OpenRaiser/Intern-Atlas huggingface dataset:
Measure of quality of finite-dimensional linear systems: A frame-theoretic view
Venue: arXiv cs.SY 2019
Year: 2019

A measure of quality of a control system is a quantitative extension of the classical binary notion of controllability. In this article we study the quality of linear control systems from a frame-theoretic perspective. We demonstrate that all LTI systems naturally generate a frame on their state space, and that three standard measures of quality involving the trace, minimum eigenvalue, and the determinant of the controllability Gramian achieve their optimum values when this generated frame is tight. Motivated by this, and in view of some recent developments in frame-theoretic signal processing, we propose a natural measure of quality for continuous time LTI systems based on a measure of tightness of the frame generated by it and then discuss some properties of this frame-theoretic measure of quality.
The Heat is On: Exploring User Behaviour in a Multisensory Virtual Environment for Fire Evacuation
Venue: arXiv cs.HC 2019
Year: 2019

Understanding validity of user behaviour in Virtual Environments (VEs) is critical as they are increasingly being used for serious Health and Safety applications such as predicting human behaviour and training in hazardous situations. This paper presents a comparative study exploring user behaviour in VE-based fire evacuation and investigates whether this is affected by the addition of thermal and olfactory simulation. Participants (N=43) were exposed to a virtual fire in an office building. Quantitative and qualitative analyses of participant attitudes and behaviours found deviations from those we would expect in real life (e.g. pre-evacuation actions), but also valid behaviours like fire avoidance. Potentially important differences were found between multisensory and audiovisual-only conditions (e.g. perceived urgency). We conclude VEs have significant potential in safety-related applications, and that multimodality may afford additional uses in this context, but the identified limitations of behavioural validity must be carefully considered to avoid misapplication of the technology.
Extreme Tensoring for Low-Memory Preconditioning
Venue: arXiv cs.LG 2019
Year: 2019

State-of-the-art models are now trained with billions of parameters, reaching hardware limits in terms of memory consumption. This has created a recent demand for memory-efficient optimizers. To this end, we investigate the limits and performance tradeoffs of memory-efficient adaptively preconditioned gradient methods. We propose extreme tensoring for high-dimensional stochastic optimization, showing that an optimizer needs very little memory to benefit from adaptive preconditioning. Our technique applies to arbitrary models (not necessarily with tensor-shaped parameters), and is accompanied by regret and convergence guarantees, which shed light on the tradeoffs between preconditioner quality and expressivity. On a large-scale NLP model, we reduce the optimizer memory overhead by three orders of magnitude, without degrading performance.
Value constrained model-free continuous control
Venue: arXiv cs.RO 2019
Year: 2019

The naive application of Reinforcement Learning algorithms to continuous control problems -- such as locomotion and manipulation -- often results in policies which rely on high-amplitude, high-frequency control signals, known colloquially as bang-bang control. Although such solutions may indeed maximize task reward, they can be unsuitable for real world systems. Bang-bang control may lead to increased wear and tear or energy consumption, and tends to excite undesired second-order dynamics. To counteract this issue, multi-objective optimization can be used to simultaneously optimize both the reward and some auxiliary cost that discourages undesired (e.g. high-amplitude) control. In principle, such an approach can yield the sought after, smooth, control policies. It can, however, be hard to find the correct trade-off between cost and return that results in the desired behavior. In this paper we propose a new constraint-based reinforcement learning approach that ensures task success while minimizing one or more auxiliary costs (such as control effort). We employ Lagrangian relaxation to learn both (a) the parameters of a control policy that satisfies the desired constraints and (b) the Lagrangian multipliers for the optimization. Moreover, we demonstrate that we can satisfy constraints either in expectation or in a per-step fashion, and can even learn a single policy that is able to dynamically trade-off between return and cost. We demonstrate the efficacy of our approach using a number of continuous control benchmark tasks, a realistic, energy-optimized quadruped locomotion task, as well as a reaching task on a real robot arm.
Self-adaptive Single and Multi-illuminant Estimation Framework based on Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

