OpenRaiser/Intern-Atlas huggingface dataset:
A Joint Planning and Learning Framework for Human-Aided Decision-Making
Venue: arXiv cs.AI 2019
Year: 2019

Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve, especially at the beginning stage. On the contrary, human learning is usually much faster because prior and general knowledge and multiple information resources are utilized. In this paper, we propose a \textbf{P}lanner-\textbf{A}ctor-\textbf{C}ritic architecture for hu\textbf{MAN}-centered planning and learning (\textbf{PACMAN}), where an agent uses prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions. PACMAN integrates Actor-Critic algorithm of RL to fine-tune its behavior towards both environmental rewards and human feedback. To the best our knowledge, This is the first unified framework where knowledge-based planning, RL, and human teaching jointly contribute to the policy learning of an agent. Our experiments demonstrate that PACMAN leads to a significant jump-start at the early stage of learning, converges rapidly and with small variance, and is robust to inconsistent, infrequent, and misleading feedback.
Whole-Body Control with (Self) Collision Avoidance using Vector Field Inequalities
Venue: arXiv cs.RO 2019
Year: 2019

This work uses vector field inequalities (VFI) to prevent robot self-collisions and collisions with the workspace. Differently from previous approaches, the method is suitable for both velocity and torque-actuated robots. We propose a new distance function and its corresponding Jacobian in order to generate a VFI to limit the angle between two Plücker lines. This new VFI is used to prevent both undesired end-effector orientations and violation of joints limits. The proposed method is evaluated in a realistic simulation and on a real humanoid robot, showing that all constraints are respected while the robot performs a manipulation task.
Measuring Bias in Contextualized Word Representations
Venue: arXiv cs.CL 2019
Year: 2019

Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks. Since they are optimized to capture the statistical properties of training data, they tend to pick up on and amplify social stereotypes present in the data as well. In this study, we (1)~propose a template-based method to quantify bias in BERT; (2)~show that this method obtains more consistent results in capturing social biases than the traditional cosine based method; and (3)~conduct a case study, evaluating gender bias in a downstream task of Gender Pronoun Resolution. Although our case study focuses on gender bias, the proposed technique is generalizable to unveiling other biases, including in multiclass settings, such as racial and religious biases.
A Performance Perspective on Web Optimized Protocol Stacks: TCP+TLS+HTTP/2 vs. QUIC
Venue: arXiv cs.NI 2019
Year: 2019

Existing performance comparisons of QUIC and TCP compared an optimized QUIC to an unoptimized TCP stack. By neglecting available TCP improvements inherently included in QUIC, comparisons do not shed light on the performance of current web stacks. In this paper, we can show that tuning TCP parameters is not negligible and directly yields significant improvements. Nevertheless, QUIC still outperforms even our tuned variant of TCP. This performance advantage is mostly caused by QUIC's reduced RTT design during connection establishment, and, in case of lossy networks due to its ability to circumvent head-of-line blocking.
Options of Different Rescue Periods on Transport Tools
Venue: arXiv cs.CY 2019
Year: 2019

In this paper, we construct a universal model to study the search and rescue work in lost planes. We establish an evaluation and decision model for traffic rescue tools. According to the characteristics of different periods, we utilize the combination of Analytic Hierarchy Process (AHP) and Fuzzy Synthetic Evaluation (FSE) to assess the capability of rescue tools in different period. Then, combined with the actual situation, determine the selection of rescue tools in different periods.
Impoved RPN for Single Targets Detection based on the Anchor Mask Net
Venue: arXiv cs.CV 2019
Year: 2019

Common target detection is usually based on single frame images, which is vulnerable to affected by the similar targets in the image and not applicable to video images. In this paper , anchor mask is proposed to add the prior knowledge for target detection and an anchor mask net is designed to impove the RPN performance for single target detection. Tested in the VOT2016, the model perform better.
Prune and Replace NAS
Venue: arXiv cs.LG 2019
Year: 2019

While recent NAS algorithms are thousands of times faster than the pioneering works, it is often overlooked that they use fewer candidate operations, resulting in a significantly smaller search space. We present PR-DARTS, a NAS algorithm that discovers strong network configurations in a much larger search space and a single day. A small candidate operation pool is used, from which candidates are progressively pruned and replaced with better performing ones. Experiments on CIFAR-10 and CIFAR-100 achieve 2.51% and 15.53% test error, respectively, despite searching in a space where each cell has 150 times as many possible configurations than in the DARTS baseline. Code is available at https://github.com/cogsys-tuebingen/prdarts
Hyperintensional Reasoning based on Natural Language Knowledge Base
Venue: arXiv cs.CL 2019
Year: 2019

