Reviewerly/exHarmony huggingface dataset:
HPIPE: Heterogeneous Layer-Pipelined and Sparse-Aware CNN Inference for FPGAs

This poster presents a novel cross-layer-pipelined Convolutional Neural Network accelerator architecture, and network compiler, that make use of precision minimization and parameter pruning to fit ResNet-50 entirely into on-chip memory on a Stratix 10 2800 FPGA. By statically partitioning the hardware across each of the layers in the network, our architecture enables full DSP utilization and reduces the soft logic per DSP ratio by roughly 4x over prior work on sparse CNN accelerators for FPGAs. This high DSP utilization, a frequency of 420MHz, and skipping zero weights enable our architecture to execute a sparse ResNet-50 model at a batch size of 1 at 3300 images/s, which is nearly 3x higher throughput than NVIDIA's fastest machine learning targeted GPU, the V100. We also present a network compiler and a flexible hardware interface that make it easy to add support for new types of neural networks, and to optimize these networks for FPGAs with different on-chip resources.
Application of artificially intelligent systems for the identification of discrete fossiliferous levels

The separation of discrete fossiliferous levels within an archaeological or paleontological site with no clear stratigraphic horizons has historically been carried out using qualitative approaches, relying on two-dimensional transversal and longitudinal projection planes. Analyses of this type, however, can often be conditioned by subjectivity based on the perspective of the analyst. This study presents a novel use of Machine Learning algorithms for pattern recognition techniques in the automated separation and identification of fossiliferous levels. This approach can be divided into three main steps including: (1) unsupervised Machine Learning for density based clustering (2) expert-in-the-loop Collaborative Intelligence Learning for the integration of geological data followed by (3) supervised learning for the final fine-tuning of fossiliferous level models. For evaluation of these techniques, this method was tested in two Late Miocene sites of the Batallones Butte paleontological complex (Madrid, Spain). Here we show Machine Learning analyses to be a valuable tool for the processing of spatial data in an efficient and quantitative manner, successfully identifying the presence of discrete fossiliferous levels in both Batallones-3 and Batallones-10. Three discrete fossiliferous levels have been identified in Batallones-3, whereas another three have been differentiated in Batallones-10.
Rain Detection and Removal via Shrinkage-based Sparse Coding and Learned Rain Dictionary

Rain removal is essential for achieving autonomous driving because it preserves the details of objects that are useful for feature extraction and removes the rain structures that hinder feature extraction. Based on a linear superposition model in which the observed rain image is decomposed into two layers, a rain layer and a non-rain layer, conventional rain removal methods estimate these two layers alternatively from an observed single image based on prior modeling. However, the prior knowledge used for the rain structures is not always correct because various types of rain structures can be observed in the rain images, which results in inaccurate rain removal. Therefore, in this article, a novel rain removal method based on the use of a scribbled rain image set and a new shrinkage-based sparse coding model is proposed. The scribbled rain images have information about which pixels have rain structures. Thus, various types of rain structures can be modeled, owing to the abundance of rain structures in the rain image set. To detect the rain regions, two types of approaches, one based on reconstruction error comparison (REC) via a learned rain dictionary and the other based on a deep convolutional neural network (DCNN), are presented. With the rain regions, the proposed shrinkage-based sparse coding model determines how much to reduce the sparse codes of the rain dictionary and maintain the sparse codes of the non-rain dictionary for accurate rain removal. Experimental results verified that the proposed shrinkage-based sparse coding model could remove rain structures and preserve objects&#x2019; details due to the REC- or DCNN-based rain detection using the scribbled rain image set. Moreover, it was confirmed that the proposed method is more effective at removing rain structures from similar objects&#x2019; structures than conventional methods.
Train Scheduling with Hybrid Answer Set Programming

Abstract We present a solution to real-world train scheduling problems, involving routing, scheduling, and optimization, based on Answer Set Programming (ASP). To this end, we pursue a hybrid approach that extends ASP with difference constraints to account for a fine-grained timing. More precisely, we exemplarily show how the hybrid ASP system clingo [DL] can be used to tackle demanding planning and scheduling problems. In particular, we investigate how to boost performance by combining distinct ASP solving techniques, such as approximations and heuristics, with preprocessing and encoding techniques for tackling large-scale, real-world train-scheduling instances.
DCGANs for realistic breast mass augmentation in x-ray mammography

Early detection has a major contribution to the curability of breast cancer, and using mammographic images, this can be achieved non-invasively. Supervised deep learning, the dominant computer-aided detection (CADe) tool currently, has played a great role in object detection in computer vision, but it suffers from a limiting property: the need of a large amount of labelled data. This becomes stricter when it comes to medical datasets which require high-cost and time-consuming annotations. Furthermore, medical datasets are usually imbalanced, a condition that often hinders classifiers performance. The aim of this paper is to learn the distribution of the minority class to synthesise new samples in order to improve lesion detection in mammography. Deep Convolutional Generative Adversarial Networks (DCGANs) can efficiently generate breast masses. They were trained on increasing-size subsets of a mammographic dataset and used to generate diverse and realistic breast masses. The effect of including the generated images and/or applying horizontal and vertical flipping was tested in an environment where an imbalanced dataset (ratio of 1:10) of masses and normal tissue patches was classified using a fully- convolutional network. A maximum of 0:09 improvement of F1 score was reported by using DCGANs along with flipping augmentation in contrast to using the original images. We show that DCGANs can be used for synthesising photo-realistic breast mass patches with a considerable diversity. It is demonstrated that appending synthetic images in this environment, along with flipping, outperforms the traditional augmentation method of flipping solely, offering faster improvements as a function of the training set size.
Complex sequential understanding through the awareness of spatial and temporal concepts