Illuminant estimation plays a key role in digital camera pipeline system, it aims at reducing color casting effect due to the influence of non-white illuminant. Recent researches handle this task by using Convolution Neural Network (CNN) as a mapping function from input image to a single illumination vector. However, global mapping approaches are difficult to deal with scenes under multi-light-sources. In this paper, we proposed a self-adaptive single and multi-illuminant estimation framework, which includes the following novelties: (1) Learning local self-adaptive kernels from the entire image for illuminant estimation with encoder-decoder CNN structure; (2) Providing confidence measurement for the prediction; (3) Clustering-based iterative fitting for computing single and multi-illumination vectors. The proposed global-to-local aggregation is able to predict multi-illuminant regionally by utilizing global information instead of training in patches, as well as brings significant improvement for single illuminant estimation. We outperform the state-of-the-art methods on standard benchmarks with the largest relative improvement of 16%. In addition, we collect a dataset contains over 13k images for illuminant estimation and evaluation. The code and dataset is available on https://github.com/LiamLYJ/KPF_WB
A characterisation of S-box fitness landscapes in cryptography
Venue: arXiv cs.NE 2019
Year: 2019

Substitution Boxes (S-boxes) are nonlinear objects often used in the design of cryptographic algorithms. The design of high quality S-boxes is an interesting problem that attracts a lot of attention. Many attempts have been made in recent years to use heuristics to design S-boxes, but the results were often far from the previously known best obtained ones. Unfortunately, most of the effort went into exploring different algorithms and fitness functions while little attention has been given to the understanding why this problem is so difficult for heuristics. In this paper, we conduct a fitness landscape analysis to better understand why this problem can be difficult. Among other, we find that almost each initial starting point has its own local optimum, even though the networks are highly interconnected.
A Subject-Specific Four-Degree-of-Freedom Foot Interface to Control a Robot Arm
Venue: arXiv cs.HC 2019
Year: 2019

In robotic surgery, the surgeon controls robotic instruments using dedicated interfaces. One critical limitation of current interfaces is that they are designed to be operated by only the hands. This means that the surgeon can only control at most two robotic instruments at one time while many interventions require three instruments. This paper introduces a novel four-degree-of-freedom foot-machine interface which allows the surgeon to control a third robotic instrument using the foot, giving the surgeon a "third hand". This interface is essentially a parallel-serial hybrid mechanism with springs and force sensors. Unlike existing switch-based interfaces that can only un-intuitively generate motion in discrete directions, this interface allows intuitive control of a slave robotic arm in continuous directions and speeds, naturally matching the foot movements with dynamic force & position feedbacks. An experiment with ten naive subjects was conducted to test the system. In view of the significant variance of motion patterns between subjects, a subject-specific mapping from foot movements to command outputs was developed using Independent Component Analysis (ICA). Results showed that the ICA method could accurately identify subjects' foot motion patterns and significantly improve the prediction accuracy of motion directions from 68% to 88% as compared with the forward kinematics-based approach. This foot-machine interface can be applied for the teleoperation of industrial/surgical robots independently or in coordination with hands in the future.
Petascale Cloud Supercomputing for Terapixel Visualization of a Digital Twin
Venue: arXiv cs.DC 2019
Year: 2019

Background: Photo-realistic terapixel visualization is computationally intensive and to date there have been no such visualizations of urban digital twins, the few terapixel visualizations that exist have looked towards space rather than earth. Objective: our aims are: creating a scalable cloud supercomputer software architecture for visualization; a photo-realistic terapixel 3D visualization of urban IoT data supporting daily updates; a rigorous evaluation of cloud supercomputing for our application. Method: we migrated the Blender Cycles path tracer to the public cloud within a new software framework designed to scale to petaFLOP performance. Results: we demonstrate we can compute a terapixel visualization in under one hour, the system scaling at 98% efficiency to use 1024 public cloud GPU nodes delivering 14 petaFLOPS. The resulting terapixel image supports interactive browsing of the city and its data at a wide range of sensing scales. Conclusion: The GPU compute resource available in the cloud is greater than anything available on our national supercomputers providing access to globally competitive resources. The direct financial cost of access, compared to procuring and running these systems, was low. The indirect cost, in overcoming teething issues with cloud software development, should reduce significantly over time.
Federated Machine Learning: Concept and Applications
Venue: arXiv cs.AI 2019
Year: 2019

Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.
Multi-views Embedding for Cattle Re-identification
Venue: arXiv cs.CV 2019
Year: 2019

