Reviewerly/exHarmony huggingface dataset:
Multi-scale Discriminative Location-Aware Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation methods aim for predicting the regions of different object categories with only a few labeled samples. It is difficult to produce segmentation results with high accuracy when a new category appears. In this paper, we propose a Multi-scale Discriminative Location-aware (MDL) network to tackle the few-shot semantic segmentation problem. In order to use information from different levels, we first keep the last three convolutional layers of FCN, and then use the VGG-16 network to extract features from the support image-label pair, which adjusts the weight of the query image segmentation branch. Discriminative location-aware architecture can improve the efficiency of few-shot segmentation, and therefore the global average pooling layer is added to produce location feature information. Finally, we evaluate our MDL model on the Pascal VOC 2012 challenge, and show that it achieves competitive mIoU score compared to methods in recent years.
Towards Automatic Embedding Cost Learning for JPEG Steganography

Current mainstream methods for digital image steganography are content adaptive. That is, the secret messages are embedded in the complicated region in the cover image while minimizing the embedding distortion so as to suppress statistical detectability. Since there is already a practical encoding scheme for data embedding near the payload-distortion bound, the design of the embedding cost function becomes a deterministic part in steganography. Unlike the traditional heuristic hand-crafted method, this paper proposes a novel generative adversarial network based framework to automatically learn the embedding cost function for JPEG steganography. The proposed framework consists of a generator, a gradient-descent friendly inverse discrete cosine transformation module, an embedding simulator and a discriminator for steganalysis. Through training the generator and discriminator in alternation, the embedding cost function can finally be obtained by the trained generator. Experimental results demonstrate that our method can automatically learn a reasonable embedding cost function and achieve a satisfying performance.
Fast quantum state transfer in hybrid quantum dot-metal nanoparticle systems by shaping ultrafast laser pulses

Semiconductor quantum dots (SQDs) have relatively short decoherence times, but have the potential for extremely fast operations implemented by femto-second laser pulses, making them a possible candidate for very high speed quantum information processing.These operations are enhanced by positioning a metal nano-particle (MNP) near to the SQD which significantly increases the strength of electric fields applied to it.Here we consider an SQD with three-levels in a Lambda configuration, and explore the requirements for implementing a transfer from one ground state to the other via the upper level.A recently developed optimization method allows us to fix the energy of the laser pulse while satisfying the requirement that the pulse has no DC component.We show that the use of carefully shaped pulses significantly reduces the required laser power.We also determine the dependence of the pulse power on the distance between the SQD and the MNP, and explore the degree to which the process follows the pulse area theorem for first-order perturbative processes.
Erasure Coding for Distributed Matrix Multiplication for Matrices With Bounded Entries

Distributed matrix multiplication is widely used in several scientific domains. It is well recognized that computation times on distributed clusters are often dominated by the slowest workers (called stragglers). Recent work has demonstrated that straggler mitigation can be viewed as a problem of designing erasure codes. For matrices A and B, the technique essentially maps the computation of ATB into the multiplication of smaller (coded) submatrices. The stragglers are treated as erasures in this process. The computation can be completed as long as a certain number of workers (called the recovery threshold) complete their assigned tasks. We present a novel coding strategy for this problem when the absolute values of the matrix entries are sufficiently small. We demonstrate a tradeoff between the assumed absolute value bounds on the matrix entries and the recovery threshold. At one extreme, we are optimal with respect to the recovery threshold, and on the other extreme, we match the threshold of prior work. Experimental results on cloud-based clusters validate the benefits of our method.
In-Vehicle Occupancy Detection With Convolutional Networks on Thermal Images

Counting people is a growing field of interest for researchers in recent years. In-vehicle passenger counting is an interesting problem in this domain that has several applications including High Occupancy Vehicle (HOV) lanes. In this paper, present a new in-vehicle thermal image dataset. We propose a tiny convolutional model to count on-board passengers and compare it to well known methods. We show that our model surpasses state-of-the-art methods in classification and has comparable performance in detection. Moreover, our model outperforms the state-of-the-art architectures in terms of speed, making it suitable for deployment on embedded platforms. We present the results of multiple deep learning models and thoroughly analyze them.
Bidirectional Conditional Generative Adversarial Networks

Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples (x) conditioned on both latent variables (z) and known auxiliary information (c). We propose the Bidirectional cGAN (BiCoGAN), which effectively disentangles z and c in the generation process and provides an encoder that learns inverse mappings from x to both z and c, trained jointly with the generator and the discriminator. We present crucial techniques for training BiCoGANs, which involve an extrinsic factor loss along with an associated dynamically-tuned importance weight. As compared to other encoder-based cGANs, BiCoGANs encode c more accurately, and utilize z and c more effectively and in a more disentangled way to generate samples.
Image Projective Invariants

In this paper, we have proved the existence of projective moment invariants of images using finite combinations of weighted moments, with relative projective differential invariants as weight functions. We have given some instances constructed in that way, and analyzed possible issues could affect the performance. Some procedures are taken to estimate partial derivatives of discrete images, and a new method is designed to normalize the number of pixels for discrete images to minimize the changes before and after the projective transformation. We have carried out experiments using popular image databases and real images to test the performance. And the results show that the invariants proposed in this paper have better stability and discriminability than other previously used moment invariants in image retrieval and classification. Users can directly extract invariant features of images for a given planar object from different viewpoints without knowing the parameters of the 2D projective transformations. Therefore, the projective moment invariant could be potentially useful for planar object recognition, image description and classification.
Truck traffic monitoring with satellite images

The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads in many parts of the world are scarce. Many low- and middle-income countries have limited ground-based traffic monitoring and freight surveying activities. In this proof of concept, we show that we can use an object detection network to count trucks in satellite images and predict average annual daily truck traffic from those counts. We describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries.
Efficient training of interval Neural Networks for imprecise training data

This paper describes a robust and computationally feasible method to train and quantify the uncertainty of Neural Networks. Specifically, we propose a back propagation algorithm for Neural Networks with interval predictions. In order to maintain numerical stability we propose minimising the maximum of the batch of errors at each step. Our approach can accommodate incertitude in the training data, and therefore adversarial examples from a commonly used attack model can be trivially accounted for. We present results on a test function example, and a more realistic engineering test case. The reliability of the predictions of these networks is guaranteed by the non-convex Scenario approach to chance constrained optimisation, which takes place following training, and is hence robust to the performance of the optimiser. A key result is that, by using minibatches of size M, the complexity of the proposed approach scales as O(M⋅Niter), and does not depend upon the number of training data points as with other Interval Predictor Model methods. In addition, troublesome penalty function methods are avoided. To the authors' knowledge this contribution presents the first computationally feasible approach for dealing with convex set based epistemic uncertainty in huge datasets.
Machine Learning Predictive Maintenance on Data in the Wild

In this paper, we report on our real-word experiences in forecasting machine downtime based on real-time predictions of imminent failures. Predictions are based on the use of a machine learning classification algorithm trained on historical machine data. This is constrained by the available sensor equipment. We report on our recent collaborative work with a machine builder of premium printing equipment for purposes of predictive maintenance. We describe our data analytics approach with a view towards processing unstructured data, show initial results, discuss issues and lessons learnt.
POLARIS: Probabilistic and Ontological Activity Recognition in Smart-Homes

Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications.Most activity recognition systems rely on supervised learning to extract activity models from labeled datasets.A problem with that approach is the acquisition of comprehensive activity datasets, which is an expensive task.The problem is particularly challenging when focusing on complex ADLs characterized by large variability of execution.Moreover, several activity recognition systems are limited to offline recognition, while many applications claim for online activity recognition.In this paper, we propose POLARIS, a framework for unsupervised activity recognition.POLARIS can recognize complex ADLs exploiting the semantics of activities, context data, and sensors.Through ontological reasoning, our algorithm derives semantic correlations among activities and sensor events.By matching observed events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities.Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology.Our system supports online recognition, thanks to a novel segmentation algorithm.Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of supervised approaches.Moreover, the online version of our system achieves essentially the same accuracy of the offline version.
An efficient framework for unsupervised feature selection

In these years, the task of fast unsupervised feature selection attracts much attentions with the increasing number of data collected from the physical world. To speed up the running time of algorithms, the bipartite graph theory has been applied in many large-scale tasks, including fast clustering, fast feature extraction, etc. Inspired by this, we present a novel bipartite graph based fast feature selection approach named Efficient Unsupervised Feature Selection (EUFS). Compared to the existing methods focusing on the same topic, EUFS is advanced in two aspects: (1) we learn a high-quality discrete indicator matrix for these unlabelled data by virtue of bipartite graph based spectral clustering, instead of obtaining an implicit cluster structure matrix; (2) we learn a row-sparse matrix for evaluating features via a generalized uncorrelated regression model supervised by the achieved indicator matrix, which succeeds in exploring the discriminative and uncorrelated features. Correspondingly, the features selected by our model could achieve an excellent clustering or classification performance while maintaining a low computational complexity. Experimentally, the results of EUFS compared to five state-of-the-art algorithms and one baseline on ten benchmark datasets verifies its efficiency and superiority.
A robust background initialization algorithm with superpixel motion detection

