OpenRaiser/Intern-Atlas huggingface dataset:
Towards 5G: Joint Optimization of Video Segment Cache, Transcoding and Resource Allocation for Adaptive Video Streaming in a Muti-access Edge Computing Network
Venue: arXiv cs.MM 2020
Year: 2020

The cache and transcoding of the multi-access edge computing (MEC) server and wireless resource allocation in eNodeB interact and determine the quality of experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients in MEC networks. However, the relationship among the three factors has not been explored, which has led to limited improvement in clients' QoE. Therefore, we propose a joint optimization framework of video segment cache and transcoding in MEC servers and resource allocation to improve the QoE of DASH clients. Based on the established framework, we develop a MEC cache management mechanism that consists of the MEC cache partition, video segment deletion, and MEC cache space transfer. Then, a joint optimization algorithm that combines video segment cache and transcoding in the MEC server and resource allocation is proposed. In the algorithm, the clients' channel state and the playback status and cooperation among MEC servers are employed to estimate the client's priority, video segment presentation switch and continuous playback time. Considering the above four factors, we develop a utility function model of clients' QoE. Then, we formulate a mixed-integer nonlinear programming mathematical model to maximize the total utility of DASH clients, where the video segment cache and transcoding strategy and resource allocation strategy are jointly optimized. To solve this problem, we propose a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms efficiently improve client's throughput, received video quality and hit ratio of video segments while decreasing the playback rebuffering time, video segment presentation switch and system backhaul traffic.
Outage Minimization for Intelligent Reflecting Surface Aided MISO Communication Systems via Stochastic Beamforming
Venue: arXiv cs.IT 2020
Year: 2020

Intelligent reflecting surface (IRS) has the potential to significantly enhance the network performance by reconfiguring the wireless propagation environments. It is however difficult to obtain the accurate downlink channel state information (CSI) for efficient beamforming design in IRS-aided wireless networks. In this article, we consider an IRS-aided downlink multiple-input single-output (MISO) network, where the base station (BS) is not required to know the underlying channel distribution. We formulate an outage probability minimization problem by jointly optimizing the beamforming vector at the BS and the phase-shift matrix at the IRS, while taking into account the transmit power and unimodular constraints. The formulated problem turns out to be a non-convex non-smooth stochastic optimization problem. To this end, we employ the sigmoid function as the surrogate to tackle the non-smoothness of the objective function. In addition, we propose a data-driven efficient alternating stochastic gradient descent (SGD) algorithm to solve the problem by utilizing the historical channel samples. Simulation results demonstrate the performance gains of the proposed algorithm over the benchmark methods in terms of minimizing the outage probability.
Spelling Error Correction with Soft-Masked BERT
Venue: arXiv cs.CL 2020
Year: 2020

Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-of-the-art method for the task selects a character from a list of candidates for correction (including non-correction) at each position of the sentence on the basis of BERT, the language representation model. The accuracy of the method can be sub-optimal, however, because BERT does not have sufficient capability to detect whether there is an error at each position, apparently due to the way of pre-training it using mask language modeling. In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique. Our method of using `Soft-Masked BERT' is general, and it may be employed in other language detection-correction problems. Experimental results on two datasets demonstrate that the performance of our proposed method is significantly better than the baselines including the one solely based on BERT.
Phase Transition of a Non-Linear Opinion Dynamics with Noisy Interactions
Venue: arXiv cs.DC 2020
Year: 2020

In several real \emph{Multi-Agent Systems} (MAS), it has been observed that only weaker forms of\emph{metastable consensus} are achieved, in which a large majority of agents agree on some opinion while other opinions continue to be supported by a (small) minority of agents. In this work, we take a step towards the investigation of metastable consensus for complex (non-linear) \emph{opinion dynamics} by considering the famous \undecided dynamics in the binary setting, which is known to reach consensus exponentially faster than the \voter dynamics. We propose a simple form of uniform noise in which each message can change to another one with probability $p$ and we prove that the persistence of a \emph{metastable consensus} undergoes a \emph{phase transition} for $p=\frac 16$. In detail, below this threshold, we prove the system reaches with high probability a metastable regime where a large majority of agents keeps supporting the same opinion for polynomial time. Moreover, this opinion turns out to be the initial majority opinion, whenever the initial bias is slightly larger than its standard deviation.On the contrary, above the threshold, we show that the information about the initial majority opinion is "lost" within logarithmic time even when the initial bias is maximum.Interestingly, using a simple coupling argument, we show the equivalence between our noisy model above and the model where a subset of agents behave in a \emph{stubborn} way.
Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension
Venue: arXiv cs.CV 2020
Year: 2020

