Reviewerly/exHarmony huggingface dataset:
Reliable identification of redundant kernels for convolutional neural network compression

To compress deep convolutional neural networks (CNNs) with large memory footprint and long inference time, this paper proposes a novel pruning criterion based on layer-wise Ln-norms of feature maps to identify unimportant convolutional kernels. We calculate the Ln-norm of the feature map outputted by each convolutional kernel to evaluate the importance of the kernel. Furthermore, we use different Ln-norms for different layers, e.g., L1-norm for the first convolutional layer, L2-norm for middle convolutional layers and L∞-norm for the last convolutional layer. With the ability of accurately identifying unimportant convolutional kernels in each layer, the proposed method achieves a good balance between model size and inference accuracy. Experimental results on CIFAR, SVHN and ImageNet datasets and an application example in a railway intelligent surveillance system show that the proposed method outperforms existing kernel-norm-based methods and is generally applicable to any deep neural network with convolutional operations.
Fast Swept Volume Estimation with Deep Learning

Swept volume, the volume displaced by a moving object, is an ideal distance metric for sampling-based motion planning because it directly correlates to the amount of motion between two configurations. However, even approximate algorithms are computationally prohibitive. Our fundamental approach is the application of deep learning to efficiently estimate swept volume computation within a 5%–10% error for all robots tested, from rigid bodies to manipulators. However, even inference via the trained network can be computationally costly given the often hundreds of thousands of computations required by sampling-based motion planning. To address this, we demonstrate an efficient hierarchal approach for applying our trained estimator. This approach first pre-filters samples using a weighted Euclidean estimator trained via swept volume. Then, it selectively applies the deep neural network estimator. The first estimator, although less accurate, has metric space properties. The second estimator is a high-fidelity unbiased estimator without metric space properties. We integrate the hierarchical selection approach in both roadmap-based and a tree-based sampling motion planners. Empirical evaluation on the robot set demonstrates that hierarchal application of the metrics yields up to 5000 times faster planning than state of the art swept volume approximation and up to five times higher probability of finding a collision-free trajectory under a fixed time budget than the traditional Euclidean metric.
Graph Signal Representation with Wasserstein Barycenters

In many applications signals reside on the vertices of weighted graphs. Thus, there is the need to learn low dimensional representations for graph signals that will allow for data analysis and interpretation. Existing unsupervised dimensionality reduction methods for graph signals have focused on dictionary learning. In these works the graph is taken into consideration by imposing a structure or a parametrization on the dictionary and the signals are represented as linear combinations of the atoms in the dictionary. However, the assumption that graph signals can be represented using linear combinations of atoms is not always appropriate. In this paper we propose a novel representation framework based on non-linear and geometry-aware combinations of graph signals by leveraging the mathematical theory of Optimal Transport. We represent graph signals as Wasserstein barycenters and demonstrate through our experiments the potential of our proposed framework for low-dimensional graph signal representation.
Negotiation throughout flexible and interactive tradeoffs applied to construction procurement

A model for e-negotiation in a typical procurement process in the construction industry is presented. The greatest contribution of this paper is a novel model for e-negotiation using an adaptation of the FITradeoff method for group decision and negotiation with an unbiased mediator agent so as to reduce the number of interactions needed to reach an agreement. The elicitation process included within the negotiation model sees to it that the cognitive effort that the negotiators need to make is reduced because partial information and strict preference statements can be used. The mediator agent facilitates the negotiation phase, thereby allowing a general contractor to compare subcontractors and to decide if there should be a 1–1 or 1-N negotiation process. Moreover, this agent conducts the negotiation process and presents the best and suboptimal agreements to the negotiators. A prototype was built to test the model using data from a real construction project.
Optimized segmentation and multiscale emphasized feature extraction for traffic sign detection and recognition