The success of automated reasoning techniques over large natural-language texts heavily relies on a fine-grained analysis of natural language assumptions. While there is a common agreement that the analysis should be hyperintensional, most of the automatic reasoning systems are still based on an intensional logic, at the best. In this paper, we introduce the system of reasoning based on a fine-grained, hyperintensional analysis. To this end we apply Tichy's Transparent Intensional Logic (TIL) with its procedural semantics. TIL is a higher-order, hyperintensional logic of partial functions, in particular apt for a fine-grained natural-language analysis. Within TIL we recognise three kinds of context, namely extensional, intensional and hyperintensional, in which a particular natural-language term, or rather its meaning, can occur. Having defined the three kinds of context and implemented an algorithm of context recognition, we are in a position to develop and implement an extensional logic of hyperintensions with the inference machine that should neither over-infer nor under-infer.
On the needs for MaaS platforms to handle competition in ridesharing mobility
Venue: arXiv cs.CY 2019
Year: 2019

Ridesharing has been emerging as a new type of mobility. However, the early promises of ridesharing for alleviating congestion in cities may be undermined by a number of challenges, including the growing number of proposed services and the subsequent increasing number of vehicles, as a natural consequence of competition. In this work, we present optimization-based approaches to model cooperation and competition between multiple ridesharing companies, in a real-time on-demand setting. A recent trend relies on solving the integrated combination of Dial-A-Ride Problems (DARP), which compute the cost of assigning incoming requests to vehicle routes, plus Linear Assignment Problems (LAP), which assign vehicles to requests. While the DARPs, are solved at the level of the vehicles of each company, we introduce cooperative and competitive approaches to solve the LAP. The cooperative model, which could make use of Mobility as a Service platforms, is shown to solve the LAP to optimality following closely results from the literature, and limiting the amount of information the companies are required to share. We investigate how a realistic model of competition deviates from this optimality and provide worst case bounds. We evaluate these models with respect to a centralized model on one-week instances of the New York City taxi dataset. Model variants coping with noise in the travel time estimations, bias in the assignment costs, and preferences in the competitive case are also presented and validated. The computational results suggest that cooperation among ridesharing companies can be conducted in such a way to limit the degradation of the level of service with respect to a centralized model. Finally, we argue that the competition can lower the quality of the ridesharing service, especially in the case customer preferences are accommodated.
Bspline solids manipulation with Mathematica
Venue: arXiv cs.MS 2019
Year: 2019

Bspline solids are used for solid objects modeling in R3. Mathematica incorporates a several commands to manipulate symbolic and graphically Bspline basis functions and to graphically manipulate Bsplines curves and surfaces; however, it does not incorporate any command to the graphical manipulation of Bspline solids. In this paper, we describe a new Mathematica program to compute and plotting the Bspline solids. The output obtained is consistent with Mathematica's notation. The performance of the commands are discussed by using some illustrative examples.
Trans-Sense: Real Time Transportation Schedule Estimation Using Smart Phones
Venue: arXiv cs.CY 2019
Year: 2019

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule. Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%.In addition, the system estimates the stations dimensions with an accuracy of 95.71%.
The Dynamics of Handwriting Improves the Automated Diagnosis of Dysgraphia
Venue: arXiv cs.CY 2019
Year: 2019

Handwriting disorder (termed dysgraphia) is a far from a singular problem as nearly 8.6% of the population in France is considered dysgraphic. Moreover, research highlights the fundamental importance to detect and remediate these handwriting difficulties as soon as possible as they may affect a child's entire life, undermining performance and self-confidence in a wide variety of school activities. At the moment, the detection of handwriting difficulties is performed through a standard test called BHK. This detection, performed by therapists, is laborious because of its high cost and subjectivity. We present a digital approach to identify and characterize handwriting difficulties via a Recurrent Neural Network model (RNN). The child under investigation is asked to write on a graphics tablet all the letters of the alphabet as well as the ten digits. Once complete, the RNN delivers a diagnosis in a few milliseconds and demonstrates remarkable efficiency as it correctly identifies more than 90% of children diagnosed as dysgraphic using the BHK test. The main advantage of our tablet-based system is that it captures the dynamic features of writing -- something a human expert, such as a teacher, is unable to do. We show that incorporating the dynamic information available by the use of tablet is highly beneficial to our digital test to discriminate between typically-developing and dysgraphic children.
EcoMobiFog -- Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications
Venue: arXiv cs.NI 2019
Year: 2019