Scene background initialization allows the recovery of a clear image without foreground objects from a video sequence, which is generally the first step in many computer vision and video processing applications. The process may be strongly affected by some challenges such as illumination changes, foreground cluttering, intermittent movement, etc. In this paper, a robust background initialization approach based on superpixel motion detection is proposed. Both spatial and temporal characteristics of frames are adopted to effectively eliminate foreground objects. A subsequence with stable illumination condition is first selected for background estimation. Images are segmented into superpixels to preserve spatial texture information and foreground objects are eliminated by superpixel motion filtering process. A low-complexity density-based clustering is then performed to generate reliable background candidates for final background determination. The approach has been evaluated on SBMnet dataset and it achieves a performance superior or comparable to other state-of-the-art works with faster processing speed. Moreover, in those complex and dynamic categories, the algorithm produces the best results showing the robustness against very challenging scenarios.
Linear transformations for cross-lingual semantic textual similarity

Cross-lingual semantic textual similarity systems estimate the degree of the meaning similarity between two sentences, each in a different language. State-of-the-art algorithms usually employ machine translation and combine vast amount of features, making the approach strongly supervised, resource rich, and difficult to use for poorly-resourced languages. In this paper, we study linear transformations, which project monolingual semantic spaces into a shared space using bilingual dictionaries. We propose a novel transformation, which builds on the best ideas from prior works. We experiment with unsupervised techniques for sentence similarity based only on semantic spaces and we show they can be significantly improved by the word weighting. Our transformation outperforms other methods and together with word weighting leads to very promising results on several datasets in different languages.
No-reference image quality assessment with visual pattern degradation

The technology of image quality assessment (IQA) is urgently demanded in perceptual-orientated image processing systems. Due to the lack of guidance from the reference information, it is challenging for the no-reference (NR) IQA to perform consistently with the human perception. It is well known that even though there exists no reference information, the human visual system can still effectively predict the image quality under the guidance of the prior knowledge. Thus, we try to build a visual content based prior knowledge database to guide the NR IQA. Cognitive researches state that the primary visual cortex presents substantially orientation selectivity, within which the structural information is extracted for visual perception. Inspired by this, the correlations among pixels in a local region are firstly analyzed for structure extraction. By mimicking the excitatory/inhibitory interactions among neurons, the relationships among pixels are estimated as their orientation similarities. Next, by aligning all of the correlations in a local region, a novel orientation similarity based pattern (OSP) is designed. Moreover, by merging the similar patterns with the clustering procedure, a set of fundamental OSPs are acquired. And then, the visual structure degradations are analyzed on these fundamental OSPs, and a prior knowledge database is learned. With the guidance of the knowledge database, a new visual pattern degradation (VPD) based NR-IQA model (NRVPD) is built. Finally, the performance of the proposed NRVPD model is verified on large benchmark IQA databases, and the experimental results demonstrate that the NRVPD performs highly consistent with the subjective perception.
A general private information retrieval scheme for MDS coded databases with colluding servers

The problem of private information retrieval (PIR) gets renewed attentions in recent years due to its information-theoretic reformulation and applications in distributed storage systems. Let M files be stored in a distributed storage system consisting of N servers, where each file is stored via an (N, K)-MDS code. A PIR scheme will allow a user to retrieve a specific file from the distributed storage system without revealing the identity of the file to any T colluding servers. PIR rate is defined as the number of symbols privately retrieved per one downloaded symbol and the supremum of all achievable rates is called the PIR capacity. The capacity has been solved for some degenerate cases, i.e. $$K=1$$ or $$T=1$$ . For the general case $$K\ge 2$$ and $$T\ge 2$$ , the exact PIR capacity remains unknown. In this paper we propose a general private information retrieval scheme for MDS coded databases with colluding servers achieving PIR rate $$(1+R+R^2+\cdots +R^{M-1})$$ , where $$R=1-\frac{{{N-T}\atopwithdelims ()K}}{{N\atopwithdelims ()K}}$$ . Our scheme captures the essence of the optimal schemes for degenerate cases. We also compare our scheme with some other known PIR schemes for non-degenerate cases. The advantages of our scheme include its independence of the property of the storage code and a better performance when the number of files M is small.
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision and machine learning.Unfortunately, state-of-the-art networks are extremely compute and memory intensive which makes them unsuitable for mW-devices such as IoT end-nodes.Aggressive quantization of these networks dramatically reduces the computation and memory footprint.Binary-weight neural networks (BWNs) follow this trend, pushing weight quantization to the limit.Hardware accelerators for BWNs presented up to now have focused on core efficiency, disregarding I/O bandwidth and system-level efficiency that are crucial for deployment of accelerators in ultra-low power devices.We present Hyperdrive: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can be used for arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level by arranging Hyperdrive chips systolically in a 2D mesh while processing the entire feature map together in parallel.Hyperdrive achieves 4.3 TOp/s/W systemlevel efficiency (i.e., including I/Os)-3.1× higher than state-ofthe-art BWN accelerators, even if its core uses resource-intensive FP16 arithmetic for increased robustness.
Intraoperative Computed Tomography and Finite Element Modelling for Multimodal Image Fusion in Brain Surgery