Traffic sign detection and recognition has been a topic of research for at least the last two decades. Efforts are being made to reliably detect candidate traffic signs in natural uncontrolled environment and to recognize their contents. For detection, a large proportion of relevant literature disc usses color based segmentation by either sticking to a predefined color space (e.g., RGB, HSI, YCbCr etc.) or make use of empirically selected subset of eigen space to achieve partially data dependent segmentation. Since, the input RGB data for various color classes and the background is not linearly separable, none of the existing methods guarantee to achieve complete separation among pixels corresponding to traffic signs and the background objects. To tackle this problem, we propose a completely data driven segmentation technique that adaptively selects an optimized color space based on available training data. To recognize the contents of potential traffic signs, we present a hybrid spatio-frequency radial feature extraction technique with an emphasis on the regions containing useful information. We explore the energy compaction property of steerable discrete cosine transform for feature extraction and augment it with well known circular histogram of oriented gradients in a pyramid. Using our proposed method, experiments on (1) German Traffic Sign Detection Benchmark, (2) our self collected dataset and on a (3) hand crafted version of the combination of the two provide competitive performance compared to various latest and state of the art methods by achieving up to 0.978 precision and 0.98 recall values at an expense of only an insignificant additional computational cost. The method also obtained 0.81 precision on traffic signs partially occluded by other objects.
A combined approach using spatially-weighted principal components analysis and wavelet transformation for geochemical anomaly mapping in the Dashui ore-concentration district, Central China

Sometimes geochemical anomalies linked with buried mineral deposits are too weak to be recognized by conventional methods. In this study, element concentration data from a stream sediment survey were subjected to a combined method of SWPCA (spatially-weighted principal components analysis) and WT (wavelet transformation) to derive a geochemical anomaly model for epithermal Au deposits in the Dashui ore-concentration district. The SWPCA was applied as a data integration method to extract information related to mineralization in the geochemical data. The WT was applied as a powerful tool for recognizing mineralization related anomalies in a complex geochemical field and for enhancing weak anomalies from background. The SWPCA–WT geochemical anomaly model indicated high favorability values for the known mineral occurrences and out-performed PCA–WT and SWPCA models. The SWPCA–WT geochemical model generated in this study provides a robust guide for further gold exploration in the Dashui ore-concentration district.
The Semantic Snake Charmer Search Engine

The booming popularity of data science is also affecting high-tech industries. However, since these usually have different core competencies --- building cyber-physical systems rather than e.g. machine learning or data mining algorithms --- delving into data science by domain experts such as system engineers or architects might be more cumbersome than expected.
Multimodal Sensor Fusion in Single Thermal Image Super-Resolution

With the fast growth in the visual surveillance and security sectors, thermal infrared images have become increasingly necessary in a large variety of industrial applications. This is true even though IR sensors are still more expensive than their RGB counterpart having the same resolution. In this paper, we propose a deep learning solution to enhance the thermal image resolution. The following results are given: (I) Introduction of a multimodal, visual-thermal fusion model that addresses thermal image super-resolution, via integrating high-frequency information from the visual image. (II) Investigation of different network architecture schemes in the literature, their up-sampling methods, learning procedures, and their optimization functions by showing their beneficial contribution to the super-resolution problem. (III) A benchmark ULB17-VT dataset that contains thermal images and their visual images counterpart is presented. (IV) Presentation of a qualitative evaluation of a large test set with 58 samples and 22 raters which shows that our proposed model performs better against state-of-the-arts.
An approach integrating planning and image-based visual servo control for road following and moving obstacles avoidance

This paper proposes an approach that integrates planning and image based visual servo control for road following and moving obstacle avoidance. One main objective of this article is to represent a robot's general plan or strategy in the form of a finite state machine or automaton. This automaton is designed previously to execution of the plan and then it is used for any instance of the robot's task. The visual servo control is used to regulate the robot's velocity according to the visual target (task specification) which depends on the state in the automaton. All the algorithms and control laws have been implemented and simulation results and experiments with a real scale-size car-like robot are presented and discussed.
Quicker ADC : Unlocking the Hidden Potential of Product Quantization With SIMD

Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. A common approach is to rely on Product Quantization, which allows the storage of large vector databases in memory and efficient distance computations. Yet, implementations of nearest neighbor search with Product Quantization have their performance limited by the many memory accesses they perform. Following this observation, Andr\'e et al. proposed Quick ADC with up to $6\times$ faster implementations of $m\times{}4$ product quantizers (PQ) leveraging specific SIMD instructions. Quicker ADC is a generalization of Quick ADC not limited to $m\times{}4$ codes and supporting AVX-512, the latest revision of SIMD instruction set. In doing so, Quicker ADC faces the challenge of using efficiently 5,6 and 7-bit shuffles that do not align to computer bytes or words. To this end, we introduce (i) irregular product quantizers combining sub-quantizers of different granularity and (ii) split tables allowing lookup tables larger than registers. We evaluate Quicker ADC with multiple indexes including Inverted Multi-Indexes and IVF HNSW and show that it outperforms the reference optimized implementations (i.e., FAISS and polysemous codes) for numerous configurations. Finally, we release an open-source fork of FAISS enhanced with Quicker ADC at http://github.com/nlescoua/faiss-quickeradc.
Using Python as a toolbox for student-directed virtual labs

