pwc-archive/papers-with-abstracts huggingface dataset:
Automated Detection of Rest Disruptions in Critically Ill Patients
Authors: Vasundhra Iyengar, Azra Bihorac, Parisa Rashidi
Date: 2020-04-21 00:00:00
Tasks: Sleep Quality

Sleep has been shown to be an indispensable and important component of patients recovery process. Nonetheless, sleep quality of patients in the Intensive Care Unit (ICU) is often low, due to factors such as noise, pain, and frequent nursing care activities. Frequent sleep disruptions by the medical staff and/or visitors at certain times might lead to disruption of patient sleep-wake cycle and can also impact the severity of pain. Examining the association between sleep quality and frequent visitation has been difficult, due to lack of automated methods for visitation detection. In this study, we recruited 38 patients to automatically assess visitation frequency from captured video frames. We used the DensePose R-CNN (ResNet-101) model to calculate the number of people in the room in a video frame. We examined when patients are interrupted the most, and we examined the association between frequent disruptions and patient outcomes on pain and length of stay.
Importance of Data Loading Pipeline in Training Deep Neural Networks
Authors: Mahdi Zolnouri, Xinlin Li, Vahid Partovi Nia
Date: 2020-04-21 00:00:00
Tasks: Data Augmentation, GPU

Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loading data takes a significant portion of model training time. As GPU servers are typically expensive, tricks that can save training time are valuable.Slow training is observed especially on real-world applications where exhaustive data augmentation operations are required. Data augmentation techniques include: padding, rotation, adding noise, down sampling, up sampling, etc. These additional operations increase the need to build an efficient data loading pipeline, and to explore existing tools to speed up training time. We focus on the comparison of two main tools designed for this task, namely binary data format to accelerate data reading, and NVIDIA DALI to accelerate data augmentation. Our study shows improvement on the order of 20% to 40% if such dedicated tools are used.
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds
Authors: Xuanyu YIN, Yoko SASAKI, Weimin WANG, Kentaro SHIMIZU
Date: 2020-04-21 00:00:00
Tasks: 2D Object Detection, 3D Object Detection, 3D Object Recognition, Autonomous Driving, Camera Calibration, Clustering, GPU, Object, object-detection, Object Detection, Object Recognition, Point Cloud Segmentation

Lidar based 3D object detection and classification tasks are essential for autonomous driving(AD). A lidar sensor can provide the 3D point cloud data reconstruction of the surrounding environment. However, real time detection in 3D point clouds still needs a strong algorithmic. This paper proposes a 3D object detection method based on point cloud and image which consists of there parts.(1)Lidar-camera calibration and undistorted image transformation. (2)YOLO-based detection and PointCloud extraction, (3)K-means based point cloud segmentation and detection experiment test and evaluation in depth image. In our research, camera can capture the image to make the Real-time 2D object detection by using YOLO, we transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar. By comparing whether 2D coordinate transferred from the 3D point is in the object bounding box or not can achieve High-speed 3D object recognition function in GPU. The accuracy and precision get imporved after k-means clustering in point cloud. The speed of our detection method is a advantage faster than PointNet.
Quality Guided Sketch-to-Photo Image Synthesis
Authors: Uche Osahor, Hadi Kazemi, Ali Dabouei, Nasser Nasrabadi
Date: 2020-04-20 00:00:00
Tasks: Attribute, Generative Adversarial Network, Image Generation

Facial sketches drawn by artists are widely used for visual identification applications and mostly by law enforcement agencies, but the quality of these sketches depend on the ability of the artist to clearly replicate all the key facial features that could aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. In this work, we propose a novel approach that adopts a generative adversarial network that synthesizes a single sketch into multiple synthetic images with unique attributes like hair color, sex, etc. We incorporate a hybrid discriminator which performs attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network and an identity preserving network that maintains the identity of the synthesised image throughout the training process. Our approach is aimed at improving the visual appeal of the synthesised images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesised image. We synthesised sketches using XDOG filter for the CelebA, WVU Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results are impressive compared to current state of the art.
IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition
Authors: Gibran Benitez-Garcia, Jesus Olivares-Mercado, Gabriel Sanchez-Perez, Keiji Yanai
Date: 2020-04-20 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Optical Flow Estimation, Semantic Segmentation

In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. This dataset contains more than 4,000 gesture samples and 800,000 RGB frames from 50 distinct subjects. We design 13 different static and dynamic gestures focused on interaction with touchless screens. We especially consider the scenario when continuous gestures are performed without transition states, and when subjects perform natural movements with their hands as non-gesture actions. Gestures were collected from about 30 diverse scenes, with real-world variation in background and illumination. With our dataset, the performance of three 3D-CNN models is evaluated on the tasks of isolated and continuous real-time HGR. Furthermore, we analyze the possibility of increasing the recognition accuracy by adding multiple modalities derived from RGB frames, i.e., optical flow and semantic segmentation, while keeping the real-time performance of the 3D-CNN model. Our empirical study also provides a comparison with the publicly available nvGesture (NVIDIA) dataset. The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark, and may help the community to step forward in the continuous HGR. Our dataset and pre-trained models used in the evaluation are publicly available at https://github.com/GibranBenitez/IPN-hand.
GREG: A Global Level Relation Extraction with Knowledge Graph Embedding
Authors: Kuekyeng Kim, Yuna Hur, Gyeongmin Kim, Heuiseok Lim
Date: 2020-02-10 00:00:00
Proceeding: Appl. Sci. 2020 2020 2
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, Relation, Relation Extraction

