OpenRaiser/Intern-Atlas huggingface dataset:
Energy-Efficient Power Control of Train-ground mmWave Communication for High Speed Trains
Venue: arXiv cs.NI 2019
Year: 2019

High speed train system has proven to be a very flexible and attractive system that can be developed under various circumstances and in different contexts and cultures. As a result, high speed trains are widely deployed around the world. Providing more reliable and higher data rate communication services for high speed trains has become one of the most urgent challenges. With vast amounts of spectrum available, the millimeter wave (mmWave) system is able to provide transmission rates of several gigabits per second for high speed trains. At the same time, mmWave communication also suffers from high attenuation, thus higher energy efficiency should be considered. This paper proposes an energy efficient power control scheme of train-ground mmWave communication for high speed trains. Considering a beam switching method for efficient beam alignment, we first establish position prediction model, the realistic direction antenna model and receive power model. And then we allocate the transmission power rationally through the power minimization algorithm while ensuring the total amount of transmission data. Based on this, this paper also develops a hybrid optimization scheme and finds the limit of total energy consumption when the number of segments goes to infinity. Through simulation with various system parameters and taking velocity estimation error into account, we demonstrate the superior performance of our schemes.
Beyond Human: Animals as an Escape from Stereotype Avatars in Virtual Reality Games
Venue: arXiv cs.HC 2019
Year: 2019

Virtual reality setups are particularly suited to create a tight bond between players and their avatars up to a degree where we start perceiving the virtual representation as our own body. We hypothesize that such an illusion of virtual body ownership (IVBO) has a particularly high, yet overlooked potential for nonhumanoid avatars. To validate our claim, we use the example of three very different creatures---a scorpion, a rhino, and a bird---to explore possible avatar controls and game mechanics based on specific animal abilities. A quantitative evaluation underpins the high game enjoyment arising from embodying such nonhuman morphologies, including additional body parts and obtaining respective superhuman skills, which allows us to derive a set of novel design implications. Furthermore, the experiment reveals a correlation between IVBO and game enjoyment, which is a further indication that nonhumanoid creatures offer a meaningful design space for VR games worth further investigation.
Edge Detection for Event Cameras using Intra-pixel-area Events
Venue: arXiv cs.RO 2019
Year: 2019

In this work, we propose an edge detection algorithm by estimating a lifetime of an event produced from dynamic vision sensor (DVS), also known as event camera. The event camera, unlike traditional CMOS camera, generates sparse event data at a pixel whose log-intensity changes. Due to this characteristic, theoretically, there is only one or no event at the specific time, which makes it difficult to grasp the world captured by the camera at a particular moment. In this work, we present an algorithm that keeps the event alive until the corresponding event is generated in a nearby pixel so that the shape of an edge is preserved. Particularly, we consider a pixel area to fit a plane on Surface of Active Events (SAE) and call the point inside the pixel area closest to the plane as a intra-pixel-area event. These intra-pixel-area events help the fitting plane algorithm to estimate life time robustly and precisely. Our algorithm performs better in terms of sharpness and similarity metric than the accumulation of events over fixed counts or time intervals, when compared with the existing edge detection algorithms, both qualitatively and quantitatively.
Interesting Open Problem Related to Complexity of Computing the Fourier Transform and Group Theory
Venue: arXiv cs.CC 2019
Year: 2019

The Fourier Transform is one of the most important linear transformations used in science and engineering. Cooley and Tukey's Fast Fourier Transform (FFT) from 1964 is a method for computing this transformation in time $O(n\log n)$. From a lower bound perspective, relatively little is known. Ailon shows in 2013 an $Ω(n\log n)$ bound for computing the normalized Fourier Transform assuming only unitary operations on pairs of coordinates is allowed. The goal of this document is to describe a natural open problem that arises from this work, which is related to group theory, and in particular to representation theory.
Robustness properties of Facebook's ResNeXt WSL models
Venue: arXiv cs.CV 2019
Year: 2019