Python is a free, text-based programming language that has already been used by those who do physics research to great effect. But students can also learn to use it and, through its use, learn other aspects of physics—especially ones that are difficult or impossible to perform as physical labs. This article serves as a most-fundamental start for teachers who are interested in how they can use Python to enhance their physics instruction. Although there have been a few articles that have made references to using Python, none so far in this journal has described how quickly and effectively it can be used to simulate a virtual lab experience or aid in creative design. In this first of two articles, I introduce Python (and VPython) and provide some sample codes to guide beginners.
Quantifying weathering rind formation rates using in situ measurements of U-series isotopes with laser ablation and inductively coupled plasma-mass spectrometry

To quantify chemical weathering processes, it is essential to develop and utilize new geochemical tools that can provide information about chemical weathering in the field. U-series isotopes have emerged as a useful chronometer to directly constrain the rates and duration of chemical weathering. However, the conventional solution-based MC-ICPMS method involves a long and expensive sample processing procedure that restricts the numbers of measurements of samples by U-series analysis that can be completed. Here, we report in situ measurements of U-series disequilibria obtained with laser ablation (LA)-MC-ICPMS on weathering rinds collected from the tropical island of Basse-Terre in the archipelago of French Guadeloupe. We characterized two weathering rinds for U-series isotope compositions and elemental distributions with LA-MC-ICPMS and LA-Q-ICPMS. The in situ measurements of U-series disequilibria were consistent with the previous bulk measurements obtained by conventional solution MC-ICPMS despite the larger analytical uncertainties. The LA technique allowed a greater number of measurements that accelerated sample throughput and improved spatial resolution of measurement. The rind formation age, weathering rates, and U-series mobility parameters modeled in this study are comparable to the results from previous studies conducted on the same clasts, and also reveal new insights on rind formation such as the impact of micro-fractures on weathering history and U-series ratios. The improved spatial resolution available with LA Q-ICPMS helps distinguish between linear and power law rind thickness-age relationships that were unresolvable using conventional solution-based MC-ICPMS. In situ measurements with LA-Q-ICPMS in these weathering rinds also elucidates the sequences of mineral reactions during chemical weathering. The LA-Q-ICPMS maps of major and trace elements and elemental ratios reveal details about the rind formation processes at the weathering interfaces of clasts such as dissolution of primary phases, formation of new phases, development of porosity, and mobility behavior of U. This study demonstrates a new analytical method for determining weathering rates in rinds rapidly and accurately that can be used in a large number of rinds, providing key information at the clast scale.
Distributed Bottleneck-Aware Coflow Scheduling in Data Centers

With the booming development of data parallel frameworks, the coflow abstraction has been greatly favored by data center transport designs, for its prominent ability in capturing application-level semantics. To accelerate job completion, coflow completion time (CCT) is a most important metric, and coflow scheduling is the most effective and widely-adopted means of optimizing CCT. However, most existing coflow scheduling mechanisms neglect the ubiquitous in-network bottlenecks and schedule coflows based on non-blocking giant switch hyperthesis. Such a practice is likely to result in undesired link contention inside the fabric, finally impairing CCT performance. To address this problem, we propose the Distributed Bottleneck-Aware coflow scheduling algorithm called DBA, which approximates the minimum remaining time first (MRTF) heuristic on all fabric-wide links. In this way, core link bandwidths are allocated to coflows as expected and the CCT performance will not be violated. As an evolutionary algorithm, DBA enhances the traditional dual decomposition method thus converges to the optimal bandwidth allocation very fast. Extensive simulations verify DBA's outstanding CCT performance as well as high link utilization. Furthermore, DBA introduces very little overhead and is robust to routing strategies, parameter variations and computation delays.
Deep feature tracking based on interactive multiple model