In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level ‘local’ relation’s to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model’s modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.
Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
Authors: Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck
Date: 2020-03-06 00:00:00
Proceeding: MDPI Applied Sciences 2020 3
Tasks: Benchmarking, Image Reconstruction, MRI Reconstruction

Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging (MRI). A lot of networks are being developed, but the comparisons remain hard because the frameworks used are not the same among studies, the networks are not properly re-trained, and the datasets used are not the same among comparisons. The recent release of a public dataset, fastMRI, consisting of raw k-space data, encouraged us to write a consistent benchmark of several deep neural networks for MR image reconstruction. This paper shows the results obtained for this benchmark, allowing to compare the networks, and links the open source implementation of all these networks in Keras. The main finding of this benchmark is that it is beneficial to perform more iterations between the image and the measurement spaces compared to having a deeper per-space network.
Fast Optical System Identification by Numerical Interferometry
Authors: Sidharth Gupta, Rémi Gribonval, Laurent Daudet, Ivan Dokmanić
Date: 2019-11-04 00:00:00
Tasks: CPU, GPU, Retrieval

We propose a numerical interferometry method for identification of optical multiply-scattering systems when only intensity can be measured. Our method simplifies the calibration of optical transmission matrices from a quadratic to a linear inverse problem by first recovering the phase of the measurements. We show that by carefully designing the probing signals, measurement phase retrieval amounts to a distance geometry problem---a multilateration---in the complex plane. Since multilateration can be formulated as a small linear system which is the same for entire rows of the transmission matrix, the phases can be retrieved very efficiently. To speed up the subsequent estimation of transmission matrices, we design calibration signals so as to take advantage of the fast Fourier transform, achieving a numerical complexity almost linear in the number of transmission matrix entries. We run experiments on real optical hardware and use the numerically computed transmission matrix to recover an unseen image behind a scattering medium. Where the previous state-of-the-art method reports hours to compute the transmission matrix on a GPU, our method takes only a few minutes on a CPU.
CARP: Compression through Adaptive Recursive Partitioning for Multi-dimensional Images
Authors: Rongjie Liu, Meng Li, Li Ma
Date: 2019-12-11 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Compression

Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. Desirable properties in compression methods include (1) high reconstruction quality at a wide range of compression rates while preserving key local details, (2) computational scalability, (3) applicability to a variety of different image/video types and of different dimensions, and (4) ease of tuning. We present such a method for multi-dimensional image compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, leading to a parsimonious representation that preserves information. CARP uses a multi-layer Bayesian hierarchical model to achieve self-tuning and regularization to avoid overfitting resulting in one single parameter to be specified by the user to attain the desired compression rate. Extensive numerical experiments using a variety of datasets including 2D ImageNet, 3D medical image, and real-life YouTube and surveillance videos show that CARP dominates the state-of-the-art compression approaches including JPEG, JPEG2000, MPEG4, and a neural network-based method for all of these different image types and often on nearly all of the individual images.
On the importance of initialization and momentum in deep learning
Authors: Ilya Sutskever, James Martens, George Dahl, Geoffrey Hinton
Date: 2013-05-26 00:00:00
Proceeding: Proceedings of the 30th International Conference on Machine Learning 2013 5
Tasks: Deep Learning, Second-order methods

Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned. Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods suffice for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods.
Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
Authors: Cosma Rohilla Shalizi, Kristina Lisa Shalizi
Date: 2004-06-06 00:00:00

We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approximates the ideal predictor from data. We discuss the reliability of CSSR, its data requirements, and its performance in simulations. Finally, we compare our approach to existing methods using variable-length Markov models and cross-validated hidden Markov models, and show theoretically and experimentally that our method delivers results superior to the former and at least comparable to the latter.
Environmental Sound Classification on Microcontrollers using Convolutional Neural Networks
Authors: Jon Nordby
Date: 2019-05-15 00:00:00
Proceeding: n/a 2019 5
Tasks: Classification, CPU, Environmental Sound Classification, General Classification, Sound Classification

Noise is a growing problem in urban areas, and according to the WHO is the second environmental cause of health problems in Europe. Noise monitoring using Wireless Sensor Networks are being applied in order to understand and help mitigate these noise problems. It is desirable that these sensor systems, in addition to logging the sound level, can indicate what the likely sound source is. However, transmitting audio to a cloud system for classification is energy-intensive and may cause privacy issues. It is also critical for widespread adoption and dense sensor coverage that individual sensor nodes are low-cost. Therefore we propose to perform the noise classification on the sensor node, using a low-cost microcontroller.

