OpenRaiser/Intern-Atlas huggingface dataset:
Region segmentation via deep learning and convex optimization
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a method to segment regions in three-dimensional point clouds. We assume that (i) the shape and the number of regions in the point cloud are not known and (ii) the point cloud may be noisy. The method consists of two steps. In the first step we use a deep neural network to predict the probability that a pair of small patches from the point cloud belongs to the same region. In the second step, we use a convex-optimization based method to improve the predictions of the network by enforcing consistency constraints. We evaluate the accuracy of our method on a custom dataset of convex polyhedra, where the regions correspond to the faces of the polyhedra. The method can be seen as a robust and flexible alternative to the famous region growing segmentation algorithm. All reported results are reproducible and come with easy to use code that could serve as a baseline for future research.
Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards
Venue: arXiv cs.CV 2019
Year: 2019

Robotic harvesting of fruits in orchards is a challenging task, since high density and overlapping of fruits and branches can heavily impact the success rate of robotic harvesting. Therefore, the vision system is demanded to provide comprehensive information of the working environment to guide the manipulator and gripping system to successful detach the target fruits. In this study, a deep learning based one-stage detector DaSNet-V2 is developed to perform the multi-task vision sensing in the working environment of apple orchards. DaSNet-V2 combines the detection and instance segmentation of fruits and semantic segmentation of branch into a single network architecture. Meanwhile, a light-weight backbone network LW-net is utilised in the DaSNet-V2 model to improve the computational efficiency of the model. In the experiment, DaSNet-V2 is tested and evaluated on the RGB-D images of the orchard. From the experiment results, DaSNet-V2 with lightweight backbone achieves 0.844, 0.858, and 0.795 on the F 1 score of the detection, and mean intersection of union on the instance segmentation of fruits and semantic segmentation of branches, respectively. To provide a direct-viewing of the working environment in orchards, the obtained sensing results are illustrated by 3D visualisation . The robustness and efficiency of the DaSNet-V2 in detection and segmentation are validated by the experiments in the real-environment of apple orchard.
J-Net: Randomly weighted U-Net for audio source separation
Venue: arXiv cs.SD 2019
Year: 2019

Several results in the computer vision literature have shown the potential of randomly weighted neural networks. While they perform fairly well as feature extractors for discriminative tasks, a positive correlation exists between their performance and their fully trained counterparts. According to these discoveries, we pose two questions: what is the value of randomly weighted networks in difficult generative audio tasks such as audio source separation and does such positive correlation still exist when it comes to large random networks and their trained counterparts? In this paper, we demonstrate that the positive correlation still exists. Based on this discovery, we can try out different architecture designs or tricks without training the whole model. Meanwhile, we find a surprising result that in comparison to the non-trained encoder (down-sample path) in Wave-U-Net, fixing the decoder (up-sample path) to random weights results in better performance, almost comparable to the fully trained model.
Refining HTN Methods via Task Insertion with Preferences
Venue: arXiv cs.AI 2019
Year: 2019

Hierarchical Task Network (HTN) planning is showing its power in real-world planning. Although domain experts have partial hierarchical domain knowledge, it is time-consuming to specify all HTN methods, leaving them incomplete. On the other hand, traditional HTN learning approaches focus only on declarative goals, omitting the hierarchical domain knowledge. In this paper, we propose a novel learning framework to refine HTN methods via task insertion with completely preserving the original methods. As it is difficult to identify incomplete methods without designating declarative goals for compound tasks, we introduce the notion of prioritized preference to capture the incompleteness possibility of methods. Specifically, the framework first computes the preferred completion profile w.r.t. the prioritized preference to refine the incomplete methods. Then it finds the minimal set of refined methods via a method substitution operation. Experimental analysis demonstrates that our approach is effective, especially in solving new HTN planning instances.
Gender Patterns of Human Mobility in Colombia: Reexamining Ravenstein's Laws of Migration
Venue: arXiv cs.SI 2019
Year: 2019

Public stakeholders implement several policies and regulations to tackle gender gaps, fostering the change in the cultural constructs associated with gender. One way to quantify if such changes elicit gender equality is by studying mobility. In this work, we study the daily mobility patterns of women and men occurring in Medellín (Colombia) in two years: 2005 and 2017. Specifically, we focus on the spatiotemporal differences in the travels and find that purpose of travel and occupation characterise each gender differently. We show that women tend to make shorter trips, corroborating Ravenstein's Laws of Migration. Our results indicate that urban mobility in Colombia seems to behave in agreement with the "archetypal" case studied by Ravenstein.
Towards Oracle Knowledge Distillation with Neural Architecture Search
Venue: arXiv cs.LG 2019
Year: 2019