Abstract BACKGROUND intraoperative computer tomography (iCT) and advanced image fusion algorithms could improve the management of brainshift and the navigation accuracy. OBJECTIVE To evaluate the performance of an iCT-based fusion algorithm using clinical data. METHODS Ten patients with brain tumors were enrolled; preoperative MRI was acquired. The iCT was applied at the end of microsurgical resection. Elastic image fusion of the preoperative MRI to iCT data was performed by deformable fusion employing a biomechanical simulation based on a finite element model. Fusion accuracy was evaluated: the target registration error (TRE, mm) was measured for rigid and elastic fusion (R f and E f ) and anatomical landmark pairs were divided into test and control structures according to distinct involvement by the brainshift. Intraoperative points describing the stereotactic position of the brain were also acquired and a qualitative evaluation of the adaptive morphing of the preoperative MRI was performed by 5 observers. RESULTS The mean TRE for control and test structures with R f was 1.81 ± 1.52 and 5.53 ± 2.46 mm, respectively. No significant change was observed applying E f to control structures; the test structures showed reduced TRE values of 3.34 ± 2.10 mm after E f ( P &lt; .001). A 32% average gain (range 9%-54%) in accuracy of image registration was recorded. The morphed MRI showed robust matching with iCT scans and intraoperative stereotactic points. CONCLUSIONS The evaluated method increased the registration accuracy of preoperative MRI and iCT data. The iCT-based non-linear morphing of the preoperative MRI can potentially enhance the consistency of neuronavigation intraoperatively.
Predicting Solar Irradiance Using Machine Learning Techniques

Machine learning is applied in almost every field of knowledge in recent years. Renewable energy domain now extensively uses machine learning to provide farmers and grid operators with proper and timely information about solar irradiance prediction for future planning. Connecting grids with solar energy systems has brought unprecedented shift in the power sector. Nevertheless, the intervention poses challenges including intermittency that affects the overall energy supply and demand management. Our proposed solution intends to make prediction models using Machine Learning techniques (Linear Regression, Regression Trees and Support Vector Machine) which uses the past weather data including the parameters such as temperature, wind speed, atmospheric pressure, and humidity and predicts solar irradiance for the future. The model can help the grid operators for improved supply and demand management.
A Kinematic Chain Space for Monocular Motion Capture

This paper deals with motion capture of kinematic chains (e.g. human skeletons) from monocular image sequences taken by uncalibrated cameras. We present a method based on projecting an observation onto a kinematic chain space (KCS). An optimization of the nuclear norm is proposed that implicitly enforces structural properties of the kinematic chain. Unlike other approaches our method is not relying on training data or previously determined constraints such as particular body lengths. The proposed algorithm is able to reconstruct scenes with little or no camera motion and previously unseen motions. It is not only applicable to human skeletons but also to other kinematic chains for instance animals or industrial robots. We achieve state-of-the-art results on different benchmark databases and real world scenes.
Rao-Blackwellized Stochastic Gradients for Discrete Distributions

We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories. When $K$ is indeed infinite, or finite but very large, the relevant summation is intractable. Accordingly, various stochastic gradient estimators have been proposed. In this paper, we describe a technique that can be applied to reduce the variance of any such estimator, without changing its bias---in particular, unbiasedness is retained. We show that our technique is an instance of Rao-Blackwellization, and we demonstrate the improvement it yields on a semi-supervised classification problem and a pixel attention task.