OpenRaiser/Intern-Atlas huggingface dataset:
MixTrain: Scalable Training of Verifiably Robust Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks. The most promising defenses, adversarially robust training and verifiably robust training, have limitations that restrict their practical applications. The adversarially robust training only makes the networks robust against a subclass of attackers and we reveal such weaknesses by developing a new attack based on interval gradients. By contrast, verifiably robust training provides protection against any L-p norm-bounded attacker but incurs orders of magnitude more computational and memory overhead than adversarially robust training. We propose two novel techniques, stochastic robust approximation and dynamic mixed training, to drastically improve the efficiency of verifiably robust training without sacrificing verified robustness. We leverage two critical insights: (1) instead of over the entire training set, sound over-approximations over randomly subsampled training data points are sufficient for efficiently guiding the robust training process; and (2) We observe that the test accuracy and verifiable robustness often conflict after certain training epochs. Therefore, we use a dynamic loss function to adaptively balance them for each epoch. We designed and implemented our techniques as part of MixTrain and evaluated it on six networks trained on three popular datasets including MNIST, CIFAR, and ImageNet-200. Our evaluations show that MixTrain can achieve up to $95.2\%$ verified robust accuracy against $L_\infty$ norm-bounded attackers while taking $15$ and $3$ times less training time than state-of-the-art verifiably robust training and adversarially robust training schemes, respectively. Furthermore, MixTrain easily scales to larger networks like the one trained on ImageNet-200, significantly outperforming the existing verifiably robust training methods.
Printable Aggregate Elements
Venue: arXiv cs.GR 2018
Year: 2018

Aggregating base elements into rigid objects such as furniture or sculptures is a great way for designers to convey a specific look and feel. Unfortunately, there is no existing solution to help model structurally sound aggregates. The challenges stem from the fact that the final shape and its structural properties emerge from the arrangements of the elements, whose sizes are large so that they remain easily identifiable. Therefore there is a very tight coupling between the object shape, structural properties, and the precise layout of the elements. We present the first method to create aggregates of elements that are structurally sound and can be manufactured on 3D printers. Rather than having to assemble an aggregate shape by painstakingly positioning elements one by one, users of our method only have to describe the structural purpose of the desired object. This is done by specifying a set of external forces and attachment points. The algorithm then automatically optimizes a layout of user-provided elements that answers the specified scenario. The elements can have arbitrary shapes: convex, concave, elongated, and can be allowed to deform. Our approach creates connections between elements through small overlaps preserving their appearance, while optimizing for the global rigidity of the resulting aggregate. We formulate a topology optimization problem whose design variables are the positions and orientations of individual elements. Global rigidity is maximized through a dedicated gradient descent scheme. Due to the challenging setting -- number of elements, arbitrary shapes, orientation, and constraints in 3D -- we propose several novel steps to achieve convergence.
Vehicle Tracking Using Surveillance with Multimodal Data Fusion
Venue: arXiv cs.CV 2018
Year: 2018

Vehicle location prediction or vehicle tracking is a significant topic within connected vehicles. This task, however, is difficult if only a single modal data is available, probably causing bias and impeding the accuracy. With the development of sensor networks in connected vehicles, multimodal data are becoming accessible. Therefore, we propose a framework for vehicle tracking with multimodal data fusion. Specifically, we fuse the results of two modalities, images and velocity, in our vehicle-tracking task. Images, being processed in the module of vehicle detection, provide direct information about the features of vehicles, whereas velocity estimation can further evaluate the possible location of the target vehicles, which reduces the number of features being compared, and decreases the time consumption and computational cost. Vehicle detection is designed with a color-faster R-CNN, which takes both the shape and color of the vehicles into consideration. Meanwhile, velocity estimation is through the Kalman filter, which is a classical method for tracking. Finally, a multimodal data fusion method is applied to integrate these outcomes so that vehicle-tracking tasks can be achieved. Experimental results suggest the efficiency of our methods, which can track vehicles using a series of surveillance cameras in urban areas.
Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic tasks on pulmonary disease except for musculoskeletal system. Current conventional method for acquisition of bone suppressed X-rays is dual energy imaging, which captures two radiographs at a very short interval with different energy levels; however, the patient is exposed to radiation twice and the artifacts arise due to heartbeats between two shots. In this paper, we introduce a deep generative model trained to predict bone suppressed images on single energy chest X-rays, analyzing a finite set of previously acquired dual energy chest X-rays. Since the relatively small amount of data is available, such approach relies on the methodology maximizing the data utilization. Here we integrate the following two approaches. First, we use a conditional generative adversarial network that complements the traditional regression method minimizing the pairwise image difference. Second, we use Haar 2D wavelet decomposition to offer a perceptual guideline in frequency details to allow the model to converge quickly and efficiently. As a result, we achieve state-of-the-art performance on bone suppression as compared to the existing approaches with dual energy chest X-rays.
Demystifying Neural Network Filter Pruning
Venue: arXiv cs.CV 2018
Year: 2018