Electric vhicles and autonomous driving dominate current research efforts in the automotive sector. The two topics go hand in hand in terms of enabling safer and more environmentally friendly driving. One fundamental building block of an autonomous vehicle is the ability to build a map of the environment and localize itself on such a map. In this paper, we make use of a stereo camera sensor in order to perceive the environment and create the map. With live Simultaneous Localization and Mapping (SLAM), there is a risk of mislocalization, since no ground truth map is used as a reference and errors accumulate over time. Therefore, we first build up and save a map of visual features of the environment at low driving speeds with our extension to the ORB-SLAM\,2 package. In a second run, we reload the map and then localize on the previously built-up map. Loading and localizing on a previously built map can improve the continuous localization accuracy for autonomous vehicles in comparison to a full SLAM. This map saving feature is missing in the original ORB-SLAM\,2 implementation. We evaluate the localization accuracy for scenes of the KITTI dataset against the built up SLAM map. Furthermore, we test the localization on data recorded with our own small scale electric model car. We show that the relative translation error of the localization stays under 1\% for a vehicle travelling at an average longitudinal speed of 36 m/s in a feature-rich environment. The localization mode contributes to a better localization accuracy and lower computational load compared to a full SLAM. The source code of our contribution to the ORB-SLAM2 will be made public at: https://github.com/TUMFTM/orbslam-map-saving-extension.
Binary Hypothesis Testing with Deterministic Finite-Memory Decision Rules
Venue: arXiv cs.IT 2020
Year: 2020

In this paper we consider the problem of binary hypothesis testing with finite memory systems. Let $X_1,X_2,\ldots$ be a sequence of independent identically distributed Bernoulli random variables, with expectation $p$ under $\mathcal{H}_0$ and $q$ under $\mathcal{H}_1$. Consider a finite-memory deterministic machine with $S$ states that updates its state $M_n \in \{1,2,\ldots,S\}$ at each time according to the rule $M_n = f(M_{n-1},X_n)$, where $f$ is a deterministic time-invariant function. Assume that we let the process run for a very long time ($n\rightarrow \infty)$, and then make our decision according to some mapping from the state space to the hypothesis space. The main contribution of this paper is a lower bound on the Bayes error probability $P_e$ of any such machine. In particular, our findings show that the ratio between the maximal exponential decay rate of $P_e$ with $S$ for a deterministic machine and for a randomized one, can become unbounded, complementing a result by Hellman.
Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Convex Shapes (CS) are common priors for optic disc and cup segmentation in eye fundus images. It is important to design proper techniques to represent convex shapes. So far, it is still a problem to guarantee that the output objects from a Deep Neural Convolution Networks (DCNN) are convex shapes. In this work, we propose a technique which can be easily integrated into the commonly used DCNNs for image segmentation and guarantee that outputs are convex shapes. This method is flexible and it can handle multiple objects and allow some of the objects to be convex. Our method is based on the dual representation of the sigmoid activation function in DCNNs. In the dual space, the convex shape prior can be guaranteed by a simple quadratic constraint on a binary representation of the shapes. Moreover, our method can also integrate spatial regularization and some other shape prior using a soft thresholding dynamics (STD) method. The regularization can make the boundary curves of the segmentation objects to be simultaneously smooth and convex. We design a very stable active set projection algorithm to numerically solve our model. This algorithm can form a new plug-and-play DCNN layer called CS-STD whose outputs must be a nearly binary segmentation of convex objects. In the CS-STD block, the convexity information can be propagated to guide the DCNN in both forward and backward propagation during training and prediction process. As an application example, we apply the convexity prior layer to the retinal fundus images segmentation by taking the popular DeepLabV3+ as a backbone network. Experimental results on several public datasets show that our method is efficient and outperforms the classical DCNN segmentation methods.
On The Capacity of Gaussian MIMO Channels Under Interference Constraints (full version)
Venue: arXiv cs.IT 2020
Year: 2020