Existing convolutional neural network (CNN) based trackers have limited tracking performance in some challenging scenarios such as deformation, background clutter and illumination variation, because the features extracted from a single layer or from a linear combination of multiple layers are insufficient to describe the target appearance. To overcome this problem, we propose a novel tracking algorithm based on interactive multiple model (IMM) framework for better exploring deep features from different layers (IMM_DFT). In this method, we first build measurement models from convolutional layers by applying correlation filters on hierarchical features. Then, to effectively estimate the target state for each layer, we design a hybrid system which consists of the foregoing measurement model and an online learning motion model. Finally, in order to achieve the optimal fusion of the systems for adapting diverse appearance variation of the target and background, an IMM estimator is developed to dynamically adjust the weight of each system using likelihood function and transition probabilities. Extensive experiments on OTB-2013, OTB-2015 and VOT-2016 benchmark databases demonstrate that the proposed algorithm achieves more favorable performance than several state-of-the-art methods.
Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory

Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that this algorithmic focus is myopic. In particular, since learning algorithms generally improve with more and better data, we propose shaping the feedback generation process as an alternate and complementary route to improving accuracy. To this effect, we explore how changes to the user interface can impact the quality and quantity of feedback data -- and therefore the learning accuracy. Motivated by information foraging theory, we study how feedback quality and quantity are influenced by interface design choices along two axes: information scent and information access cost. We present a user study of these interface factors for the common task of picking a movie to watch, showing that these factors can effectively shape and improve the implicit feedback data that is generated while maintaining the user experience.
CubeDMA – Optimizing three-dimensional DMA transfers for hyperspectral imaging applications

Onboard computing is one of the principal needs in space-related technology in the recent years. In particular, onboard hyperspectral imaging (HSI) processing has advanced significantly. Due to advances in sensor technology, onboard HSI processing continuously meets new challenges related to increasing dataset size, limited processing time and limited communication links. High throughput and data reduction are crucial for satisfying real-time constraint and for preserving transmission bandwidth. For systems capable of accommodating a wide range of processing algorithms, there is a need for a flexible communication infrastructure that can provide fast access to/from memory in different access patterns. In this paper, existing FPGA-related Direct Memory Access (DMA) solutions have been evaluated, and a new DMA solution tailored for hyperspectral images has been proposed. Results show that the proposed DMA core, CubeDMA, handles targeted memory access patterns in more efficient manner than existing solutions while being resource efficient.
A Semi-supervised Deep Generative Model for Human Body Analysis

Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such models is typically not interpretable, resulting in less flexible models. In this work, we adopt a structured semi-supervised approach and present a deep generative model for human body analysis where the body pose and the visual appearance are disentangled in the latent space. Such a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as pose-transfer without being explicitly trained for such a task. In addition, our setting allows for semi-supervised pose estimation, relaxing the need for labelled data. We demonstrate the capabilities of our generative model on the Human3.6M and on the DeepFashion datasets.
Discovering Correlations between Sparse Features in Distant Supervision for Relation Extraction

The recent art in relation extraction is distant supervision which generates training data by heuristically aligning a knowledge base with free texts and thus avoids human labelling. However, the concerned relation mentions often use the bag-of-words representation, which ignores inner correlations between features located in different dimensions and makes relation extraction less effective. To capture the complex characteristics of relation expression and tighten the correlated features, we attempt to discover and utilise informative correlations between features by the following four phases: 1) formulating semantic similarities between lexical features using the embedding method; 2) constructing generative relation for lexical features with different sizes of side windows; 3) computing correlation scores between syntactic features through a kernel-based method; and 4) conducting a distillation process for the obtained correlated feature pairs and integrating informative pairs with existing relation extraction models. The extensive experiments demonstrate that our method can effectively discover correlation information and improve the performance of state-of-the-art relation extraction methods.
Formally verified big step semantics out of x86-64 binaries

This paper presents a methodology for generating formally proven equivalence theorems between decompiled x86-64 machine code and big step semantics. These proofs are built on top of two additional contributions. First, a robust and tested formal x86-64 machine model containing small step semantics for 1625 instructions. Second, a decompilation-into-logic methodology supporting both x86-64 assembly and machine code at large scale. This work enables black-box binary verification, i.e., formal verification of a binary where source code is unavailable. As such, it can be applied to safety-critical systems that consist of legacy components, or components whose source code is unavailable due to proprietary reasons. The methodology minimizes the trusted code base by leveraging machine-learned semantics to build a formal machine model. We apply the methodology to several case studies, including binaries that heavily rely on the SSE2 floating-point instruction set, and binaries that are obtained by compiling code that is obtained by inlining assembly into C code.