We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks. Our approach addresses the inherent model capacity issue between teacher and student and aims to maximize benefit from teacher models during distillation by reducing their capacity gap. Specifically, we employ a neural architecture search technique to augment useful structures and operations, where the searched network is appropriate for knowledge distillation towards student models and free from sacrificing its performance by fixing the network capacity. We also introduce an oracle knowledge distillation loss to facilitate model search and distillation using an ensemble-based teacher model, where a student network is learned to imitate oracle performance of the teacher. We perform extensive experiments on the image classification datasets---CIFAR-100 and TinyImageNet---using various networks. We also show that searching for a new student model is effective in both accuracy and memory size and that the searched models often outperform their teacher models thanks to neural architecture search with oracle knowledge distillation.
Hybrid Decision Trees: Longer Quantum Time is Strictly More Powerful
Venue: arXiv cs.CC 2019
Year: 2019

In this paper, we introduce the hybrid query complexity, denoted as $\mathrm{Q}(f;q)$, which is the minimal query number needed to compute $f$, when a classical decision tree is allowed to call $q'$-query quantum subroutines for any $q'\leq q$. We present the following results: $\bullet$ There exists a total Boolean function $f$ such that $\mathrm{Q}(f;1) = \widetilde{\mathcal{O}}(\mathrm{R}(f)^{4/5})$. $\bullet$ $\mathrm{Q}(f;q) = Ω(\mathrm{bs}(f)/q + \sqrt{\mathrm{bs}(f)})$ for any Boolean function $f$; the lower bound is tight when $f$ is the ${\rm O{\small R}}$ function. $\bullet$ $\mathrm{Q}(g \circ {\rm X{\small OR}}_{C \log n};1) = \widetildeΩ(\sqrt{n})$ for some sufficiently large constant $C$, where $g := {\rm B{\small OOL}S{\small IMON}}_n$ is a variant of Simon's problem. Note that $\mathrm{Q}(g\circ {\rm X{\small OR}}_{C \log n}) = \mathcal{O}(\mathrm{polylog}\; n)$. Therefore an exponential separation is established. Furthermore, this open the road to prove the conjecture $\forall k,\,\mathrm{Q}(g \circ {\rm X{\small OR}}_{C \log^{k+1} n};\log^{k} n) = \widetildeΩ(\sqrt{n})$, which would imply the oracle separation $\mathsf{HP}(\mathsf{QSIZE}(n^α))^\mathfrak{O} \subsetneq \mathsf{BQP}^\mathfrak{O}$ for any $α$, where $\mathsf{HP}(\mathsf{QSIZE}(n^α))$ is a complexity class that contains $\mathsf{BQTIME}(n^α)^{\mathsf{BPP}}$ and $\mathsf{BPP}^{\mathsf{BQTIME}(n^α)}$ in any relativized world.
A concrete example of inclusive design: deaf-oriented accessibility
Venue: arXiv cs.HC 2019
Year: 2019

One of the continuing challenges of Human Computer Interaction research is the full inclusion of people with special needs into the digital world. In particular, this crucial category includes people that experiences some kind of limitation in exploiting traditional information communication channels. One immediately thinks about blind people, and several researches aim at addressing their needs. On the contrary, limitations suffered by deaf people are often underestimated. This often the result of a kind of ignorance or misunderstanding of the real nature of their communication difficulties. This chapter aims at both increasing the awareness of deaf problems in the digital world, and at proposing the project of a comprehensive solution for their better inclusion. As for the former goal, we will provide a bird's-eye presentation of history and evolution of understanding of deafness issues, and of strategies to address them. As for the latter, we will present the design, implementation and evaluation of the first nucleus of a comprehensive digital framework to facilitate the access of deaf people into the digital world.
A multi-resolution SPH method for fluid-structure interactions
Venue: arXiv cs.CE 2019
Year: 2019

In this paper, we present a multi-resolution smoothed particle hydrodynamics (SPH) method for modeling fluid-structure interaction (FSI) problems. By introducing different smoothing lengths and time steps, the spatio-temporal discretization is applied with different resolutions for fluid and structure. To ensure momentum conservation at the fluid-structure coupling, a position-based Verlet time integration scheme is introduced. Furthermore, the time-averaged velocity and acceleration of solid particles are introduced to enhance force matching in the fluid and solid equations. A set of numerical examples including several bio-mechanical problems are considered to demonstrate the efficiency, accuracy and robustness of the present method. A open-source code for all the examples is also provided.
A Graph-based Ranking Approach to Extract Key-frames for Static Video Summarization
Venue: arXiv cs.MM 2019
Year: 2019