Gaussian MIMO channel under total transmit and multiple interference power constraints (TPC and IPCs) is considered. A closed-form solution for its optimal transmit covariance matrix is obtained in the general case (up to dual variables). A number of more explicit closed-form solutions are obtained in some special cases, including full-rank and rank-1 (beamforming) solutions, which differ significantly from the well-known water-filling solutions (e.g. signaling on the channel eigenmodes is not optimal anymore and the capacity can be zero for non-zero transmit power). A whitening filter is shown to be an important part of optimal precoding under interference constraints. Capacity scaling with transmit power is studied: its qualitative behaviour is determined by a natural linear-algebraic structure induced by MIMO channels of multiple users. A simple rank condition is given to characterize the cases where spectrum sharing is possible. An interplay between the TPC and IPCs is investigated, including the transition from power-limited to interference-limited regimes. A number of unusual properties of an optimal covariance matrix under IPCs are pointed out and a bound on its rank is established. Partial null forming known in the adaptive antenna array literature is shown to be optimal from the information-theoretic perspective as well in some cases.
Convolutional neural networks for classification and regression analysis of one-dimensional spectral data
Venue: arXiv cs.LG 2020
Year: 2020

Convolutional neural networks (CNNs) are widely used for image recognition and text analysis, and have been suggested for application on one-dimensional data as a way to reduce the need for pre-processing steps. Pre-processing is an integral part of multivariate analysis, but determination of the optimal pre-processing methods can be time-consuming due to the large number of available methods. In this work, the performance of a CNN was investigated for classification and regression analysis of spectral data. The CNN was compared with various other chemometric methods, including support vector machines (SVMs) for classification and partial least squares regression (PLSR) for regression analysis. The comparisons were made both on raw data, and on data that had gone through pre-processing and/or feature selection methods. The models were used on spectral data acquired with methods based on near-infrared, mid-infrared, and Raman spectroscopy. For the classification datasets the models were evaluated based on the percentage of correctly classified observations, while for regression analysis the models were assessed based on the coefficient of determination (R$^2$). Our results show that CNNs can outperform standard chemometric methods, especially for classification tasks where no pre-processing is used. However, both CNN and the standard chemometric methods see improved performance when proper pre-processing and feature selection methods are used. These results demonstrate some of the capabilities and limitations of CNNs used on one-dimensional data.
6G Communication Technology: A Vision on Intelligent Healthcare
Venue: arXiv cs.NI 2020
Year: 2020

6G is a promising communication technology that will dominate the entire health market from 2030 onward. It will dominate not only health sector but also diverse sectors. It is expected that 6G will revolutionize many sectors including healthcare. Healthcare will be fully AI-driven and dependent on 6G communication technology, which will change our perception of lifestyle. Currently, time and space are the key barriers to health care and 6G will be able to overcome these barriers. Also, 6G will be proven as a game changing technology for healthcare. Therefore, in this perspective, we envision healthcare system for the era of 6G communication technology. Also, various new methodologies have to be introduced to enhance our lifestyle, which is addressed in this perspective, including Quality of Life (QoL), Intelligent Wearable Devices (IWD), Intelligent Internet of Medical Things (IIoMT), Hospital-to-Home (H2H) services, and new business model. In addition, we expose the role of 6G communication technology in telesurgery, Epidemic and Pandemic.
Simple Sensor Intentions for Exploration
Venue: arXiv cs.LG 2020
Year: 2020

Modern reinforcement learning algorithms can learn solutions to increasingly difficult control problems while at the same time reduce the amount of prior knowledge needed for their application. One of the remaining challenges is the definition of reward schemes that appropriately facilitate exploration without biasing the solution in undesirable ways, and that can be implemented on real robotic systems without expensive instrumentation. In this paper we focus on a setting in which goal tasks are defined via simple sparse rewards, and exploration is facilitated via agent-internal auxiliary tasks. We introduce the idea of simple sensor intentions (SSIs) as a generic way to define auxiliary tasks. SSIs reduce the amount of prior knowledge that is required to define suitable rewards. They can further be computed directly from raw sensor streams and thus do not require expensive and possibly brittle state estimation on real systems. We demonstrate that a learning system based on these rewards can solve complex robotic tasks in simulation and in real world settings. In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.
Elastic execution of checkpointed MPI applications
Venue: arXiv cs.DC 2020
Year: 2020

