OpenRaiser/Intern-Atlas huggingface dataset:
Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features
Venue: arXiv cs.CV 2020
Year: 2020

We explore the application of linear discriminant analysis (LDA) to the features obtained in different layers of pretrained deep convolutional neural networks (CNNs). The advantage of LDA compared to other techniques in dimensionality reduction is that it reduces dimensions while preserving the global structure of data, so distances in the low-dimensional structure found are meaningful. The LDA applied to the CNN features finds that the centroids of classes corresponding to the similar data lay closer than classes corresponding to different data. We applied the method to a modification of the MNIST dataset with ten additional classes, each new class with half of the images from one of the standard ten classes. The method finds the new classes close to the corresponding standard classes we took the data form. We also applied the method to a dataset of images of butterflies to find that related subspecies are found to be close. For both datasets, we find a performance similar to state-of-the-art methods.
Optimizing Placement of Heap Memory Objects in Energy-Constrained Hybrid Memory Systems
Venue: arXiv cs.AR 2020
Year: 2020

Main memory (DRAM) significantly impacts the power and energy utilization of the overall server system. Non-Volatile Memory (NVM) devices, such as Phase Change Memory and Spin-Transfer Torque RAM, are suitable candidates for main memory to reduce energy consumption. But unlike DRAM, NVMs access latencies are higher than DRAM and NVM writes are more energy sensitive than DRAM write operations. Thus, Hybrid Main Memory Systems (HMMS) employing DRAM and NVM have been proposed to reduce the overall energy depletion of main memory while optimizing the performance of NVM. This paper proposes eMap, an optimal heap memory object placement planner in HMMS. eMap considers the object-level access patterns and energy consumption at the application level and provides an ideal placement strategy for each object to augment performance and energy utilization. eMap is equipped with two modules, eMPlan and eMDyn. Specifically, eMPlan is a static placement planner which provides one time placement policies for memory object to meet the energy budget while eMDyn is a runtime placement planner to consider the change in energy limiting constraint during the runtime and shuffles the memory objects by taking into account the access patterns as well as the migration cost in terms of energy and performance. The evaluation shows that our proposed solution satisfies both the energy limiting constraint and the performance. We compare our methodology with the state-of-the-art memory object classification and allocation (MOCA) framework. Our extensive evaluation shows that our proposed solution, eMPlan meets the energy constraint with 4.17 times less costly and reducing the energy consumption up to 14% with the same performance. eMDyn also satisfies the performance and energy requirement while considering the migration cost in terms of time and energy.
High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups
Venue: arXiv cs.CV 2020
Year: 2020

Large driving datasets are a key component in the current development and safeguarding of automated driving functions. Various methods can be used to collect such driving data records. In addition to the use of sensor equipped research vehicles or unmanned aerial vehicles (UAVs), the use of infrastructure sensor technology offers another alternative. To minimize object occlusion during data collection, it is crucial to record the traffic situation from several perspectives in parallel. A fusion of all raw sensor data might create better conditions for multi-object detection and tracking (MODT) compared to the use of individual raw sensor data. So far, no sufficient studies have been conducted to sufficiently confirm this approach. In our work we investigate the impact of fused LiDAR point clouds compared to single LiDAR point clouds. We model different urban traffic scenarios with up to eight 64-layer LiDARs in simulation and in reality. We then analyze the properties of the resulting point clouds and perform MODT for all emerging traffic participants. The evaluation of the extracted trajectories shows that a fused infrastructure approach significantly increases the tracking results and reaches accuracies within a few centimeters.
Covert Communications with Constrained Age of Information
Venue: arXiv cs.IT 2020
Year: 2020

In this letter, we consider the requirement of information freshness in covert communications for the first time. With artificial noise (AN) generated from a full-duplex (FD) receiver, we formulate a covertness maximization problem under the average age of information (AoI) constraint to optimize the transmit probability of information signal. In particular, the transmit probability not only represents the generation rate of information signal but also represents the prior probability of the alternative hypothesis in covert communications, which builds up a bridge between information freshness and communication covertness. Our analysis shows that the best transmit probability is not always 0.5, which differs from the equal prior probabilities assumption in most related works on covert communications. Furthermore, the limitation of average AoI enlarges the transmit probability at the cost of the covertness reduction and leads to a positive lower bound on the information transmit power for non-zero covertness.
Autonomous Execution of Cinematographic Shots with Multiple Drones
Venue: arXiv cs.RO 2020
Year: 2020

