OpenRaiser/Intern-Atlas huggingface dataset:
Comprehensive Multiparty Session Types
Venue: arXiv cs.PL 2019
Year: 2019

Multiparty session types (MST) are a well-established type theory that describes the interactive structure of a fixed number of components from a global point of view and type-checks the components through projection of the global type onto the participants of the session. They guarantee communicationsafety for a language of multiparty sessions (LMS), i.e., distributed, parallel components can exchange values without deadlocking and unexpected message types. Several variants of MST and LMS have been proposed to study key features of distributed and parallel programming. We observe that the population of the considered variants follows from only one ancestor, i.e., the original LMS/MST, and there are overlapping traits between features of the considered variants and the original. These hamper evolution of session types and languages and their adoption in practice. This paper addresses the following question: What are the essential features for MST and LMS, and how can these be modelled with simple constructs? To the best of our knowledge, this is the first time this question has been addressed. We performed a systematic analysis of the features and the constructs in MST, LMS, and the considered variants to identify the essential features. The variants are among the most influential (according to Google Scholar) and well-established systems that cover a wide set of areas in distributed, parallel programming. We used classical techniques of formal models such as BNF, structural congruence, small step operational semantics and typing judgments to build our language and type system. Lastly, the coherence of operational semantics and type system is proven by induction. This paper proposes a set of essential features, a language of structured interactions and a type theory of comprehensive multiparty session types, including global types and type system. The analysis removes overlapping features and captures the shared traits, thereby introducing the essential features. The constructs of the language are simple and fundamental, based on the $λ$ and $π$ calculi. Analogously, our global types reflect what is omitted and introduced in the language. Our system covers all the features of the original and variants, with a better ratio of the number of language and type constructs over the number of covered features. The features of the original, variants, and our system along with the number of constructs in the respective language and global types to model them are presented through a table. The syntax, operational semantics, meta-theory and type system of our system are given. We modelled all the motivating examples of the variants in our model, describing the reduction and typing steps. The work discusses how new features, in particular the non-essential ones (formerly excluded) and advanced ones can be either modelled atop the essential ones or added with minimal efforts, i.e. without modifying the existing ones. The fundamental properties of typed processes such as subject reduction, communication safety, and progress are established.
Separating Use and Reuse to Improve Both
Venue: arXiv cs.PL 2019
Year: 2019

Context: Trait composition has inspired new research in the area of code reuse for object oriented (OO) languages. One of the main advantages of this kind of composition is that it makes possible to separate subtyping from subclassing; which is good for code-reuse, design and reasoning. However, handling of state within traits is difficult, verbose or inelegant. Inquiry: We identify the this-leaking problem as the fundamental limitation that prevents the separation of subtyping from subclassing in conventional OO languages. We explain that the concept of trait composition addresses this problem, by distinguishing code designed for use (as a type) from code designed for reuse (i.e. inherited). We are aware of at least 3 concrete independently designed research languages following this methodology: TraitRecordJ, Package Templates and DeepFJig. Approach: In this paper, we design $42_μ$ a new language, where we improve use and reuse and support the This type and family polymorphism by distinguishing code designed for use from code designed for reuse. In this way $42_μ$ synthesise the 3 approaches above, and improves them with abstract state operations: a new elegant way to handle state composition in trait based languages. Knowledge and Grounding: Using case studies, we show that $42_μ$'s model of traits with abstract state operations is more usable and compact than prior work. We formalise our work and prove that type errors cannot arise from composing well typed code. Importance: This work is the logical core of the programming language 42. This shows that the ideas presented in this paper can be applicable to a full general purpose language. This form of composition is very flexible and could be used in many new languages.
Towards Disaggregating the SDN Control Plane
Venue: arXiv cs.NI 2019
Year: 2019

Current SDN controllers have been designed based on a monolithic approach that integrates all of services and applications into one single, huge program. The monolithic design of SDN controllers restricts programmers who build management applications to the specific programming interfaces and services that a given SDN controller provides, making application development dependent on the controller, and thereby restricting portability of management applications across controllers. Furthermore, the monolithic approach means an SDN controller must be recompiled whenever a change is made, and does not provide an easy way to add new functionality or scale to handle large networks. To overcome the weaknesses inherent in the monolithic approach, the next generation of SDN controllers must use a distributed, microservice architecture that disaggregates the control plane by dividing the monolithic controller into a set of cooperative microservices. In this paper, we explain the steps that are required to migrate from a monolithic to a microservice architecture and propose two potential architectures to achieve the goal. Finally, the paper reports the results of testbed measurements that we use to evaluate the proposed disaggregated architecture from multiple perspectives, including functionality and performance.
A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories
Venue: arXiv cs.RO 2019
Year: 2019

