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Articles 1 - 30 of 101
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On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov
On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov
Computer Science Faculty Publications and Presentations
Fair clustering is a constrained clustering problem where we need to partition a set of colored points. The fraction of points of each color in every cluster should be more or less equal to the fraction of points of this color in the dataset. The problem was recently introduced by Chierichetti et al. (2017) [1]. We propose a new construction of coresets for fair clustering for Euclidean and general metrics based on random sampling. For the Euclidean space Rd, we provide the first coreset whose size does not depend exponentially on the dimension d. The question of whether such constructions …
Fea Simulations For Thermal Distributions Of Large Scale 3dic Packages, Suxia Chen, Qiang Wu, Wayne Xun, Jiachen Zhang, Jianping Xun
Fea Simulations For Thermal Distributions Of Large Scale 3dic Packages, Suxia Chen, Qiang Wu, Wayne Xun, Jiachen Zhang, Jianping Xun
Computer Science Faculty Publications and Presentations
As the market increases for Artificial Intelligence and High-Performance Computing applications, the geometry of 3-Dimensional Integrated Circuit packages becomes more complicated; therefore, predicting the thermal distributions of the structures becomes not only more important but also more challenging. The physics governing the thermal distribution is a 3-dimensional partial differential equation. In order to predict the thermal distributions, various approaches such as the layer modeling method have been invented. While practical, these approaches solve a simplified version of the differential equation placing an inherent limitation on their capabilities which may be improved upon. In this research we solve the actual differential …
Static Reflective Surfaces For Improved Terahertz Coverage, Thanh Le, Suresh Singh
Static Reflective Surfaces For Improved Terahertz Coverage, Thanh Le, Suresh Singh
Computer Science Faculty Publications and Presentations
LoS (Line of Sight) MIMO (Multiple Input Multiple Output) is considered the best way to deliver high capacity channels for terahertz communications due to the severe attenuation suffered by reflected components. Unfortunately, terahertz links are easily blocked by any obstruction resulting in link breakage. Therefore, it is necessary to provide alternative paths via reflectors. A problem shared by LoS paths and reflected paths (via polished reflectors) is that the channel matrix is rank 1 in the far-field. As a result, the achieved capacity is lower than what can theoretically be achieved in a rich multi-path environment. In this work, we …
Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang
Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang
Computer Science Faculty Publications and Presentations
Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS …
Self-Optimizing Feature Generation Via Categorical Hashing Representation And Hierarchical Reinforcement Crossing, Wangyang Ying, Dongjie Wang, Kunpeng Liu, Leilei Sun, Yanjie Fu
Self-Optimizing Feature Generation Via Categorical Hashing Representation And Hierarchical Reinforcement Crossing, Wangyang Ying, Dongjie Wang, Kunpeng Liu, Leilei Sun, Yanjie Fu
Computer Science Faculty Publications and Presentations
Feature generation aims to generate new and meaningful features to create a discriminative representation space. A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities. We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, …
Deep Adaptive Graph Clustering Via Von Mises-Fisher Distributions, Pengfei Wang, Daqing Wu, Chong Chen, Kunpeng Liu, Yanjie Fu, Jianqiang Huang, Yuanchun Zhou, Jianfeng Zhan, Xiansheng Hua
Deep Adaptive Graph Clustering Via Von Mises-Fisher Distributions, Pengfei Wang, Daqing Wu, Chong Chen, Kunpeng Liu, Yanjie Fu, Jianqiang Huang, Yuanchun Zhou, Jianfeng Zhan, Xiansheng Hua
Computer Science Faculty Publications and Presentations
Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, …
Gated Recurrent Units For Blockage Mitigation In Mmwave Wireless, Ahmed H. Almutairi, Alireza Keshavarz-Haddad, Ehsan Aryafar
Gated Recurrent Units For Blockage Mitigation In Mmwave Wireless, Ahmed H. Almutairi, Alireza Keshavarz-Haddad, Ehsan Aryafar
Computer Science Faculty Publications and Presentations
Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To …
Auxiliary Features-Guided Super Resolution For Monte Carlo Rendering, Qiqi Hou, Feng Liu
Auxiliary Features-Guided Super Resolution For Monte Carlo Rendering, Qiqi Hou, Feng Liu
Computer Science Faculty Publications and Presentations
This paper investigates super-resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made to super-resolution technologies, it is essentially an ill-posed problem and cannot recover high-frequency details in renderings. To address this problem, we exploit high-resolution auxiliary features to guide super-resolution of low-resolution renderings. These high-resolution auxiliary features can be quickly rendered by a rendering engine and at the same time provide valuable high-frequency details to assist super-resolution. To this end, we develop a cross-modality transformer network that consists of an auxiliary feature branch and a low-resolution …