People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets. However, little research has been conducted on animal identification and re-identification, even if this knowledge may be useful in a rich variety of different scenarios. Here, we tackle cattle re-identification exploiting deep CNN and show how this task is poorly related with the human one, presenting unique challenges that makes it far from being solved. We present various baselines, both based on deep architectures or on standard machine learning algorithms, and compared them with our solution. Finally, a rich ablation study has been conducted to further investigate the unique peculiarities of this task.
Energy harvesting wireless networks with correlated energy sources
Venue: arXiv cs.PF 2019
Year: 2019

This work considers a system with two energy harvesting (EH) nodes transmitting to a common destination over a random access channel. The amount of harvested energy is assumed to be random and independent over time, but correlated among the nodes possibly with respect to their relative position. A threshold-based transmission policy is developed for the maximization of the expected aggregate network throughput. Assuming that there is no a priori channel state or EH information available to the nodes, the aggregate network throughput is obtained. The optimal thresholds are determined for two practically important special cases: i) at any time only one of the sensors harvests energy due to, for example, physical separation of the nodes; ii) the nodes are spatially close, and at any time, either both nodes or none of them harvests energy.
Sample Variance Decay in Randomly Initialized ReLU Networks
Venue: arXiv cs.LG 2019
Year: 2019

Before training a neural net, a classic rule of thumb is to randomly initialize the weights so the variance of activations is preserved across layers. This is traditionally interpreted using the total variance due to randomness in both weights \emph{and} samples. Alternatively, one can interpret the rule of thumb as preservation of the variance over samples for a fixed network. The two interpretations differ little for a shallow net, but the difference is shown to grow with depth for a deep ReLU net by decomposing the total variance into the network-averaged sum of the sample variance and square of the sample mean. We demonstrate that even when the total variance is preserved, the sample variance decays in the later layers through an analytical calculation in the limit of infinite network width, and numerical simulations for finite width. We show that Batch Normalization eliminates this decay and provide empirical evidence that preserving the sample variance instead of only the total variance at initialization time can have an impact on the training dynamics of a deep network.
Two-Dimensional Batch Linear Programming on the GPU
Venue: arXiv cs.DC 2019
Year: 2019

This paper presents a novel, high-performance, graphical processing unit-based algorithm for efficiently solving two-dimensional linear programs in batches. The domain of two-dimensional linear programs is particularly useful due to the prevalence of relevant geometric problems. Batch linear programming refers to solving numerous different linear programs within one operation. By solving many linear programs simultaneously and distributing workload evenly across threads, graphical processing unit utilization can be maximized. Speedups of over 22 times and 63 times are obtained against state-of-the-art graphics processing unit and CPU linear program solvers, respectively.
How do infinite width bounded norm networks look in function space?
Venue: arXiv cs.LG 2019
Year: 2019

We consider the question of what functions can be captured by ReLU networks with an unbounded number of units (infinite width), but where the overall network Euclidean norm (sum of squares of all weights in the system, except for an unregularized bias term for each unit) is bounded; or equivalently what is the minimal norm required to approximate a given function. For functions $f : \mathbb R \rightarrow \mathbb R$ and a single hidden layer, we show that the minimal network norm for representing $f$ is $\max(\int |f''(x)| dx, |f'(-\infty) + f'(+\infty)|)$, and hence the minimal norm fit for a sample is given by a linear spline interpolation.
Probabilistic Generative Deep Learning for Molecular Design
Venue: arXiv cs.LG 2019
Year: 2019

Probabilistic generative deep learning for molecular design involves the discovery and design of new molecules and analysis of their structure, properties and activities by probabilistic generative models using the deep learning approach. It leverages the existing huge databases and publications of experimental results, and quantum-mechanical calculations, to learn and explore molecular structure, properties and activities. We discuss the major components of probabilistic generative deep learning for molecular design, which include molecular structure, molecular representations, deep generative models, molecular latent representations and latent space, molecular structure-property and structure-activity relationships, molecular similarity and molecular design. We highlight significant recent work using or applicable to this new approach.
A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, a novel covariance-based channel feedback mechanism is investigated for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The concept capitalizes on the notion of user statistical separability which was hinted in several prior works in the massive antenna regime but not fully exploited so far. We here propose a hybrid statistical-instantaneous feedback mechanism where the users are separated into two classes of feedback design based on their channel covariance. Under the hybrid framework, each user either operates on a statistical feedback mode or quantized instantaneous channel feedback mode depending on their so-called statistical isolability. The key challenge lies in the design of a covariance-aware classification algorithm which can handle the complex mutual interactions between all users. The classification is derived from rate bound principles. A suitable precoding method is also devised under the mixed statistical and instantaneous feedback model. Simulations are performed to validate our analytical results and illustrate the sum rate advantages of the proposed feedback scheme under a global feedback overhead constraint.
Non-contact photoplethysmogram and instantaneous heart rate estimation from infrared face video
Venue: arXiv cs.CV 2019
Year: 2019