The problem of maneuvering a vehicle through a race course in minimum time requires computation of both longitudinal (brake and throttle) and lateral (steering wheel) control inputs. Unfortunately, solving the resulting nonlinear optimal control problem is typically computationally expensive and infeasible for real-time trajectory planning. This paper presents an iterative algorithm that divides the path generation task into two sequential subproblems that are significantly easier to solve. Given an initial path through the race track, the algorithm runs a forward-backward integration scheme to determine the minimum-time longitudinal speed profile, subject to tire friction constraints. With this fixed speed profile, the algorithm updates the vehicle's path by solving a convex optimization problem that minimizes the resulting path curvature while staying within track boundaries and obeying affine, time-varying vehicle dynamics constraints. This two-step process is repeated iteratively until the predicted lap time no longer improves. While providing no guarantees of convergence or a globally optimal solution, the approach performs very well when validated on the Thunderhill Raceway course in Willows, CA. The predicted lap time converges after four to five iterations, with each iteration over the full 4.5 km race course requiring only thirty seconds of computation time on a laptop computer. The resulting trajectory is experimentally driven at the race circuit with an autonomous Audi TTS test vehicle, and the resulting lap time and racing line is comparable to both a nonlinear gradient descent solution and a trajectory recorded from a professional racecar driver. The experimental results indicate that the proposed method is a viable option for online trajectory planning in the near future.
Path Tracking of Highly Dynamic Autonomous Vehicle Trajectories via Iterative Learning Control
Venue: arXiv cs.RO 2019
Year: 2019

Iterative learning control has been successfully used for several decades to improve the performance of control systems that perform a single repeated task. Using information from prior control executions, learning controllers gradually determine open-loop control inputs whose reference tracking performance can exceed that of traditional feedback-feedforward control algorithms. This paper considers iterative learning control for a previously unexplored field: autonomous racing. Racecars are driven multiple laps around the same sequence of turns while operating near the physical limits of tire-road friction, where steering dynamics become highly nonlinear and transient, making accurate path tracking difficult. However, because the vehicle trajectory is identical for each lap in the case of single-car racing, the nonlinear vehicle dynamics and unmodelled road conditions are repeatable and can be accounted for using iterative learning control, provided the tire force limits have not been exceeded. This paper describes the design and application of proportional-derivative (PD) and quadratically optimal (Q-ILC) learning algorithms for multiple-lap path tracking of an autonomous race vehicle. Simulation results are used to tune controller gains and test convergence, and experimental results are presented on an Audi TTS race vehicle driving several laps around Thunderhill Raceway in Willows, CA at lateral accelerations of up to 8 $\mathrm{m/s^2}$. Both control algorithms are able to correct transient path tracking errors and improve the performance provided by a reference feedforward controller.
Computational Complexity of Queries Based on Itemsets
Venue: arXiv cs.CC 2019
Year: 2019

We investigate determining the exact bounds of the frequencies of conjunctions based on frequent sets. Our scenario is an important special case of some general probabilistic logic problems that are known to be intractable. We show that despite the limitations our problems are also intractable, namely, we show that checking whether the maximal consistent frequency of a query is larger than a given threshold is NP-complete and that evaluating the Maximum Entropy estimate of a query is PP-hard. We also prove that checking consistency is NP-complete.
Multiuser Video Streaming Rate Adaptation: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach
Venue: arXiv cs.MM 2019
Year: 2019

We consider a multi-user video streaming service optimization problem over a time-varying and mutually interfering multi-cell wireless network. The key research challenge is to appropriately adapt each user's video streaming rate according to the radio frequency environment (e.g., channel fading and interference level) and service demands (e.g., play request), so that the users' long-term experience for watching videos can be optimized. To address the above challenge, we propose a novel two-level cross-layer optimization framework for multiuser adaptive video streaming over wireless networks. The key idea is to jointly design the physical layer optimization-based beamforming scheme (performed at the base stations) and the application layer Deep Reinforcement Learning (DRL)-based scheme (performed at the user terminals), so that a highly complex multi-user, cross-layer, time-varying video streaming problem can be decomposed into relatively simple problems and solved effectively. Our strategy represents a significant departure for the existing schemes where either short-term user experience optimization is considered, or only single-user point-to-point long-term optimization is considered. Extensive simulations based on real-data sets show that the proposed cross-layer design is effective and promising.
FurcaNet: An end-to-end deep gated convolutional, long short-term memory, deep neural networks for single channel speech separation
Venue: arXiv cs.SD 2019
Year: 2019