This paper presents a system for the execution of autonomous cinematography missions with a team of drones. The system allows media directors to design missions involving different types of shots with one or multiple cameras, running sequentially or concurrently. We introduce the complete architecture, which includes components for mission design, planning and execution. Then, we focus on the components related to autonomous mission execution. First, we propose a novel parametric description for shots, considering different types of camera motion and tracked targets; and we use it to implement a set of canonical shots. Second, for multi-drone shot execution, we propose distributed schedulers that activate different shot controllers on board the drones. Moreover, an event-based mechanism is used to synchronize shot execution among the drones and to account for inaccuracies during shot planning. Finally, we showcase the system with field experiments filming sport activities, including a real regatta event. We report on system integration and lessons learnt during our experimental campaigns.
Run-Time Power Modelling in Embedded GPUs with Dynamic Voltage and Frequency Scaling
Venue: arXiv cs.OH 2020
Year: 2020

This paper investigates the application of a robust CPU-based power modelling methodology that performs an automatic search of explanatory events derived from performance counters to embedded GPUs. A 64-bit Tegra TX1 SoC is configured with DVFS enabled and multiple CUDA benchmarks are used to train and test models optimized for each frequency and voltage point. These optimized models are then compared with a simpler unified model that uses a single set of model coefficients for all frequency and voltage points of interest. To obtain this unified model, a number of experiments are conducted to extract information on idle, clock and static power to derive power usage from a single reference equation. The results show that the unified model offers competitive accuracy with an average 5\% error with four explanatory variables on the test data set and it is capable to correctly predict the impact of voltage, frequency and temperature on power consumption. This model could be used to replace direct power measurements when these are not available due to hardware limitations or worst-case analysis in emulation platforms.
Approaches For Multi-View Redescription Mining
Venue: arXiv cs.LG 2020
Year: 2020

The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain redescriptions and allow for the exploration and analyses of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework can use any multi-target regression or multi-label classification algorithm, with models that can be represented as sets of rules, to generate redescriptions. Multi-view redescriptions are built using incremental view-extending heuristic from initially created two-view redescriptions. In this work, we use different types of Predictive Clustering trees algorithms (regular, extra, with random output selection) and the Random Forest thereof in order to improve the quality of final redescription sets and/or execution time needed to generate them. We provide multiple performance analyses of the proposed framework and compare it against the naive approach to multi-view redescription mining. We demonstrate the usefulness of the proposed multi-view extension on several datasets, including a use-case on understanding of machine learning models - a topic of growing importance in machine learning and artificial intelligence in general.
Deep Network with Approximation Error Being Reciprocal of Width to Power of Square Root of Depth
Venue: arXiv cs.LG 2020
Year: 2020

A new network with super approximation power is introduced. This network is built with Floor ($\lfloor x\rfloor$) or ReLU ($\max\{0,x\}$) activation function in each neuron and hence we call such networks Floor-ReLU networks. For any hyper-parameters $N\in\mathbb{N}^+$ and $L\in\mathbb{N}^+$, it is shown that Floor-ReLU networks with width $\max\{d,\, 5N+13\}$ and depth $64dL+3$ can uniformly approximate a Hölder function $f$ on $[0,1]^d$ with an approximation error $3λd^{α/2}N^{-α\sqrt{L}}$, where $α\in(0,1]$ and $λ$ are the Hölder order and constant, respectively. More generally for an arbitrary continuous function $f$ on $[0,1]^d$ with a modulus of continuity $ω_f(\cdot)$, the constructive approximation rate is $ω_f(\sqrt{d}\,N^{-\sqrt{L}})+2ω_f(\sqrt{d}){N^{-\sqrt{L}}}$. As a consequence, this new class of networks overcomes the curse of dimensionality in approximation power when the variation of $ω_f(r)$ as $r\to 0$ is moderate (e.g., $ω_f(r) \lesssim r^α$ for Hölder continuous functions), since the major term to be considered in our approximation rate is essentially $\sqrt{d}$ times a function of $N$ and $L$ independent of $d$ within the modulus of continuity.
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network
Venue: arXiv cs.CV 2020
Year: 2020