We investigate the robustness properties of ResNeXt class image recognition models trained with billion scale weakly supervised data (ResNeXt WSL models). These models, recently made public by Facebook AI, were trained with ~1B images from Instagram and fine-tuned on ImageNet. We show that these models display an unprecedented degree of robustness against common image corruptions and perturbations, as measured by the ImageNet-C and ImageNet-P benchmarks. They also achieve substantially improved accuracies on the recently introduced "natural adversarial examples" benchmark (ImageNet-A). The largest of the released models, in particular, achieves state-of-the-art results on ImageNet-C, ImageNet-P, and ImageNet-A by a large margin. The gains on ImageNet-C, ImageNet-P, and ImageNet-A far outpace the gains on ImageNet validation accuracy, suggesting the former as more useful benchmarks to measure further progress in image recognition. Remarkably, the ResNeXt WSL models even achieve a limited degree of adversarial robustness against state-of-the-art white-box attacks (10-step PGD attacks). However, in contrast to adversarially trained models, the robustness of the ResNeXt WSL models rapidly declines with the number of PGD steps, suggesting that these models do not achieve genuine adversarial robustness. Visualization of the learned features also confirms this conclusion. Finally, we show that although the ResNeXt WSL models are more shape-biased than comparable ImageNet-trained models in a shape-texture cue conflict experiment, they still remain much more texture-biased than humans, suggesting that they share some of the underlying characteristics of ImageNet-trained models that make this benchmark challenging.
Revealing the Role of User Moods in Struggling Search Tasks
Venue: arXiv cs.HC 2019
Year: 2019

User-centered approaches have been extensively studied and used in the area of struggling search. Related research has targeted key aspects of users such as user satisfaction or frustration, and search success or failure, using a variety of experimental methods including laboratory user studies, in-situ explicit feedback from searchers and by using crowdsourcing. Such studies are valuable in advancing the understanding of search difficulty from a user's perspective, and yield insights that can directly improve search systems and their evaluation. However, little is known about how user moods influence their interactions with a search system or their perception of struggling. In this work, we show that a user's own mood can systematically bias the user's perception, and experience while interacting with a search system and trying to satisfy an information need. People who are in activated-pleasant / activated-unpleasant moods tend to issue more queries than people in deactivated or neutral moods. Those in an unpleasant mood perceive a higher level of difficulty. Our insights extend the current understanding of struggling search tasks and have important implications on the design and evaluation of search systems supporting such tasks.
Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC
Venue: arXiv cs.CV 2019
Year: 2019

Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption constrain the feasible hardware to embedded platforms such as FPGAs. Whilst various stereo algorithms have been deployed on these platforms, usually cut down to better match the embedded architecture, certain key parts of the more advanced algorithms, e.g. those that rely on unpredictable access to memory or are highly iterative in nature, are difficult to deploy efficiently on FPGAs, and thus the depth quality that can be achieved is limited. In this paper, we leverage a FPGA-CPU chip to propose a novel, sophisticated, stereo approach that combines the best features of SGM and ELAS-based methods to compute highly accurate dense depth in real time. Our approach achieves an 8.7% error rate on the challenging KITTI 2015 dataset at over 50 FPS, with a power consumption of only 5W.
HTCC: Haskell to Handel-C Compiler
Venue: arXiv cs.PL 2019
Year: 2019

Functional programming languages, such as Haskell, enable simple, concise, and correct-by-construction hardware development. HTCC compiles a subset of Haskell to Handel-C language with hardware output. Moreover, HTCC generates VHDL, Verilog, EDIF, and SystemC programs. The design of HTCC compiler includes lexical, syntax and semantic analyzers. HTCC automates a transformational derivation methodology to rapidly produce hardware that maps onto Field Programmable Gate Arrays (FPGAs) . HTCC is generated using ANTLR compiler-compiler tool and supports an effective integrated development environment. This paper presents the design rationale and the implementation of HTCC. Several sample generations of first-class and higher-order functions are presented. In-addition, a compilation case-study is presented for the XTEA cipher. The investigation comprises a thorough evaluation and performance analysis. The targeted FPGAs include Cyclone II, Stratix IV, and Virtex-6 from Altera and Xilinx.
Classification Schemas for Artificial Intelligence Failures
Venue: arXiv cs.CY 2019
Year: 2019

In this paper we examine historical failures of artificial intelligence (AI) and propose a classification scheme for categorizing future failures. By doing so we hope that (a) the responses to future failures can be improved through applying a systematic classification that can be used to simplify the choice of response and (b) future failures can be reduced through augmenting development lifecycles with targeted risk assessments.
Approximating Constraint Satisfaction Problems on High-Dimensional Expanders
Venue: arXiv cs.DS 2019
Year: 2019