Rdkg: A Reinforcement Learning Framework For Disease Diagnosis On Knowledge Graph, Shipeng Guo, Kunpeng Liu, Pengfei Wang, Weiwei Dai, Yi Du, Yuanchun Zhou, Wenjuan Cui
Rdkg: A Reinforcement Learning Framework For Disease Diagnosis On Knowledge Graph, Shipeng Guo, Kunpeng Liu, Pengfei Wang, Weiwei Dai, Yi Du, Yuanchun Zhou, Wenjuan Cui
Computer Science Faculty Publications and Presentations
Automatic disease diagnosis from symptoms has attracted much attention in medical practices. It can assist doctors and medical practitioners in narrowing down disease candidates, reducing testing costs, improving diagnosis efficiency, and more importantly, saving human lives. Existing research has made significant progress in diagnosing disease but was limited by the gap between interpretability and accuracy. To fill this gap, in this paper, we propose a method called Reinforced Disease Diagnosis on Knowlege Graph (RDKG). Specifically, we first construct a knowledge graph containing all information from electronic medical records. To capture informative embeddings, we propose an enhanced knowledge graph embedding method …
An Improved Lower Bound For Sparse Reconstruction From Subsampled Walsh Matrices, Jaroslaw Blasiok, Patrick Lopatto, Kyle Luh, Jake Marcinek, Shravas Rao
An Improved Lower Bound For Sparse Reconstruction From Subsampled Walsh Matrices, Jaroslaw Blasiok, Patrick Lopatto, Kyle Luh, Jake Marcinek, Shravas Rao
Computer Science Faculty Publications and Presentations
We give a short argument that yields a new lower bound on the number of uniformly and independently subsampled rows from a bounded, orthonormal matrix necessary to form a matrix with the restricted isometry property. We show that a matrix formed by uniformly and independently subsampling rows of an N ×N Walsh matrix contains a K-sparse vector in the kernel, unless the number of subsampled rows is Ω(KlogKlog(N/K)) — our lower bound applies whenever min(K,N/K) > logC N. Containing a sparse vector in the kernel precludes not only the restricted isometry property, but more generally the application of those matrices for …
Learned Compressive Representations For Single-Photon 3d Imaging, Felipe Gutierrez-Barragan, Fangzhou Mu, Andrei Ardelean, Atul Ingle, Claudio Bruschini, Edoardo Charbon, Yin Li, Mohit Gupta, Andreas Velten
Learned Compressive Representations For Single-Photon 3d Imaging, Felipe Gutierrez-Barragan, Fangzhou Mu, Andrei Ardelean, Atul Ingle, Claudio Bruschini, Edoardo Charbon, Yin Li, Mohit Gupta, Andreas Velten
Computer Science Faculty Publications and Presentations
Single-photon 3D cameras can record the time-of-arrival of billions of photons per second with picosecond accuracy. One common approach to summarize the photon data stream is to build a per-pixel timestamp histogram, resulting in a 3D histogram tensor that encodes distances along the time axis. As the spatio-temporal resolution of the histogram tensor increases, the in-pixel memory requirements and output data rates can quickly become impractical. To overcome this limitation, we propose a family of linear compressive representations of histogram tensors that can be computed efficiently, in an online fashion, as a matrix operation. We design practical lightweight compressive representations …
Panoramas From Photons, Sacha Jungerman, Atul Ingle, Mohit Gupta
Panoramas From Photons, Sacha Jungerman, Atul Ingle, Mohit Gupta
Computer Science Faculty Publications and Presentations
Scene reconstruction in the presence of high-speed motion and low illumination is important in many applications such as augmented and virtual reality, drone navigation, and autonomous robotics. Traditional motion estimation techniques fail in such conditions, suffering from too much blur in the presence of high-speed motion and strong noise in low-light conditions. Single-photon cameras have recently emerged as a promising technology capable of capturing hundreds of thousands of photon frames per second thanks to their high speed and extreme sensitivity. Unfortunately, traditional computer vision techniques are not well suited for dealing with the binary-valued photon data captured by these cameras …
Sequential Frame-Interpolation And Dct-Based Video Compression Framework, Yeganeh Jalalpour, Wu-Chi Feng, Feng Liu
Sequential Frame-Interpolation And Dct-Based Video Compression Framework, Yeganeh Jalalpour, Wu-Chi Feng, Feng Liu
Computer Science Faculty Publications and Presentations
Video data is ubiquitous; capturing, transferring, and storing even compressed video data is challenging because it requires substantial resources. With the large amount of video traffic being transmitted on the internet, any improvement in compressing such data, even small, can drastically impact resource consumption. In this paper, we present a hybrid video compression framework that unites the advantages of both DCT-based and interpolation-based video compression methods in a single framework. We show that our work can deliver the same visual quality or, in some cases, improve visual quality while reducing the bandwidth by 10--20%.