Significant progress has been made for estimating optical flow using deep neural networks. Advanced deep models achieve accurate flow estimation often with a considerable computation complexity and time-consuming training processes. In this work, we present a lightweight yet effective model for real-time optical flow estimation, termed FDFlowNet (fast deep flownet). We achieve better or similar accuracy on the challenging KITTI and Sintel benchmarks while being about 2 times faster than PWC-Net. This is achieved by a carefully-designed structure and newly proposed components. We first introduce an U-shape network for constructing multi-scale feature which benefits upper levels with global receptive field compared with pyramid network. In each scale, a partial fully connected structure with dilated convolution is proposed for flow estimation that obtains a good balance among speed, accuracy and number of parameters compared with sequential connected and dense connected structures. Experiments demonstrate that our model achieves state-of-the-art performance while being fast and lightweight.
Automatic Recall Machines: Internal Replay, Continual Learning and the Brain
Venue: arXiv cs.LG 2020
Year: 2020

Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective, without extraneous buffers or generator networks. Instead the implicit memory of learned samples within the assessed model itself is exploited. Furthermore, whereas existing work focuses on reinforcing the full seen data distribution, we show that optimizing for not forgetting calls for the generation of samples that are specialized to each real training batch, which is more efficient and scalable. We consider high-level parallels with the brain, notably the use of a single model for inference and recall, the dependency of recalled samples on the current environment batch, top-down modulation of activations and learning, abstract recall, and the dependency between the degree to which a task is learned and the degree to which it is recalled. These characteristics emerge naturally from the method without being controlled for.
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights
Venue: arXiv cs.LG 2020
Year: 2020

In the absence of large labelled datasets, self-supervised learning techniques can boost performance by learning useful representations from unlabelled data, which is often more readily available. However, there is often a domain shift between the unlabelled collection and the downstream target problem data. We show that by learning Bayesian instance weights for the unlabelled data, we can improve the downstream classification accuracy by prioritising the most useful instances. Additionally, we show that the training time can be reduced by discarding unnecessary datapoints. Our method, BetaDataWeighter is evaluated using the popular self-supervised rotation prediction task on STL-10 and Visual Decathlon. We compare to related instance weighting schemes, both hand-designed heuristics and meta-learning, as well as conventional self-supervised learning. BetaDataWeighter achieves both the highest average accuracy and rank across datasets, and on STL-10 it prunes up to 78% of unlabelled images without significant loss in accuracy, corresponding to over 50% reduction in training time.
Unsupervised Learning of Global Registration of Temporal Sequence of Point Clouds
Venue: arXiv cs.CV 2020
Year: 2020

Global registration of point clouds aims to find an optimal alignment of a sequence of 2D or 3D point sets. In this paper, we present a novel method that takes advantage of current deep learning techniques for unsupervised learning of global registration from a temporal sequence of point clouds. Our key novelty is that we introduce a deep Spatio-Temporal REPresentation (STREP) feature, which describes the geometric essence of both temporal and spatial relationship of the sequence of point clouds acquired with sensors in an unknown environment. In contrast to the previous practice that treats each time step (pair-wise registration) individually, our unsupervised model starts with optimizing a sequence of latent STREP feature, which is then decoded to a temporally and spatially continuous sequence of geometric transformations to globally align multiple point clouds. We have evaluated our proposed approach over both simulated 2D and real 3D datasets and the experimental results demonstrate that our method can beat other techniques by taking into account the temporal information in deep feature learning.
Deep Learning feature selection to unhide demographic recommender systems factors
Venue: arXiv cs.IR 2020
Year: 2020

Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. This paper provides a deep learning-based method: DeepUnHide, able to extract demographic information from the users and items factors in collaborative filtering recommender systems. The core of the proposed method is the gradient-based localization used in the image processing literature to highlight the representative areas of each classification class. Validation experiments make use of two public datasets and current baselines. Results show the superiority of DeepUnHide to make feature selection and demographic classification, compared to the state of art of feature selection methods. Relevant and direct applications include recommendations explanation, fairness in collaborative filtering and recommendation to groups of users.
Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning
Venue: arXiv cs.CY 2020
Year: 2020

