactions for anomaly detection and (4) we propose a new task of explaining anomalies and release a large dataset for evaluating methods on this task. Our method competes well with the state of the art on public datasets while also providing anomaly explanation based on objects and their interactions.
State of the art quantum transport models for semiconductor nanodevices attribute negative (posit... more State of the art quantum transport models for semiconductor nanodevices attribute negative (positive) unit charges to states of the conduction (valence) band. Hybrid states that enable band-to-band tunneling are subject to interpolation that yield model dependent charge contributions. In any nanodevice structure, these models rely on device and physics specific input for the dielectric constants. This paper exemplifies the large variability of different charge interpretation models when applied to ultrathin body transistor performance predictions. To solve this modeling challenge, an electron-only band structure model is extended to atomistic quantum transport. Performance predictions of MOSFETs and tunneling FETs confirm the generality of the new model and its independence of additional screening models.
2015 IEEE International Conference on Computer Vision (ICCV), 2015
The objective of this work is human pose estimation in videos, where multiple frames are availabl... more The objective of this work is human pose estimation in videos, where multiple frames are available. We investigate a ConvNet architecture that is able to benefit from temporal context by combining information across the multiple frames using optical flow. To this end we propose a network architecture with the following novelties: (i) a deeper network than previously investigated for regressing heatmaps; (ii) spatial fusion layers that learn an implicit spatial model; (iii) optical flow is used to align heatmap predictions from neighbouring frames; and (iv) a final parametric pooling layer which learns to combine the aligned heatmaps into a pooled confidence map. We show that this architecture outperforms a number of others, including one that uses optical flow solely at the input layers, one that regresses joint coordinates directly, and one that predicts heatmaps without spatial fusion. The new architecture outperforms the state of the art by a large margin on three video pose estimation datasets, including the very challenging Poses in the Wild dataset, and outperforms other deep methods that don't use a graphical model on the single-image FLIC benchmark (and also [5, 35] in the high precision region).
Advances in Natural Sciences: Nanoscience and Nanotechnology, 2013
ABSTRACT Spin-based electronics or 'spintronics' has been a topic of interest for... more ABSTRACT Spin-based electronics or 'spintronics' has been a topic of interest for over two decades. Electronic devices based on the manipulation of the electron spin are believed to offer the possibility of very small, non-volatile and ultrafast devices with very low power consumption. Since the proposal of a spin-field-effect transistor (SpinFET) by Datta and Das in 1990, many attempts have been made to achieve spin injection, detection and manipulation in semiconductor materials either by incorporating ferromagnetic materials into device architectures or by using external magnetic fields. This approach has significant design complexities, partly due to the influence of stray magnetic fields on device operation. In addition, magnetic electrodes can have magneto-resistance and spurious Hall voltages that can complicate device performance. To date, there has been no successful report of a working Datta–Das SpinFET. Over the last few years we have investigated an all-electric means of manipulating spins, one that only relies on electric fields and voltages and not on ferromagnetic materials or external magnetic fields. We believe we have found a pathway toward this goal, using in-plane side-gated quantum point contacts (QPCs) that rely on lateral spin–orbit coupling to create spin polarization. In this paper we discuss several aspects of our work, beginning with our finding what we believe is nearly complete spin-polarization in InAs QPCs by purely electrical means, our theoretical work to understand the basic mechanisms leading to that situation (asymmetric lateral confinement, lateral spin–orbit coupling and a strong e–e interaction), and our recent work extending the effort to GaAs and to dual QPC systems where one QPC acts as a polarizer and the other as an analyzer.