A Simpler Machine Learning Model For Acute Kidney Injury Risk Stratification In Hospitalized Patients, Yirui Hu, Kunpeng Liu, Kevin Ho, David Riviello, Jason Brown, Alex R. Chang, Gurmukteshwar Singh, H. Lester Kirchner
A Simpler Machine Learning Model For Acute Kidney Injury Risk Stratification In Hospitalized Patients, Yirui Hu, Kunpeng Liu, Kevin Ho, David Riviello, Jason Brown, Alex R. Chang, Gurmukteshwar Singh, H. Lester Kirchner
Computer Science Faculty Publications and Presentations
Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during …
Quantum Algorithms For Attacking Hardness Assumptions In Classical And Post‐Quantum Cryptography, J.-F. Biasse, X. Bonnetain, E. Kirshanova, A. Schrottenloher, Fang Song
Quantum Algorithms For Attacking Hardness Assumptions In Classical And Post‐Quantum Cryptography, J.-F. Biasse, X. Bonnetain, E. Kirshanova, A. Schrottenloher, Fang Song
Computer Science Faculty Publications and Presentations
In this survey, the authors review the main quantum algorithms for solving the computational problems that serve as hardness assumptions for cryptosystem. To this end, the authors consider both the currently most widely used classically secure cryptosystems, and the most promising candidates for post-quantum secure cryptosystems. The authors provide details on the cost of the quantum algorithms presented in this survey. The authors furthermore discuss ongoing research directions that can impact quantum cryptanalysis in the future.
The Db Community Vis-À-Vis Environmental, Health, And Societal Grand Challenges: Innovation Engine, Plumber, Or Bystander?, Anastasia Ailamaki, Leilani Battle, Johannes Gehrke, Masaru Kitsuregawa, David Maier, Christopher Re, Meihui Zhang, Magdalena Balazinska
The Db Community Vis-À-Vis Environmental, Health, And Societal Grand Challenges: Innovation Engine, Plumber, Or Bystander?, Anastasia Ailamaki, Leilani Battle, Johannes Gehrke, Masaru Kitsuregawa, David Maier, Christopher Re, Meihui Zhang, Magdalena Balazinska
Computer Science Faculty Publications and Presentations
This panel considers the role of the database research community in addressing humanity's greatest challenges. Are we an innovation engine, tool providers, or are we standing on the side while other research communities take the lead?