Ecosystem restoration has been recognized to be critical to achieving accelerating progress on all of the United Nations' Sustainable Development Goals. Decision makers, policymakers, data scientists, earth scientists, and other scholars working on these projects could positively benefit from the explicit consideration and inclusion of diverse perspectives. Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being, the conservation of biodiversity, ecosystem functions, and the resilience of socio-ecological systems. Conceptual frameworks are needed for the meaningful integration of traditional ecological knowledge of indigenous peoples and local communities with data science and machine learning work practices. Adaptive frameworks would consider and address the needs and challenges of local communities and geographic locations by improving community and inter-agent communication around restoration and conservation projects and by making relevant real-time data accessible. In this paper, we provide a brief analysis of existing Machine Learning (ML) applications for forest ecosystem restoration projects. We go on to question if their inherent limitations may prevent them from being able to adequately address socio-cultural aspects of the well-being of all involved stakeholders. Bias and unintended consequences pose significant risks of downstream negative implications of ML-based solutions. We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects and align incentives between human and algorithmic actors. Furthermore, framing ML projects as open and reiterative processes can facilitate access on various levels and create incentives that lead to catalytic cooperation in the scaling of restoration efforts.
Time-Domain to Delay-Doppler Domain Conversion of OTFS Signals in Very High Mobility Scenarios
Venue: arXiv cs.IT 2020
Year: 2020

In Orthogonal Time Frequency Space (OTFS) modulation, information symbols are embedded in the delay-Doppler (DD) domain instead of the time-frequency (TF) domain. n order to ensure compatibility with existing OFDM systems (e.g. 4G LTE), most prior work on OTFS receivers consider a two-step conversion, where the received time-domain (TD) signal is firstly converted to a time-frequency (TF) signal (using an OFDM demodulator) followed by post-processing of this TF signal into a DD domain signal. In this paper, we show that the spectral efficiency (SE) performance of a two-step conversion based receiver degrades in very high mobility scenarios where the Doppler shift is a significant fraction of the communication bandwidth (e.g., control and non-payload communication (CNPC) in Unmanned Aircraft Systems (UAS)). We therefore consider an alternate conversion, where the received TD signal is directly converted to the DD domain. The resulting received DD domain signal is shown to be not the same as that obtained in the two-step conversion considered in prior works. The alternate conversion does not require an OFDM demodulator and is shown to have lower complexity than the two-step conversion. Analysis and simulations reveal that even in very high mobility scenarios, the SE achieved with the alternate conversion is invariant of Doppler shift and is significantly higher than the SE achieved with two-step conversion (which degrades with increasing Doppler shift).
Asymptotic Boundary Shrink Control with Multi-robot Systems
Venue: arXiv cs.RO 2020
Year: 2020

Harmful marine spills, such as algae blooms and oil spills, damage ecosystems and threaten public health tremendously. Hence, an effective spill coverage and removal strategy will play a significant role in environmental protection. In recent years, low-cost water surface robots have emerged as a solution, with their efficacy verified at small scale. However, practical limitations such as connectivity, scalability, and sensing and operation ranges significantly impair their large-scale use. To circumvent these limitations, we propose a novel asymptotic boundary shrink control strategy that enables collective coverage of a spill by autonomous robots featuring customized operation ranges. For each robot, a novel controller is implemented that relies only on local vision sensors with limited vision range. Moreover, the distributedness of this strategy allows any number of robots to be employed without inter-robot collisions. Finally, features of this approach including the convergence of robot motion during boundary shrink control, spill clearance rate, and the capability to work under limited ranges of vision and wireless connectivity are validated through extensive experiments with simulation.
$C$-differential bent functions and perfect nonlinearity
Venue: arXiv cs.IT 2020
Year: 2020

Drawing inspiration from Nyberg's paper~\cite{Nyb91} on perfect nonlinearity and the $c$-differential notion we defined in~\cite{EFRST20}, in this paper we introduce the concept of $c$-differential bent functions in two different ways (thus extending Kumar et al.~\cite{Ku85} classical definition). We further extend the notion of perfect $c$-nonlinear introduced in~\cite{EFRST20}, also in two different ways, and show that, in both cases, the concepts of $c$-differential bent and perfect $c$-nonlinear are equivalent (under some natural restriction of the parameters). Some constructions of functions with these properties are also provided; one such construction provides a large class of PcN functions with respect to all $c$ in some subfield of the field under consideration. We also show that both our classes of $0$-differential bents are supersets of permutation polynomials, and that Maiorana-McFarland bent functions are not differential bent (of the first kind).
PICO: Primitive Imitation for COntrol
Venue: arXiv cs.AI 2020
Year: 2020

In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO. The approach combines ideas from imitation learning, task decomposition, and novel task sequencing to generalize from demonstrations to new behaviors. Demonstrations are automatically decomposed into existing or missing sub-behaviors which allows the framework to identify novel behaviors while not duplicating existing behaviors. Generalization to new tasks is achieved through dynamic blending of behavior primitives. We evaluated the approach using demonstrations from two different robotic platforms. The experimental results show that PICO is able to detect the presence of a novel behavior primitive and build the missing control policy.