Deep gated convolutional networks have been proved to be very effective in single channel speech separation. However current state-of-the-art framework often considers training the gated convolutional networks in time-frequency (TF) domain. Such an approach will result in limited perceptual score, such as signal-to-distortion ratio (SDR) upper bound of separated utterances and also fail to exploit an end-to-end framework. In this paper we present an integrated simple and effective end-to-end approach to monaural speech separation, which consists of deep gated convolutional neural networks (GCNN) that takes the mixed utterance of two speakers and maps it to two separated utterances, where each utterance contains only one speaker's voice. In addition long short-term memory (LSTM) is employed for long term temporal modeling. For the objective, we propose to train the network by directly optimizing utterance level SDR in a permutation invariant training (PIT) style. Our experiments on the public WSJ0-2mix data corpus demonstrate that this new scheme can produce more discriminative separated utterances and leading to performance improvement on the speaker separation task.
Natural Language Processing, Sentiment Analysis and Clinical Analytics
Venue: arXiv cs.CL 2019
Year: 2019

Recent advances in Big Data has prompted health care practitioners to utilize the data available on social media to discern sentiment and emotions expression. Health Informatics and Clinical Analytics depend heavily on information gathered from diverse sources. Traditionally, a healthcare practitioner will ask a patient to fill out a questionnaire that will form the basis of diagnosing the medical condition. However, medical practitioners have access to many sources of data including the patients writings on various media. Natural Language Processing (NLP) allows researchers to gather such data and analyze it to glean the underlying meaning of such writings. The field of sentiment analysis (applied to many other domains) depend heavily on techniques utilized by NLP. This work will look into various prevalent theories underlying the NLP field and how they can be leveraged to gather users sentiments on social media. Such sentiments can be culled over a period of time thus minimizing the errors introduced by data input and other stressors. Furthermore, we look at some applications of sentiment analysis and application of NLP to mental health. The reader will also learn about the NLTK toolkit that implements various NLP theories and how they can make the data scavenging process a lot easier.
Transmission strategies for cell-free massive mimo with limited capacity fronthaul links
Venue: arXiv cs.IT 2019
Year: 2019

We study an uplink scenario of a cell-free massive multiple-input multiple-output (CF-mMIMO) system with limited capacity fronthaul links (LC-FHLs) connecting each access point (AP) to the central unit (CU), where user equipments and APs are subject to hardware impairments. Therefore, to efficiently use the capacity of FHLs to maximize the achievable rate, we analyze three strategies for performing compression and forwarding of channel state information (CSI) and data signals over the LC-FHLs to the CU; Compress-forward-estimate (CFE), estimate-compress-forward (ECF), and estimate-multiply-compress-forward (EMCF). For CFE and EMCF achievable rates are derived, and for ECF one upper and lower bounds are presented which are tight for ideal hardware and FHLs. Also for forwarding the quantized version of CSI and data signals of each user, low-complexity fronthaul capacity allocations are proposed for ECF and EMCF strategies, which considerably improve the performance of the system, especially for limited capacity FHLs. Our results indicate that at high SNR regime and for large enough capacity of FHLs, estimating channels at the CU rather than APs result in smaller estimation error. Then, geometric programming power allocations are developed for CFE and ECF to maximize sum rates. Finally, to highlight the performance characteristics of the system numerical results are presented.
Making a Case for Social Media Corpus for Detecting Depression
Venue: arXiv cs.CL 2019
Year: 2019

The social media platform provides an opportunity to gain valuable insights into user behaviour. Users mimic their internal feelings and emotions in a disinhibited fashion using natural language. Techniques in Natural Language Processing have helped researchers decipher standard documents and cull together inferences from massive amount of data. A representative corpus is a prerequisite for NLP and one of the challenges we face today is the non-standard and noisy language that exists on the internet. Our work focuses on building a corpus from social media that is focused on detecting mental illness. We use depression as a case study and demonstrate the effectiveness of using such a corpus for helping practitioners detect such cases. Our results show a high correlation between our Social Media Corpus and the standard corpus for depression.
Self-Binarizing Networks
Venue: arXiv cs.CV 2019
Year: 2019