Based on filter magnitude ranking (e.g. L1 norm), conventional filter pruning methods for Convolutional Neural Networks (CNNs) have been proved with great effectiveness in computation load reduction. Although effective, these methods are rarely analyzed in a perspective of filter functionality. In this work, we explore the filter pruning and the retraining through qualitative filter functionality interpretation. We find that the filter magnitude based method fails to eliminate the filters with repetitive functionality. And the retraining phase is actually used to reconstruct the remained filters for functionality compensation for the wrongly-pruned critical filters. With a proposed functionality-oriented pruning method, we further testify that, by precisely addressing the filter functionality redundancy, a CNN can be pruned without considerable accuracy drop, and the retraining phase is unnecessary.
Building Corpora for Single-Channel Speech Separation Across Multiple Domains
Venue: arXiv cs.CL 2018
Year: 2018

To date, the bulk of research on single-channel speech separation has been conducted using clean, near-field, read speech, which is not representative of many modern applications. In this work, we develop a procedure for constructing high-quality synthetic overlap datasets, necessary for most deep learning-based separation frameworks. We produced datasets that are more representative of realistic applications using the CHiME-5 and Mixer 6 corpora and evaluate standard methods on this data to demonstrate the shortcomings of current source-separation performance. We also demonstrate the value of a wide variety of data in training robust models that generalize well to multiple conditions.
Distilling Critical Paths in Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Neural network compression and acceleration are widely demanded currently due to the resource constraints on most deployment targets. In this paper, through analyzing the filter activation, gradients, and visualizing the filters' functionality in convolutional neural networks, we show that the filters in higher layers learn extremely task-specific features, which are exclusive for only a small subset of the overall tasks, or even a single class. Based on such findings, we reveal the critical paths of information flow for different classes. And by their intrinsic property of exclusiveness, we propose a critical path distillation method, which can effectively customize the convolutional neural networks to small ones with much smaller model size and less computation.
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we propose an actor ensemble algorithm, named ACE, for continuous control with a deterministic policy in reinforcement learning. In ACE, we use actor ensemble (i.e., multiple actors) to search the global maxima of the critic. Besides the ensemble perspective, we also formulate ACE in the option framework by extending the option-critic architecture with deterministic intra-option policies, revealing a relationship between ensemble and options. Furthermore, we perform a look-ahead tree search with those actors and a learned value prediction model, resulting in a refined value estimation. We demonstrate a significant performance boost of ACE over DDPG and its variants in challenging physical robot simulators.
GNSS Time Synchronization in Vehicular Ad-Hoc Networks: Benefits and Feasibility
Venue: arXiv cs.NI 2018
Year: 2018

Time synchronization is critical for the operation of distributed systems in networked environments. It is also demanded in vehicular ad-hoc networks (VANETs), which, as a special type of wireless networks, are becoming increasingly important for emerging cooperative intelligent transport systems. Global navigation satellite system (GNSS) is a proven technology to provide precise timing information in many distributed systems. It is well recognized to be the primary means for vehicle positioning and velocity determination in VANETs. However, GNSS-based time synchronization is not well understood for its role in the coordination of various tasks in VANETs. To address this issue, this paper examines the requirements, potential benefits, and feasibility of GNSS time synchronization in VANETs. The availability of GNSS time synchronization is characterized by almost 100% in our experiments in high-rise urban streets, where the availability of GNSS positioning solutions is only 80%. Experiments are also conducted to test the accuracy of time synchronization with 1-PPS signals output from consumer grade GNSS receivers. They have shown 30-ns synchronization accuracy between two receivers of different models. All these experimental results demonstrate the feasibility of GNSS time synchronization for stringent VANET applications.
A Method for Ontology-based Architecture Reconstruction of Computing Platforms
Venue: arXiv cs.CR 2018
Year: 2018