Several Convolutional Neural Networks were designed for the STM32L476 low-power microcontroller using the Keras deep-learning framework, and deployed using the vendor-provided X-CUBE-AI inference engine. The resource budget for the model was set at maximum 50% utilization of CPU, RAM, and FLASH. 10 model variations were evaluated on the Environmental Sound Classification task using the standard Urbansound8k dataset.

The best models used Depthwise-Separable convolutions with striding for downsampling, and were able to reach 70.9% mean 10-fold accuracy while consuming only 20% CPU. To our knowledge, this is the highest reported performance on Urbansound8k using a microcontroller. One of the models was also tested on a microcontroller development device, demonstrating the classification of environmental sounds in real-time.

These results indicate that it is computationally feasible to classify environmental sound on low-power microcontrollers. Further development should make it possible to create wireless sensor-networks for noise monitoring with on-edge noise source classification.
On Improving Antivirus Scanning Engines: Memory On- Access Scanner
Authors: Mohammed I. Al-Saleh and Rasha K. Al-Huthaifi
Date: 2017-07-25 00:00:00
Proceeding: Journal of Computer Science 2017 7

The Antivirus (AV) products are utilized by home user's community to attain protection. To some extent, the AV meets users' expectations by detecting previously known malware samples. In this study, we question the set of events which should trigger the AV to scan data. Scanning every single piece of data as it moves from one location into another could be a demanding and performance-killing task. The AV faces a design challenge when deciding what kind of data to scan and when to do so. Typically, the on-access scanner component of the AV scans data upon moving from/to hard drives. Other occurrences of data movements are of equal importance. For example, data moves between different memory locations or between memory and network. In this study, we are motivated to explore what it needs to be done by the AV upon various data movements. We design and implement a system that has a capability of scanning memory when necessary. We recognize and intercept the most effective API calls that involve memory. Afterwards, we extract involved data and scan it if it has not been scanned before. We test our system against 15 real malware and find out that our system is capable of detecting all malware samples. Furthermore, we provide a thorough performance study to present the overhead of our system.
It’s Not You, it’s Me: Detecting Flirting and its Misperception in Speed-Dates
Authors: a
Date: 2009-03-01 00:00:00
Proceeding: EMNLP09 2009 3
Tasks: Dialogue Understanding

Automatically detecting human social intentions from spoken conversation is an
important task for dialogue understanding. Since the social intentions of the
speaker may differ from what is perceived
by the hearer, systems that analyze human
conversations need to be able to extract
both the perceived and the intended social
meaning. We investigate this difference
between intention and perception by using
a spoken corpus of speed-dates in which
both the speaker and the listener rated the
speaker on flirtatiousness. Our flirtationdetection system uses prosodic, dialogue,
and lexical features to detect a speaker’s
intent to flirt with up to 71.5% accuracy,
significantly outperforming the baseline,
but also outperforming the human interlocuters. Our system addresses lexical feature sparsity given the small amount of
training data by using an autoencoder network to map sparse lexical feature vectors
into 30 compressed features. Our analysis shows that humans are very poor perceivers of intended flirtatiousness, instead
often projecting their own intended behavior onto their interlocutors.
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm
Authors: Sayar Karmakar, Anirbit Mukherjee
Date: 2020-05-08 00:00:00
Tasks: Data Poisoning

In this work, we demonstrate provable guarantees on the training of a single ReLU gate in hitherto unexplored regimes. We give a simple iterative stochastic algorithm that can train a ReLU gate in the realizable setting in linear time while using significantly milder conditions on the data distribution than previous such results. Leveraging certain additional moment assumptions, we also show a first-of-its-kind approximate recovery of the true label generating parameters under an (online) data-poisoning attack on the true labels, while training a ReLU gate by the same algorithm. Our guarantee is shown to be nearly optimal in the worst case and its accuracy of recovering the true weight degrades gracefully with increasing probability of attack and its magnitude. For both the realizable and the non-realizable cases as outlined above, our analysis allows for mini-batching and computes how the convergence time scales with the mini-batch size. We corroborate our theorems with simulation results which also bring to light a striking similarity in trajectories between our algorithm and the popular S.G.D. algorithm - for which similar guarantees as here are still unknown.
The critical locus of overparameterized neural networks
Authors: Y. Cooper
Date: 2020-05-08 00:00:00

Many aspects of the geometry of loss functions in deep learning remain mysterious. In this paper, we work toward a better understanding of the geometry of the loss function $L$ of overparameterized feedforward neural networks. In this setting, we identify several components of the critical locus of $L$ and study their geometric properties. For networks of depth $\ell \geq 4$, we identify a locus of critical points we call the star locus $S$. Within $S$ we identify a positive-dimensional sublocus $C$ with the property that for $p \in C$, $p$ is a degenerate critical point, and no existing theoretical result guarantees that gradient descent will not converge to $p$. For very wide networks, we build on earlier work and show that all critical points of $L$ are degenerate, and give lower bounds on the number of zero eigenvalues of the Hessian at each critical point. For networks that are both deep and very wide, we compare the growth rates of the zero eigenspaces of the Hessian at all the different families of critical points that we identify. The results in this paper provide a starting point to a more quantitative understanding of the properties of various components of the critical locus of $L$.