We consider the problem of approximately solving constraint satisfaction problems with arity $k > 2$ ($k$-CSPs) on instances satisfying certain expansion properties, when viewed as hypergraphs. Random instances of $k$-CSPs, which are also highly expanding, are well-known to be hard to approximate using known algorithmic techniques (and are widely believed to be hard to approximate in polynomial time). However, we show that this is not necessarily the case for instances where the hypergraph is a high-dimensional expander. We consider the spectral definition of high-dimensional expansion used by Dinur and Kaufman [FOCS 2017] to construct certain primitives related to PCPs. They measure the expansion in terms of a parameter $γ$ which is the analogue of the second singular value for expanding graphs. Extending the results by Barak, Raghavendra and Steurer [FOCS 2011] for 2-CSPs, we show that if an instance of MAX k-CSP over alphabet $[q]$ is a high-dimensional expander with parameter $γ$, then it is possible to approximate the maximum fraction of satisfiable constraints up to an additive error $ε$ using $q^{O(k)} \cdot (k/ε)^{O(1)}$ levels of the sum-of-squares SDP hierarchy, provided $γ\leq ε^{O(1)} \cdot (1/(kq))^{O(k)}$. Based on our analysis, we also suggest a notion of threshold-rank for hypergraphs, which can be used to extend the results for approximating 2-CSPs on low threshold-rank graphs. We show that if an instance of MAX k-CSP has threshold rank $r$ for a threshold $τ= (ε/k)^{O(1)} \cdot (1/q)^{O(k)}$, then it is possible to approximately solve the instance up to additive error $ε$, using $r \cdot q^{O(k)} \cdot (k/ε)^{O(1)}$ levels of the sum-of-squares hierarchy. As in the case of graphs, high-dimensional expanders (with sufficiently small $γ$) have threshold rank 1 according to our definition.
Optimal Downlink-Uplink Scheduling of Wireless Networked Control for Industrial IoT
Venue: arXiv cs.IT 2019
Year: 2019

This paper considers a wireless networked control system (WNCS) consisting of a dynamic system to be controlled (i.e., a plant), a sensor, an actuator and a remote controller for mission-critical Industrial Internet of Things (IIoT) applications. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In this work, we consider a practical half-duplex controller, which introduces a novel transmission-scheduling problem for WNCSs. A frequent scheduling of sensor's transmission results in a better estimation of plant states at the controller and thus a higher quality of control command, but it leads to a less frequent/timely control of the plant. Therefore, considering the overall control performance of the plant in terms of its average cost function, there exists a fundamental tradeoff between the sensor's and the controller's transmissions. We formulate a new problem to optimize the transmission-scheduling policy for minimizing the long-term average cost function. We derive the necessary and sufficient condition of the existence of a stationary and deterministic optimal policy that results in a bounded average cost in terms of the transmission reliabilities of the sensor-to-controller and controller-to-actuator channels. Also, we derive an easy-to-compute suboptimal policy, which notably reduces the average cost of the plant compared to a naive alternative-scheduling policy.
Event-based Feature Extraction Using Adaptive Selection Thresholds
Venue: arXiv cs.NE 2019
Year: 2019

Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage.
Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video
Venue: arXiv cs.CV 2019
Year: 2019

Automatic instrument segmentation in video is an essentially fundamental yet challenging problem for robot-assisted minimally invasive surgery. In this paper, we propose a novel framework to leverage instrument motion information, by incorporating a derived temporal prior to an attention pyramid network for accurate segmentation. Our inferred prior can provide reliable indication of the instrument location and shape, which is propagated from the previous frame to the current frame according to inter-frame motion flow. This prior is injected to the middle of an encoder-decoder segmentation network as an initialization of a pyramid of attention modules, to explicitly guide segmentation output from coarse to fine. In this way, the temporal dynamics and the attention network can effectively complement and benefit each other. As additional usage, our temporal prior enables semi-supervised learning with periodically unlabeled video frames, simply by reverse execution. We extensively validate our method on the public 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset with three different tasks. Our method consistently exceeds the state-of-the-art results across all three tasks by a large margin. Our semi-supervised variant also demonstrates a promising potential for reducing annotation cost in the clinical practice.
A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images
Venue: arXiv cs.CV 2019
Year: 2019