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021
Our objective is to detect anomalies in video while also automatically explaining the reason behi... more Our objective is to detect anomalies in video while also automatically explaining the reason behind the detector's response. In a practical sense, explainability is crucial for this task as the required response to an anomaly depends on its nature and severity. However, most leading methods (based on deep neural networks) are not interpretable and hide the decision making process in uninterpretable feature representations. In an effort to tackle this problem we make the following contributions: (1) we show how to build interpretable feature representations suitable for detecting anomalies with state of the art performance, (2) we propose an interpretable probabilistic anomaly detector which can describe the reason behind it's response using high level concepts, (3) we are the first to directly consider object interactions for anomaly detection and (4) we propose a new task of explaining anomalies and release a large dataset for evaluating methods on this task. Our method competes well with the state of the art on public datasets while also providing anomaly explanation based on objects and their interactions.
State of the art quantum transport models for semiconductor nanodevices attribute negative (posit... more State of the art quantum transport models for semiconductor nanodevices attribute negative (positive) unit charges to states of the conduction (valence) band. Hybrid states that enable band-to-band tunneling are subject to interpolation that yield model dependent charge contributions. In any nanodevice structure, these models rely on device and physics specific input for the dielectric constants. This paper exemplifies the large variability of different charge interpretation models when applied to ultrathin body transistor performance predictions. To solve this modeling challenge, an electron-only band structure model is extended to atomistic quantum transport. Performance predictions of MOSFETs and tunneling FETs confirm the generality of the new model and its independence of additional screening models.
2015 IEEE International Conference on Computer Vision (ICCV), 2015
The objective of this work is human pose estimation in videos, where multiple frames are availabl... more The objective of this work is human pose estimation in videos, where multiple frames are available. We investigate a ConvNet architecture that is able to benefit from temporal context by combining information across the multiple frames using optical flow. To this end we propose a network architecture with the following novelties: (i) a deeper network than previously investigated for regressing heatmaps; (ii) spatial fusion layers that learn an implicit spatial model; (iii) optical flow is used to align heatmap predictions from neighbouring frames; and (iv) a final parametric pooling layer which learns to combine the aligned heatmaps into a pooled confidence map. We show that this architecture outperforms a number of others, including one that uses optical flow solely at the input layers, one that regresses joint coordinates directly, and one that predicts heatmaps without spatial fusion. The new architecture outperforms the state of the art by a large margin on three video pose estimation datasets, including the very challenging Poses in the Wild dataset, and outperforms other deep methods that don't use a graphical model on the single-image FLIC benchmark (and also [5, 35] in the high precision region).
Advances in Natural Sciences: Nanoscience and Nanotechnology, 2013
ABSTRACT Spin-based electronics or 'spintronics' has been a topic of interest for... more ABSTRACT Spin-based electronics or 'spintronics' has been a topic of interest for over two decades. Electronic devices based on the manipulation of the electron spin are believed to offer the possibility of very small, non-volatile and ultrafast devices with very low power consumption. Since the proposal of a spin-field-effect transistor (SpinFET) by Datta and Das in 1990, many attempts have been made to achieve spin injection, detection and manipulation in semiconductor materials either by incorporating ferromagnetic materials into device architectures or by using external magnetic fields. This approach has significant design complexities, partly due to the influence of stray magnetic fields on device operation. In addition, magnetic electrodes can have magneto-resistance and spurious Hall voltages that can complicate device performance. To date, there has been no successful report of a working Datta–Das SpinFET. Over the last few years we have investigated an all-electric means of manipulating spins, one that only relies on electric fields and voltages and not on ferromagnetic materials or external magnetic fields. We believe we have found a pathway toward this goal, using in-plane side-gated quantum point contacts (QPCs) that rely on lateral spin–orbit coupling to create spin polarization. In this paper we discuss several aspects of our work, beginning with our finding what we believe is nearly complete spin-polarization in InAs QPCs by purely electrical means, our theoretical work to understand the basic mechanisms leading to that situation (asymmetric lateral confinement, lateral spin–orbit coupling and a strong e–e interaction), and our recent work extending the effort to GaAs and to dual QPC systems where one QPC acts as a polarizer and the other as an analyzer.
Uploads
Papers by james charles