Video abstraction has become one of the efficient approaches to grasp the content of a video without seeing it entirely. Key frame-based static video summarization falls under this category. In this paper, we propose a graph-based approach which summarizes the video with best user satisfaction. We treated each video frame as a node of the graph and assigned a rank to each node by our proposed VidRank algorithm. We developed three different models of VidRank algorithm and performed a comparative study on those models. A comprehensive evaluation of 50 videos from open video database using objective and semi-objective measures indicates the superiority of our static video summary generation method.
Error-mitigated data-driven circuit learning on noisy quantum hardware
Venue: arXiv cs.ET 2019
Year: 2019

Application-inspired benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and for generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work we use gradient-based data-driven circuit learning to benchmark the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with $28$ tunable parameters.
A Restricted Second-Order Logic for Non-deterministic Poly-Logarithmic Time
Venue: arXiv cs.LO 2019
Year: 2019

We introduce a restricted second-order logic $\mathrm{SO}^{\mathit{plog}}$ for finite structures where second-order quantification ranges over relations of size at most poly-logarithmic in the size of the structure. We demonstrate the relevance of this logic and complexity class by several problems in database theory. We then prove a Fagin's style theorem showing that the Boolean queries which can be expressed in the existential fragment of $\mathrm{SO}^{\mathit{plog}}$ corresponds exactly to the class of decision problems that can be computed by a non-deterministic Turing machine with random access to the input in time $O((\log n)^k)$ for some $k \ge 0$, i.e., to the class of problems computable in non-deterministic poly-logarithmic time. It should be noted that unlike Fagin's theorem which proves that the existential fragment of second-order logic captures NP over arbitrary finite structures, our result only holds over ordered finite structures, since $\mathrm{SO}^{\mathit{plog}}$ is too weak as to define a total order of the domain. Nevertheless $\mathrm{SO}^{\mathit{plog}}$ provides natural levels of expressibility within poly-logarithmic space in a way which is closely related to how second-order logic provides natural levels of expressibility within polynomial space. Indeed, we show an exact correspondence between the quantifier prefix classes of $\mathrm{SO}^{\mathit{plog}}$ and the levels of the non-deterministic poly-logarithmic time hierarchy, analogous to the correspondence between the quantifier prefix classes of second-order logic and the polynomial-time hierarchy. Our work closely relates to the constant depth quasipolynomial size AND/OR circuits and corresponding restricted second-order logic defined by David A. Mix Barrington in 1992. We explore this relationship in detail.
Mechanism for Embossing Braille Characters on Paper: Conceptual Design
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents the conceptual design of a low-cost simple printer head for Braille embossers. Such device consists of a set of three rotary cam-follower mechanisms that, upon actuation, produce deformation on paper. The set of cam-followers is actuated by a single servomotor which rotation determines which cam-follower strikes the paper. Braille characters are quickly embossed by column using the proposed system. The aim of this research is to provide new actuation ideas for making Braille embossers more affordable.
A Reparameterization-Invariant Flatness Measure for Deep Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples. Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error. For several flatness measures, this correlation has been empirically validated. However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization due to a lack of invariance with respect to reparameterizations. We propose a natural modification of existing flatness measures that results in invariance to reparameterization.
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles
Venue: arXiv cs.LG 2019
Year: 2019

Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving. Direct applications of Reinforcement Learning algorithms with discrete action space will yield unsatisfactory results at the operational level of driving where continuous control actions are actually required. In addition, the design of neural networks often fails to incorporate the domain knowledge of the targeting problem such as the classical control theories in our case. In this paper, we propose a hybrid model by combining Q-learning and classic PID (Proportion Integration Differentiation) controller for handling continuous vehicle control problems under dynamic driving environment. Particularly, instead of using a big neural network as Q-function approximation, we design a Quadratic Q-function over actions with multiple simple neural networks for finding optimal values within a continuous space. We also build an action network based on the domain knowledge of the control mechanism of a PID controller to guide the agent to explore optimal actions more efficiently.We test our proposed approach in simulation under two common but challenging driving situations, the lane change scenario and ramp merge scenario. Results show that the autonomous vehicle agent can successfully learn a smooth and efficient driving behavior in both situations.
DeepMimic: Mentor-Student Unlabeled Data Based Training
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we present a deep neural network (DNN) training approach called the "DeepMimic" training method. Enormous amounts of data are available nowadays for training usage. Yet, only a tiny portion of these data is manually labeled, whereas almost all of the data are unlabeled. The training approach presented utilizes, in a most simplified manner, the unlabeled data to the fullest, in order to achieve remarkable (classification) results. Our DeepMimic method uses a small portion of labeled data and a large amount of unlabeled data for the training process, as expected in a real-world scenario. It consists of a mentor model and a student model. Employing a mentor model trained on a small portion of the labeled data and then feeding it only with unlabeled data, we show how to obtain a (simplified) student model that reaches the same accuracy and loss as the mentor model, on the same test set, without using any of the original data labels in the training of the student model. Our experiments demonstrate that even on challenging classification tasks the student network architecture can be simplified significantly with a minor influence on the performance, i.e., we need not even know the original network architecture of the mentor. In addition, the time required for training the student model to reach the mentor's performance level is shorter, as a result of a simplified architecture and more available data. The proposed method highlights the disadvantages of regular supervised training and demonstrates the benefits of a less traditional training approach.
Algorithms for flows over time with scheduling costs
Venue: arXiv cs.DS 2019
Year: 2019