Sl-Cyclegan: Blind Motion Deblurring In Cycles Using Sparse Learning, Ali Syed Saqlain, Li-Yun Wang, Zhiyong Liu
Sl-Cyclegan: Blind Motion Deblurring In Cycles Using Sparse Learning, Ali Syed Saqlain, Li-Yun Wang, Zhiyong Liu
Computer Science Faculty Publications and Presentations
In this paper, we introduce an end-to-end generative adversarial network (GAN) based on sparse learning for single image motion deblurring, which we called SL-CycleGAN. For the first time in image motion deblurring, we propose a sparse ResNet-block as a combination of sparse convolution layers and a trainable spatial pooler k-winner based on HTM (Hierarchical Temporal Memory) to replace non-linearity such as ReLU in the ResNet-block of SL-CycleGAN generators. Furthermore, we take our inspiration from the domain-to-domain translation ability of the CycleGAN, and we show that image deblurring can be cycle-consistent while achieving the best qualitative results. Finally, we perform extensive …
Rate Maximization In A Uav Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization, Syed Muhammad Hashir, Sabyasachi Gupta, Gavin Megson, Ehsan Aryafar, Joseph Camp
Rate Maximization In A Uav Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization, Syed Muhammad Hashir, Sabyasachi Gupta, Gavin Megson, Ehsan Aryafar, Joseph Camp
Computer Science Faculty Publications and Presentations
In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the …
Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles, Nirupama Bulusu, Ehsan Aryafar, Feng Liu
Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles, Nirupama Bulusu, Ehsan Aryafar, Feng Liu
Computer Science Faculty Publications and Presentations
Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to …
Learned Dual-View Reflection Removal, Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron, Neal Wadhwa, Rahul Garg, Feng Liu, Tianfan Xue
Learned Dual-View Reflection Removal, Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron, Neal Wadhwa, Rahul Garg, Feng Liu, Tianfan Xue
Computer Science Faculty Publications and Presentations
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registration, and a learned synthesis model for combining aligned image pairs. Because …
View Synthesis Of Dynamic Scenes Based On Deep 3d Mask Volume, Kai-En Lin, Guowei Yang, Lei Xiao, Feng Liu, Ravi Ramamoorthi
View Synthesis Of Dynamic Scenes Based On Deep 3d Mask Volume, Kai-En Lin, Guowei Yang, Lei Xiao, Feng Liu, Ravi Ramamoorthi
Computer Science Faculty Publications and Presentations
Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high-quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables …
Selectivity And Robustness Of Sparse Coding Networks, Dylan M. Paiton, Charles Frye, Sheng Y. Lundquist, Joel D. Bowen, Ryan Zarcone, Bruno A. Olshausen
Selectivity And Robustness Of Sparse Coding Networks, Dylan M. Paiton, Charles Frye, Sheng Y. Lundquist, Joel D. Bowen, Ryan Zarcone, Bruno A. Olshausen
Computer Science Faculty Publications and Presentations
We investigate how the population nonlinearities resulting from lateral inhibition and thresholding in sparse coding networks influence neural response selectivity and robustness. We show that when compared to pointwise nonlinear models, such population nonlinearities improve the selectivity to a preferred stimulus and protect against adversarial perturbations of the input. These findings are predicted from the geometry of the single-neuron iso-response surface, which provides new insight into the relationship between selectivity and adversarial robustness. Inhibitory lateral connections curve the iso-response surface outward in the direction of selectivity. Since adversarial perturbations are orthogonal to the iso-response surface, adversarial attacks tend to be …
Computer Science For Equity: Teacher Education, Agency, And Statewide Reform, Joanna Goode, Max Skorodinsky, Jill Hubbard, James Hook
Computer Science For Equity: Teacher Education, Agency, And Statewide Reform, Joanna Goode, Max Skorodinsky, Jill Hubbard, James Hook
Computer Science Faculty Publications and Presentations
This paper reports on a statewide “Computer Science for All” initiative in Oregon that aims to democratize high school computer science and broaden participation in an academic subject that is one of the most segregated disciplines nationwide, in terms of both race and gender. With no statewide policies to support computing instruction, Oregon's legacy of computer science education has been marked by both low participation and by rates of underrepresented students falling well-below the already dismal national rates. The study outlined in this paper focuses on how teacher education can support educators in developing knowledge and agency, and impacting policies …
An Uncultivated Virus Infecting A Symbiotic Nanoarchaeota In The Hot Springs Of Yellowstone National Park, Jacob H. Munson-Mcgee, Colleen Rooney, Mark J. Young
An Uncultivated Virus Infecting A Symbiotic Nanoarchaeota In The Hot Springs Of Yellowstone National Park, Jacob H. Munson-Mcgee, Colleen Rooney, Mark J. Young
Computer Science Faculty Publications and Presentations
The Nanoarchaeota are small cells with reduced genomes that are found attached to and dependent on a second archaeal cell for their growth and replication. Initially found in marine hydrothermal environments and subsequently in terrestrial geothermal hot springs, the Nanoarchaeota species that have been described are obligate ectobionts, each with a different host species. However, no viruses have been described that infect the Nanoarchaeota. Here we identify a virus infecting Nanoarchaeota using a combination of viral metagenomic and bioinformatic approaches. This virus, tentatively named Nanoarchaeota Virus 1 (NAV1), consists of a 35.6kb circular DNA genome encoding for 52 proteins. We …
Artificial Intelligence Hits The Barrier Of Meaning, Melanie Mitchell
Artificial Intelligence Hits The Barrier Of Meaning, Melanie Mitchell
Computer Science Faculty Publications and Presentations
Today’s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning. The lack of humanlike understanding in machines is underscored by recent studies demonstrating lack of robustness of state-of-the-art deep-learning systems. Deeper networks and larger datasets alone are not likely to unlock AI’s “barrier of meaning”; instead the field will need to embrace its original roots as an interdisciplinary science of intelligence.