The emerging 5G paradigm will enable multi-radio smartphones to run high-rate stream applications. However, since current smartphones remain resource and battery-limited, the 5G era opens new challenges on how to actually support these applications. In principle, the service orchestration capability of the Fog and Cloud Computing paradigms could be an effective means of dynamically providing resource-augmentation to smartphones. Motivated by these considerations, the peculiar focus of this paper is on the joint and adaptive optimization of the resource and task allocations of mobile stream applications in 5G-supported multi-tier Mobile-Fog-Cloud virtualized ecosystems. The objective is the minimization of the computing-plus-network energy of the overall ecosystem under hard constraints on the minimum streaming rate and the maximum computing-plus-networking resources. To this end: 1) we model the target ecosystem energy by explicitly accounting for the virtualized and multi-core nature of the Fog/Cloud servers; 2) since the resulting problem is non-convex and involves both continuous and discrete variables, we develop an optimality-preserving decomposition into the cascade of a (continuous) resource allocation sub-problem and a (discrete) task-allocation sub-problem; and 3) we numerically solve the first sub-problem through a suitably designed set of gradient-based adaptive iterations, while we approach the solution of the second sub-problem by resorting to an ad-hoc-developed elitary Genetic algorithm. Finally, we design the main blocks of EcoMobiFog, a technological virtualized platform for supporting the developed solver. The extensive numerical tests confirm that the energy-delay performance of the proposed solving framework is typically within a few per-cent the benchmark one of the exhaustive search-based solution.
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
Venue: arXiv cs.LG 2019
Year: 2019

Scientific imaging techniques such as optical and electron microscopy and computed tomography (CT) scanning are used to study the 3D structure of an object through 2D observations. These observations are related to the original 3D object through orthogonal integral projections. For common 3D reconstruction algorithms, computational efficiency requires the modeling of the 3D structures to take place in Fourier space by applying the Fourier slice theorem. At present, it is unclear how to differentiate through the projection operator, and hence current learning algorithms can not rely on gradient based methods to optimize 3D structure models. In this paper we show how back-propagation through the projection operator in Fourier space can be achieved. We demonstrate the validity of the approach with experiments on 3D reconstruction of proteins. We further extend our approach to learning probabilistic models of 3D objects. This allows us to predict regions of low sampling rates or estimate noise. A higher sample efficiency can be reached by utilizing the learned uncertainties of the 3D structure as an unsupervised estimate of the model fit. Finally, we demonstrate how the reconstruction algorithm can be extended with an amortized inference scheme on unknown attributes such as object pose. Through empirical studies we show that joint inference of the 3D structure and the object pose becomes more difficult when the ground truth object contains more symmetries. Due to the presence of for instance (approximate) rotational symmetries, the pose estimation can easily get stuck in local optima, inhibiting a fine-grained high-quality estimate of the 3D structure.
A Study of the Learning Progress in Neural Architecture Search Techniques
Venue: arXiv cs.LG 2019
Year: 2019

In neural architecture search, the structure of the neural network to best model a given dataset is determined by an automated search process. Efficient Neural Architecture Search (ENAS), proposed by Pham et al. (2018), has recently received considerable attention due to its ability to find excellent architectures within a comparably short search time. In this work, which is motivated by the quest to further improve the learning speed of architecture search, we evaluate the learning progress of the controller which generates the architectures in ENAS. We measure the progress by comparing the architectures generated by it at different controller training epochs, where architectures are evaluated after having re-trained them from scratch. As a surprising result, we find that the learning curves are completely flat, i.e., there is no observable progress of the controller in terms of the performance of its generated architectures. This observation is consistent across the CIFAR-10 and CIFAR-100 datasets and two different search spaces. We conclude that the high quality of the models generated by ENAS is a result of the search space design rather than the controller training, and our results indicate that one-shot architecture design is an efficient alternative to architecture search by ENAS.
Improving Sentiment Analysis with Multi-task Learning of Negation
Venue: arXiv cs.CL 2019
Year: 2019

Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in a data-driven manner. We describe our approach, a cascading neural architecture with selective sharing of LSTM layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.
Joint Learning of Geometric and Probabilistic Constellation Shaping
Venue: arXiv cs.IT 2019
Year: 2019

The choice of constellations largely affects the performance of communication systems. When designing constellations, both the locations and probability of occurrence of the points can be optimized. These approaches are referred to as geometric and probabilistic shaping, respectively. Usually, the geometry of the constellation is fixed, e.g., quadrature amplitude modulation (QAM) is used. In such cases, the achievable information rate can still be improved by probabilistic shaping. In this work, we show how autoencoders can be leveraged to perform probabilistic shaping of constellations. We devise an information-theoretical description of autoencoders, which allows learning of capacity-achieving symbol distributions and constellations. Recently, machine learning techniques to perform geometric shaping were proposed. However, probabilistic shaping is more challenging as it requires the optimization of discrete distributions. Furthermore, the proposed method enables joint probabilistic and geometric shaping of constellations over any channel model. Simulation results show that the learned constellations achieve information rates very close to capacity on an additive white Gaussian noise (AWGN) channel and outperform existing approaches on both AWGN and fading channels.
Cooperative Rate-Splitting for MISO Broadcast Channel with User Relaying, and Performance Benefits over Cooperative NOMA
Venue: arXiv cs.IT 2019
Year: 2019

Due to its promising performance in a wide range of practical scenarios, Rate-Splitting (RS) has recently received significant attention in academia for the downlink of communication systems. In this letter, we propose and analyse a Cooperative Rate-Splitting (CRS) strategy based on the three-node relay channel where the transmitter is equipped with multiple antennas. By splitting user messages and linearly precoding common and private streams at the transmitter, and opportunistically asking the relaying user to forward its decoded common message, CRS can efficiently cope with a wide range of propagation conditions (disparity of user channel strengths and directions) and compensate for the performance degradation due to deep fading. The precoder design and the resource allocation are optimized by solving the Weighted Sum Rate (WSR) maximization problem. Numerical results demonstrate that our proposed CRS scheme can achieve an explicit rate region improvement compared to its non-cooperative counterpart and other cooperative strategies (such as cooperative NOMA).
Analytical Derivatives for Differentiable Renderer: 3D Pose Estimation by Silhouette Consistency
Venue: arXiv cs.CV 2019
Year: 2019

Differentiable render is widely used in optimization-based 3D reconstruction which requires gradients from differentiable operations for gradient-based optimization. The existing differentiable renderers obtain the gradients of rendering via numerical technique which is of low accuracy and efficiency. Motivated by this fact, a differentiable mesh renderer with analytical gradients is proposed. The main obstacle of rasterization based rendering being differentiable is the discrete sampling operation. To make the rasterization differentiable, the pixel intensity is defined as a double integral over the pixel area and the integral is approximated by anti-aliasing with an average filter. Then the analytical gradients with respect to the vertices coordinates can be derived from the continuous definition of pixel intensity. To demonstrate the effectiveness and efficiency of the proposed differentiable renderer, experiments of 3D pose estimation by only multi-viewpoint silhouettes were conducted. The experimental results show that 3D pose estimation without 3D and 2D joints supervision is capable of producing competitive results both qualitatively and quantitatively. The experimental results also show that the proposed differentiable renderer is of higher accuracy and efficiency compared with previous method of differentiable renderer.
Low Probability of Detection Communication: Opportunities and Challenges
Venue: arXiv cs.IT 2019
Year: 2019

Low probability of detection (LPD) communication has recently emerged as a new transmission technology to address privacy and security in wireless networks. Recent studies have established the fundamental limits of LPD communication in terms of the amount of information bits that can be conveyed from a transmitter to a receiver subject to a constraint on a warden's detection error probability. The established information-theoretic metric enables analytical studies on the design and performance of LPD communication under various channel conditions. In this article, we present the key features of LPD communication and discuss various important design considerations. Firstly, we clarify the differences between LPD communication and the well-known physical-layer security. Then, from an information-theoretic point of view, we discuss the optimal signalling strategies for transmitting the message-carrying signal and artificial-noise signal for LPD communication. Finally, we identify the key challenges in the design of practical LPD communication systems and point out future research directions in this context. This article provides guidelines for designing practical LPD communication strategies in wireless systems and networks.