We worked with Nestle SHIELD (Skin Health, Innovation, Education, and Longevity Development, NSH) to develop a deep learning model that is able to assess acne severity from selfie images as accurate as dermatologists. The model was deployed as a mobile application, providing patients an easy way to assess and track the progress of their acne treatment. NSH acquired 4,700 selfie images for this study and recruited 11 internal dermatologists to label them in five categories: 1-Clear, 2- Almost Clear, 3-Mild, 4-Moderate, 5-Severe. Using OpenCV to detect facial landmarks we cut specific skin patches from the selfie images in order to minimize irrelevant background. We then applied a transfer learning approach by extracting features from the patches using a ResNet 152 pre-trained model, followed by a fully connected layer trained to approximate the desired severity rating. To address the problem of spatial sensitivity of CNN models, we introduce a new image rolling data augmentation approach, effectively causing acne lesions appeared in more locations in the training images. Our results demonstrate that this approach improved the generalization of the CNN model, outperforming more than half of the panel of human dermatologists on test images. To our knowledge, this is the first deep learning-based solution for acne assessment using selfie images.
Priorities in tock-CSP
Venue: arXiv cs.LO 2019
Year: 2019

The $tock$-CSP encoding embeds a rich and flexible approach to modelling discrete timed behaviours in CSP where the event $tock$ is interpreted to mark the passage of time. The model checker FDR provides tailored support for $tock$-CSP, including a prioritisation operator that has typically been used to ensure maximal progress, where time only advances after internal activity has stabilised. Prioritisation may also be used on its own right as a modelling construct. Its operational semantics, however, is only congruent over the most discriminating semantic model of CSP: the finite-linear model. To enable sound and compositional reasoning in a $tock$-CSP setting, we calculate a denotational definition for prioritisation. For that we establish a Galois connection between a specialisation of the finite-linear model, with $tock$ and $\checkmark$, that signals termination, as special events, and $\checkmark$-$tock$-CSP, a model for $tock$-CSP that captures termination, deadlines, and is adequate for reasoning about timed refinement. Our results are mechanised using Isabelle/HOL.
Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach
Venue: arXiv cs.CV 2019
Year: 2019

Many road accidents occur due to distracted drivers. Today, driver monitoring is essential even for the latest autonomous vehicles to alert distracted drivers in order to take over control of the vehicle in case of emergency. In this paper, a spatio-temporal approach is applied to classify drivers' distraction level and movement decisions using convolutional neural networks (CNNs). We approach this problem as action recognition to benefit from temporal information in addition to spatial information. Our approach relies on features extracted from sparsely selected frames of an action using a pre-trained BN-Inception network. Experiments show that our approach outperforms the state-of-the art results on the Distracted Driver Dataset (96.31%), with an accuracy of 99.10% for 10-class classification while providing real-time performance. We also analyzed the impact of fusion using RGB and optical flow modalities with a very recent data level fusion strategy. The results on the Distracted Driver and Brain4Cars datasets show that fusion of these modalities further increases the accuracy.
A Differentially Private Algorithm for Range Queries on Trajectories
Venue: arXiv cs.DB 2019
Year: 2019

We propose a novel algorithm to ensure $ε$-differential privacy for answering range queries on trajectory data. In order to guarantee privacy, differential privacy mechanisms add noise to either data or query, thus introducing errors to queries made and potentially decreasing the utility of information. In contrast to the state-of-the-art, our method achieves significantly lower error as it is the first data- and query-aware approach for such queries. The key challenge for answering range queries on trajectory data privately is to ensure an accurate count. Simply representing a trajectory as a set instead of \emph{sequence} of points will generally lead to highly inaccurate query answers as it ignores the sequential dependency of location points in trajectories, i.e., will violate the consistency of trajectory data. Furthermore, trajectories are generally unevenly distributed across a city and adding noise uniformly will generally lead to a poor utility. To achieve differential privacy, our algorithm adaptively adds noise to the input data according to the given query set. It first privately partitions the data space into uniform regions and computes the traffic density of each region. The regions and their densities, in addition to the given query set, are then used to estimate the distribution of trajectories over the queried space, which ensures high accuracy for the given query set. We show the accuracy and efficiency of our algorithm using extensive empirical evaluations on real and synthetic data sets.