Today's ubiquitous computing ecosystem involves various kinds of hardware and software technologies for different computing environments. As the result, computing systems can be seen as integrated system of hardware and software systems. Realizing such complex systems is crucial for providing safety, security, and maintenance. This is while the characterization of computing systems is not possible without a systematic procedure for enumerating different components and their structural/behavioral relationships. Architecture Reconstruction (AR) is a practice defined in the domain of software engineering for the realization of a specific software component. However, it is not applicable to a whole system (including HW/SW). Inspired by Symphony AR framework, we have proposed a generalized method to reconstruct the architecture of a computing platform at HW/SW boundary. In order to cover diverge set of existing HW/SW technologies, our method uses an ontology-based approach to handle these complexities. Due to the lack of a comprehensive accurate ontology in the literature, we have developed our own ontology -- called PLATOnt -- which is shown to be more effective by ONTOQA evaluation framework. We have used our AR method in two use case scenarios to reconstruct the architecture of ARM-based Trusted execution environment and a Raspberry-pi platform have extensive application in embedded systems and IoT devices.
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
Venue: arXiv cs.LG 2018
Year: 2018

Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate the risk of new AKI onset. We generated models from clinical notes within the first 24 hours following intensive care unit (ICU) admission extracted from Medical Information Mart for Intensive Care III (MIMIC-III). From the clinical notes, we generated clinically meaningful word and concept representations and embeddings, respectively. Five supervised learning classifiers and knowledge-guided deep learning architecture were used to construct prediction models. The best configuration yielded a competitive AUC of 0.779. Our work suggests that natural language processing of clinical notes can be applied to assist clinicians in identifying the risk of incident AKI onset in critically ill patients upon admission to the ICU.
A new exact approach for the Bilevel Knapsack with Interdiction Constraints
Venue: arXiv cs.GT 2018
Year: 2018

We consider the Bilevel Knapsack with Interdiction Constraints, an extension of the classic 0-1 knapsack problem formulated as a Stackelberg game with two agents, a leader and a follower, that choose items from a common set and hold their own private knapsacks. First, the leader selects some items to be interdicted for the follower while satisfying a capacity constraint. Then the follower packs a set of the remaining items according to his knapsack constraint in order to maximize the profits. The goal of the leader is to minimize the follower's profits. The presence of two decision levels makes this problem very difficult to solve in practice: the current state-of-the-art algorithms can solve to optimality instances with 50-55 items at most. We derive effective lower bounds and present a new exact approach that exploits the structure of the induced follower's problem. The approach successfully solves all benchmark instances within one second in the worst case and larger instances with up to 500 items within 60 seconds.
Instance Retrieval at Fine-grained Level Using Multi-Attribute Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present a method for instance ranking and retrieval at fine-grained level based on the global features extracted from a multi-attribute recognition model which is not dependent on landmarks information or part-based annotations. Further, we make this architecture suitable for mobile-device application by adopting the bilinear CNN to make the multi-attribute recognition model smaller (in terms of the number of parameters). The experiments run on the Dress category of DeepFashion In-Shop Clothes Retrieval and CUB200 datasets show that the results of instance retrieval at fine-grained level are promising for these datasets, specially in terms of texture and color.
Reachability Analysis of Pushdown Systems with an Upper Stack
Venue: arXiv cs.FL 2018
Year: 2018

Pushdown systems (PDSs) are a natural model for sequential programs, but they can fail to accurately represent the way an assembly stack actually operates. Indeed, one may want to access the part of the memory that is below the current stack or base pointer, hence the need for a model that keeps track of this part of the memory. To this end, we introduce pushdown systems with an upper stack (UPDSs), an extension of PDSs where symbols popped from the stack are not destroyed but instead remain just above its top, and may be overwritten by later push rules. We prove that the sets of successors post* and predecessors pre* of a regular set of configurations of such a system are not always regular, but that post* is context-sensitive, so that we can decide whether a single configuration is forward reachable or not. In order to under-approximate pre* in a regular fashion, we consider a bounded-phase analysis of UPDSs, where a phase is a part of a run during which either push or pop rules are forbidden. We then present a method to over-approximate post* that relies on regular abstractions of runs of UPDSs. Finally, we show how these approximations can be used to detect stack overflows and stack pointer manipulations with malicious intent.
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

There exists a plethora of techniques for inducing structured sparsity in parametric models during the optimization process, with the final goal of resource-efficient inference. However, few methods target a specific number of floating-point operations (FLOPs) as part of the optimization objective, despite many reporting FLOPs as part of the results. Furthermore, a one-size-fits-all approach ignores realistic system constraints, which differ significantly between, say, a GPU and a mobile phone -- FLOPs on the former incur less latency than on the latter; thus, it is important for practitioners to be able to specify a target number of FLOPs during model compression. In this work, we extend a state-of-the-art technique to directly incorporate FLOPs as part of the optimization objective and show that, given a desired FLOPs requirement, different neural networks can be successfully trained for image classification.
Chance Constrained Motion Planning for High-Dimensional Robots
Venue: arXiv cs.RO 2018
Year: 2018