Extracting the instantaneous heart rate (iHR) from face videos has been well studied in recent years. It is well known that changes in skin color due to blood flow can be captured using conventional cameras. One of the main limitations of methods that rely on this principle is the need of an illumination source. Moreover, they have to be able to operate under different light conditions. One way to avoid these constraints is using infrared cameras, allowing the monitoring of iHR under low light conditions. In this work, we present a simple, principled signal extraction method that recovers the iHR from infrared face videos. We tested the procedure on 7 participants, for whom we recorded an electrocardiogram simultaneously with their infrared face video. We checked that the recovered signal matched the ground truth iHR, showing that infrared is a promising alternative to conventional video imaging for heart rate monitoring, especially in low light conditions. Code is available at https://github.com/natalialmg/IR_iHR
TIRA: Toolbox for Interval Reachability Analysis
Venue: arXiv cs.SY 2019
Year: 2019

This paper presents TIRA, a Matlab library gathering several methods for the computation of interval over-approximations of the reachable sets for both continuous- and discrete-time nonlinear systems. Unlike other existing tools, the main strength of interval-based reachability analysis is its simplicity and scalability, rather than the accuracy of the over-approximations. The current implementation of TIRA contains four reachability methods covering wide classes of nonlinear systems, handled with recent results relying on contraction/growth bounds and monotonicity concepts. TIRA's architecture features a central function working as a hub between the user-defined reachability problem and the library of available reachability methods. This design choice offers increased extensibility of the library, where users can define their own method in a separate function and add the function call in the hub function.
A Framework towards Quantifying Human Restorativeness in Virtual Built Environments
Venue: arXiv cs.CY 2019
Year: 2019

The impact of built environment on the human restorativeness has long been argued; however, the interrelations between neuroscience and the built environment, and the degree to which the built environment contributes to increased human restorativeness has not been completely understood yet. Understanding the interrelations between neuroscience and the built environment is critical as 90% of time in a typical day is spent indoors and architectural features impact the productivity, health and comfort of occupants. The goal of this study is to bring a structured understanding of architecture and neuroscience interactions in designed facilities and quantification of the impact of design on human experience. The authors first built two virtual environments (i.e., restorative and non-restorative) using the architectural designs features related to human restorativeness identified by previous research efforts. Next, user experiments were conducted in the two built virtual environments including 22 people. The subjects were asked to conduct navigational tasks while their bodily responses recorded by body area sensors (e.g., EEG, GSR, and Eye-tracking). The result showed that human responses in restorative and non-restorative environment had statistically significant difference. This study serves as the first step of understanding human responses in the virtual environment, and designing spaces that maximize human experience.
Reasoning About a Machine with Local Capabilities: Provably Safe Stack and Return Pointer Management - Technical Appendix Including Proofs and Details
Venue: arXiv cs.PL 2019
Year: 2019

We propose a calling convention for capability machines with local capabilities. The calling convention ensures local-state encapsulation and well-bracketed control flow. We use the calling convention in a hand-full of program examples and prove that they behave correctly. The correctness proofs use a logical relation that is also presented in this appendix. This is the technical appendix for the paper with the same name and authors accepted at ESOP18 and under review for TOPLAS.
Efficient Decoding of Synchronized Colliding LoRa Signals
Venue: arXiv cs.NI 2019
Year: 2019

In LoRa (Long Range), when a collision occurs in the network, each end-device has to retransmit its colliding frame, which reduces the throughput, and increases the energy consumption of the end-devices and the delay of the frames. In this paper, we propose an algorithm to decode colliding synchronized LoRa signals and thus improve the overall performance of the network. Indeed, we use successive transmissions of bitmaps by the end-devices to determine the correct symbols of each colliding frame, instead of retransmitting the whole frames. Simulation results show that our algorithm is able to significantly improve the overall throughput of LoRaWAN, and to decrease the energy consumption and the delay of the transmitters.