Flows over time have received substantial attention from both an optimization and (more recently) a game-theoretic perspective. In this model, each arc has an associated delay for traversing the arc, and a bound on the rate of flow entering the arc; flows are time-varying. We consider a setting which is very standard within the transportation economic literature, but has received little attention from an algorithmic perspective. The flow consists of users who are able to choose their route but also their departure time, and who desire to arrive at their destination at a particular time, incurring a 'scheduling cost' if they arrive earlier or later. The total cost of a user is then a combination of the time they spend commuting, and the scheduling cost they incur. We present a combinatorial algorithm for the natural optimization problem, that of minimizing the average total cost of all users (i.e., maximizing the social welfare). Based on this, we also show how to set tolls so that this optimal flow is induced as an equilibrium of the underlying game.
Improved Approximation Algorithms for Inventory Problems
Venue: arXiv cs.DS 2019
Year: 2019

We give new approximation algorithms for the submodular joint replenishment problem and the inventory routing problem, using an iterative rounding approach. In both problems, we are given a set of $N$ items and a discrete time horizon of $T$ days in which given demands for the items must be satisfied. Ordering a set of items incurs a cost according to a set function, with properties depending on the problem under consideration. Demand for an item at time $t$ can be satisfied by an order on any day prior to $t$, but a holding cost is charged for storing the items during the intermediate period; the goal is to minimize the sum of the ordering and holding cost. Our approximation factor for both problems is $O(\log \log \min(N,T))$; this improves exponentially on the previous best results.
Hardware Versus Software Fault Injection of Modern Undervolted SRAMs
Venue: arXiv cs.PF 2019
Year: 2019

To improve power efficiency, researchers are experimenting with dynamically adjusting the supply voltage of systems below the nominal operating points. However, production systems are typically not allowed to function on voltage settings that is below the reliable limit. Consequently, existing software fault tolerance studies are based on fault models, which inject faults on random fault locations using fault injection techniques. In this work we study whether random fault injection is accurate to simulate the behavior of undervolted SRAMs. Our study extends the Gem5 simulator to support fault injection on the caches of the simulated system. The fault injection framework uses fault maps, which describe the faulty bits of SRAMs, as inputs. To compare random fault injection and hardware guided fault injection, we use two types of fault maps. The first type of maps are created through undervolting real SRAMs and observing the location of the erroneous bits, whereas the second type of maps are created by corrupting random bits of the SRAMs. During our study we corrupt the L1-Dcache of the simulated system and we monitor the behavior of the two types of fault maps on the resiliency of six benchmarks. The difference among the resiliency of a benchmark when tested with the different fault maps can be up to 24%.
Joint Resource and Admission Management for Slice-enabled Networks
Venue: arXiv cs.NI 2019
Year: 2019

Network slicing is a crucial part of the 5G networks that communication service providers (CSPs) seek to deploy. By exploiting three main enabling technologies, namely, software-defined networking (SDN), network function virtualization (NFV), and network slicing, communication services can be served to the end-users in an efficient, scalable, and flexible manner. To adopt these technologies, what is highly important is how to allocate the resources and admit the customers of the CSPs based on the predefined criteria and available resources. In this regard, we propose a novel joint resource and admission management algorithm for slice-enabled networks. In the proposed algorithm, our target is to minimize the network cost of the CSP subject to the slice requests received from the tenants corresponding to the virtual machines and virtual links constraints. Our performance evaluation of the proposed method shows its efficiency in managing CSP's resources.