This paper introduces Probabilistic Chekov (p-Chekov), a chance-constrained motion planning system that can be applied to high degree-of-freedom (DOF) robots under motion uncertainty and imperfect state information. Given process and observation noise models, it can find feasible trajectories which satisfy a user-specified bound over the probability of collision. Leveraging our previous work in deterministic motion planning which integrated trajectory optimization into a sparse roadmap framework, p-Chekov shows superiority in its planning speed for high-dimensional tasks. P-Chekov incorporates a linear-quadratic Gaussian motion planning approach into the estimation of the robot state probability distribution, applies quadrature theories to waypoint collision risk estimation, and adapts risk allocation approaches to assign allowable probabilities of failure among waypoints. Unlike other existing risk-aware planners, p-Chekov can be applied to high-DOF robotic planning tasks without the convexification of the environment. The experiment results in this paper show that this p-Chekov system can effectively reduce collision risk and satisfy user-specified chance constraints in typical real-world planning scenarios for high-DOF robots.
Classification of cyber-physical production systems applications: Proposition of an analysis framework
Venue: arXiv cs.SE 2018
Year: 2018

Cyber-physical systems have encountered a huge success in the past decade in several scientific communities, and specifically in production topics. The main attraction of the concept relies in the fact that it encompasses many scientific topics that were distinct before. The downside is the lack of readability of the current developments about cyber-physical production systems (CPPS). Indeed, the large scientific area of CPPS makes it difficult to identify clearly and rapidly, in the various applications that were made of CPPS, what are the choices, best practices and methodology that are suggested and that could be used for a new application. This work intends to introduce an analysis framework able to classify those developments. An extensive study of literature enabled to extract the major criteria that are to be used in the framework, namely: Development Extent; Research Axis; Instrumenting; Communication standards; Intelligence deposit; Cognition level; Human factor. Several recent examples of CPPS developments in literature are used to illustrate the use of the framework and brief conclusions are drawn from the comparative analysis of those examples.
Decision Procedures for Path Feasibility of String-Manipulating Programs with Complex Operations
Venue: arXiv cs.FL 2018
Year: 2018

The design and implementation of decision procedures for checking path feasibility in string-manipulating programs is an important problem, whose applications include symbolic execution and automated detection of cross-site scripting (XSS) vulnerabilities. A (symbolic) path is a finite sequence of assignments and assertions (i.e. without loops), and checking its feasibility amounts to determining the existence of inputs that yield a successful execution. We give two general semantic conditions which together ensure the decidability of path feasibility: (1) each assertion admits regular monadic decomposition, and (2) each assignment uses a (possibly nondeterministic) function whose inverse relation preserves regularity. We show these conditions are expressive since they are satisfied by a multitude of string operations. They also strictly subsume existing decidable string theories, and most existing benchmarks (e.g. most of Kaluza's, and all of SLOG's, Stranger's, and SLOTH's). We give a simple decision procedure and an extensible architecture of a string solver in that a user may easily incorporate his/her own string functions. We show the general fragment has a tight, but high complexity. To address this, we propose to allow only partial string functions (i.e., prohibit nondeterminism) in condition (2). When nondeterministic functions are needed, we also provide a syntactic fragment that provides a support of nondeterministic functions but can be reduced to an existing solver SLOTH. We provide an efficient implementation of our decision procedure for deterministic partial string functions in a new string solver OSTRICH. It provides built-in support for concatenation, reverse, functional transducers, and replaceall and provides a framework for extensibility to support further string functions. We demonstrate the efficacy of our new solver against other competitive solvers.
Attention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Support
Venue: arXiv cs.CL 2018
Year: 2018

Customer support is a central objective at Square as it helps us build and maintain great relationships with our sellers. In order to provide the best experience, we strive to deliver the most accurate and quasi-instantaneous responses to questions regarding our products. In this work, we introduce the Attention Fusion Network model which combines signals extracted from seller interactions on the Square product ecosystem, along with submitted email questions, to predict the most relevant solution to a seller's inquiry. We show that the innovative combination of two very different data sources that are rarely used together, using state-of-the-art deep learning systems outperforms, candidate models that are trained only on a single source.