Context-Aware Synthesis For Video Frame Interpolation, Simon Niklaus, Feng Liu
Context-Aware Synthesis For Video Frame Interpolation, Simon Niklaus, Feng Liu
Computer Science Faculty Publications and Presentations
Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a …
Samu: Design And Implementation Of Frequency Selectivity-Aware Multi-User Mimo For Wlans, Yongjiu Du, Yan Shi, Ehsan Aryafar, Pengfei Cui, Joseph Camp, Mung Chiang
Samu: Design And Implementation Of Frequency Selectivity-Aware Multi-User Mimo For Wlans, Yongjiu Du, Yan Shi, Ehsan Aryafar, Pengfei Cui, Joseph Camp, Mung Chiang
Computer Science Faculty Publications and Presentations
The traffic demand of wireless networks is expected to increase 1000-fold over the next decade. In anticipation of such increasing data demand for dense networks with a large number of stations, IEEE 802.11ax has introduced key technologies for capacity improvement including Orthogonal Frequency-Division Multiple Access (OFDMA), multi-user multi-input multi-output (MU-MIMO), and greater bandwidth. However, IEEE 802.11ax has yet to fully define a specific scheduling framework, on which the throughput improvement of networks significantly depends. Even within a 20 MHz of bandwidth, users experience heterogeneous channel orthogonality characteristics across sub-carriers, which prevents access points (APs) from achieving the ideal multi-user gain. …
Challenges And Opportunities In Transportation Data, Kristin A. Tufte, Kushal Datta, Alekh Jindal, David Maier, Robert L. Bertini
Challenges And Opportunities In Transportation Data, Kristin A. Tufte, Kushal Datta, Alekh Jindal, David Maier, Robert L. Bertini
Computer Science Faculty Publications and Presentations
From the time and money lost sitting in congestion and waiting for traffic signals to change, to the many people injured and killed in traffic crashes each year, to the emissions and energy consumption from our vehicles, the effects of transportation on our daily lives are immense. A wealth of transportation data is available to help address these problems; from data from sensors installed to monitor and operate the roadways and traffic signals to data from cell phone apps and -- just over the horizon -- data from connected vehicles and infrastructure. However, this wealth of data has yet to …
Video Frame Interpolation Via Adaptive Separable Convolution, Simon Niklaus, Long Mai, Feng Liu
Video Frame Interpolation Via Adaptive Separable Convolution, Simon Niklaus, Long Mai, Feng Liu
Computer Science Faculty Publications and Presentations
Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion, which limits the number of pixels whose kernels can be estimated at once due to the large memory demand. To address this problem, this paper formulates frame interpolation as local separable convolution over input frames using pairs of 1D kernels. Compared to regular 2D …
Fast On-Line Kernel Density Estimation For Active Object Localization, Anthony D. Rhodes, Max H. Quinn, Melanie Mitchell
Fast On-Line Kernel Density Estimation For Active Object Localization, Anthony D. Rhodes, Max H. Quinn, Melanie Mitchell
Computer Science Faculty Publications and Presentations
A major goal of computer vision is to enable computers to interpret visual situations—abstract concepts (e.g., “a person walking a dog,” “a crowd waiting for a bus,” “a picnic”) whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. In this paper, we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images. In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations— a situation model—that represent the expected spatial structure of the given situation. These …