MPI applications begin with a fixed number of rank and, by default, the rank remains constant throughout the application's lifetime. The developer can choose to increase the rank by dynamically spawning MPI processes. However doing this manually adds complexity to the MPI application. Making the MPI applications malleable \cite{b20} would allow HPC applications to have the same elasticity as that of cloud applications. We propose multiple approaches to change the rank of an MPI program agnostic to the modification of the user code. We use checkpointing as a tool to achieve mutability of rank by halting the execution and resuming the MPI program with a new state. In this paper, we focus on the scenario of increasing the rank of an MPI program using ExaMPI as the implementation for MPI.
Referencing Sources of Molecular Spectroscopic Data in the Era of Data Science: Application to the HITRAN and AMBDAS Databases
Venue: arXiv cs.DL 2020
Year: 2020

The application described has been designed to create bibliographic entries in large databases with diverse sources automatically, which reduces both the frequency of mistakes and the workload for the administrators. This new system uniquely identifies each reference from its digital object identifier (DOI) and retrieves the corresponding bibliographic information from any of several online services, including the SAO/NASA Astrophysics Data Systems (ADS) and CrossRef APIs. Once parsed into a relational database, the software is able to produce bibliographies in any of several formats, including HTML and BibTeX, for use on websites or printed articles. The application is provided free-of-charge for general use by any scientific database. The power of this application is demonstrated when used to populate reference data for the HITRAN and AMBDAS databases as test cases. HITRAN contains data that is provided by researchers and collaborators throughout the spectroscopic community. These contributors are accredited for their contributions through the bibliography produced alongside the data returned by an online search in HITRAN. Prior to the work presented here, HITRAN and AMBDAS created these bibliographies manually, which is a tedious, time-consuming and error-prone process. The complete code for the new referencing system can be found at \url{https://github.com/hitranonline/refs}.
Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline
Venue: arXiv cs.CL 2020
Year: 2020

We propose the task of emotion style transfer, which is particularly challenging, as emotions (here: anger, disgust, fear, joy, sadness, surprise) are on the fence between content and style. To understand the particular difficulties of this task, we design a transparent emotion style transfer pipeline based on three steps: (1) select the words that are promising to be substituted to change the emotion (with a brute-force approach and selection based on the attention mechanism of an emotion classifier), (2) find sets of words as candidates for substituting the words (based on lexical and distributional semantics), and (3) select the most promising combination of substitutions with an objective function which consists of components for content (based on BERT sentence embeddings), emotion (based on an emotion classifier), and fluency (based on a neural language model). This comparably straight-forward setup enables us to explore the task and understand in what cases lexical substitution can vary the emotional load of texts, how changes in content and style interact and if they are at odds. We further evaluate our pipeline quantitatively in an automated and an annotation study based on Tweets and find, indeed, that simultaneous adjustments of content and emotion are conflicting objectives: as we show in a qualitative analysis motivated by Scherer's emotion component model, this is particularly the case for implicit emotion expressions based on cognitive appraisal or descriptions of bodily reactions.
DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling
Venue: arXiv cs.AI 2020
Year: 2020

In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity, HEFT-like algorithms are known to be vulnerable to a small amount of noise added to the environment. Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the "best" task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs. We~describe a DeepSoCS design process using a real-time heterogeneous SoC scheduling emulator, discuss major challenges, and present two novel neural network design features that lead to outperforming HEFT: (i) hierarchical job- and task-graph embedding; and (ii) efficient use of real-time task information in the state space. Furthermore, we~introduce effective techniques to address two fundamental challenges present in our environment: delayed consequences and joint actions. Through an extensive simulation study, we~show that our DeepSoCS exhibits the significantly higher performance of job execution time than that of HEFT with a higher level of robustness under realistic noise conditions. We~conclude with a discussion of the potential improvements for our DeepSoCS neural scheduler.