We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary. To obtain similar binary networks, existing methods rely on the sign activation function. This function, however, has no gradients for non-zero values, which makes standard backpropagation impossible. To circumvent the difficulty of training a network relying on the sign activation function, these methods alternate between floating-point and binary representations of the network during training, which is sub-optimal and inefficient. We approach the binarization task by training on a unique representation involving a smooth activation function, which is iteratively sharpened during training until it becomes a binary representation equivalent to the sign activation function. Additionally, we introduce a new technique to perform binary batch normalization that simplifies the conventional batch normalization by transforming it into a simple comparison operation. This is unlike existing methods, which are forced to the retain the conventional floating-point-based batch normalization. Our binary networks, apart from displaying advantages of lower memory and computation as compared to conventional floating-point and binary networks, also show higher classification accuracy than existing state-of-the-art methods on multiple benchmark datasets.
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image. Our approach draws inspiration from related work on super-resolution and in-painting. We propose a novel architecture that seeks to pull contextual cues separately from the intensity image and the depth features and then fuse them later in the network. We argue that this approach effectively exploits the relationship between the two modalities and produces accurate results while respecting salient image structures. We present experimental results to demonstrate that our approach is comparable with state of the art methods and generalizes well across multiple datasets.
Phoenix: An Epidemic Approach to Time Reconstruction
Venue: arXiv cs.NI 2019
Year: 2019

Harsh deployment environments and uncertain run-time conditions create numerous challenges for postmortem time reconstruction methods. For example, motes often reboot and thus lose their clock state, considering that the majority of mote platforms lack a real-time clock. While existing time reconstruction methods for long-term data gathering networks rely on a persistent basestation for assigning global timestamps to measurements, the basestation may be unavailable due to hardware and software faults. We present Phoenix, a novel offline algorithm for reconstructing global timestamps that is robust to frequent mote reboots and does not require a persistent global time source. This independence sets Phoenix apart from the majority of time reconstruction algorithms which assume that such a source is always available. Motes in Phoenix exchange their time-related state with their neighbors, establishing a chain of transitive temporal relationships to one or more motes with references to the global time. These relationships allow Phoenix to reconstruct the measurement timeline for each mote. Results from simulations and a deployment indicate that Phoenix can achieve timing accuracy up to 6 ppm for 99% of the collected measurements. Phoenix is able to maintain this performance for periods that last for months without a persistent global time source. To achieve this level of performance for the targeted environmental monitoring application, Phoenix requires an additional space overhead of 4% and an additional duty cycle of 0.2%.
DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabilistic background model (DeepPBM) estimation approach is enabled by the power of deep neural networks in learning compressed representations of video frames and reconstructing them back to the original domain. We evaluated the performance of our DeepPBM in background subtraction on 9 surveillance videos from the background model challenge (BMC2012) dataset, and compared that with a standard subspace learning technique, robust principle component analysis (RPCA), which similarly estimates a deterministic low dimensional representation of the background in videos and is widely used for this application. Our method outperforms RPCA on BMC2012 dataset with 23% in average in F-measure score, emphasizing that background subtraction using the trained model can be done in more than 10 times faster.
Night Time Haze and Glow Removal using Deep Dilated Convolutional Network
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we address the single image haze removal problem in a nighttime scene. The night haze removal is a severely ill-posed problem especially due to the presence of various visible light sources with varying colors and non-uniform illumination. These light sources are of different shapes and introduce noticeable glow in night scenes. To address these effects we introduce a deep learning based DeGlow-DeHaze iterative architecture which accounts for varying color illumination and glows. First, our convolution neural network (CNN) based DeGlow model is able to remove the glow effect significantly and on top of it a separate DeHaze network is included to remove the haze effect. For our recurrent network training, the hazy images and the corresponding transmission maps are synthesized from the NYU depth datasets and consequently restored a high-quality haze-free image. The experimental results demonstrate that our hybrid CNN model outperforms other state-of-the-art methods in terms of computation speed and image quality. We also show the effectiveness of our model on a number of real images and compare our results with the existing night haze heuristic models.