Understanding sequential information is a fundamental task for artificial intelligence. Current neural networks attempt to learn spatial and temporal information as a whole, limited their abilities to represent large scale spatial representations over long-range sequences. Here, we introduce a new modeling strategy called Semi-Coupled Structure (SCS), which consists of deep neural networks that decouple the complex spatial and temporal concepts learning. Semi-Coupled Structure can learn to implicitly separate input information into independent parts and process these parts respectively. Experiments demonstrate that a Semi-Coupled Structure can successfully annotate the outline of an object in images sequentially and perform video action recognition. For sequence-to-sequence problems, a Semi-Coupled Structure can predict future meteorological radar echo images based on observed images. Taken together, our results demonstrate that a Semi-Coupled Structure has the capacity to improve the performance of LSTM-like models on large scale sequential tasks.
Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles

Environment maps must first be generated to drive mobile robots automatically. Path planning is performed based on the information given in an environment map. Various types of sensors, such as ultrasonic and laser sensors, are used by mobile robots to acquire data on its surrounding environment. Among these, the laser sensor, which has the property of being able to go straight and high accuracy, is used most often. However, the beams from laser sensors are refracted and reflected when it meets a transparent obstacle, thus generating noise. Therefore, in this paper, a state-of-the-art algorithm was proposed to detect transparent obstacles by analyzing the pattern of the reflected noise generated when a laser meets a transparent obstacle. The experiment was carried out using the environment map generated by the aforementioned method and gave results demonstrating that the robot could avoid transparent obstacles while it was moving towards the destination.
The Fourier-Argand Representation: An Optimal Basis of Steerable Patterns

Computing the convolution between a 2D signal and a corresponding filter with variable orientations is a basic problem that arises in various tasks ranging from low level image processing (e.g. ridge/edge detection) to high level computer vision (e.g. pattern recognition). Through decades of research, there still lacks an efficient method for solving this problem. In this paper, we investigate this problem from the perspective of approximation by considering the following problem: what is the optimal basis for approximating all rotated versions of a given bivariate function? Surprisingly, solely minimising the L2-approximation-error leads to a rotation-covariant linear expansion, which we name Fourier-Argand representation. This representation presents two major advantages: 1) rotation-covariance of the basis, which implies a "strong steerability" - rotating by an angle α corresponds to multiplying each basis function by a complex scalar e-ikα; 2) optimality of the Fourier-Argand basis, which ensures a few number of basis functions suffice to accurately approximate complicated patterns and highly direction-selective filters. We show the relation between the Fourier-Argand representation and the Radon transform, leading to an efficient implementation of the decomposition for digital filters. We also show how to retrieve accurate orientation of local structures/patterns using a fast frequency estimation algorithm.
MyShake Citizen Seismologists Help Launch Dual-Use Seismic Network in California

The MyShake app began delivering earthquake early warning alerts to users in California on October 17, 2019. The app delivers alerts from ShakeAlert when the estimated magnitude is 4.5 or greater to phones in the Modified Mercali Intensity (MMI) III or greater zone. MyShake users receive the alerts, but also serve as citizen seismologists for the system. They contribute accelerometer data allowing this dual-use network to serve as an alert delivery platform and an earthquake monitoring network. Users are also now afforded the ability to interactively share their experience in an earthquake with others on the system through the use of an experience report. The design and maintenance of this system requires the interoperability of many technical systems (servers, code, smartphones) and stakeholders (scientists, federal and state agencies, public). This imparts some constraints on our ability to address problems and implement new features, but ultimately provides a considered framework within which we can design for future use cases. We discuss new features of the app, such as the experience report, the collection of timing information to improve delivery latencies, and examples of how human centered design responds to user needs. We also look at privacy considerations and ways MyShake can continue to improve in the future.
Compact three-dimensional computational imaging using a dynamic virtual camera

We present a three-dimensional (3D) computational imaging architecture based on the imaging principle of a dynamic virtual camera, which enables the spatial reconstruction using a single camera and a compact wedge prism device. By rotating the prism for camera boresight adjustment, the proposed system can capture an object from different viewpoints. Each captured image appears to be recorded directly with one virtual camera moving in a certain path, facilitating the computational process for stereo matching and profile reconstruction. The experimental results have demonstrated that our architecture allows a compact and flexible system to achieve 3D imaging performance competitive to conventional stereovision.
Quantum Sampling Algorithms for Near-Term Devices

Efficient sampling from a classical Gibbs distribution is an important computational problem with applications ranging from statistical physics over Monte Carlo and optimization algorithms to machine learning. We introduce a family of quantum algorithms that provide unbiased samples by preparing a state encoding the entire Gibbs distribution. We show that this approach leads to a speedup over a classical Markov chain algorithm for several examples including the Ising model and sampling from weighted independent sets of two different graphs. Our approach connects computational complexity with phase transitions, providing a physical interpretation of quantum speedup. Moreover, it opens the door to exploring potentially useful sampling algorithms on near-term quantum devices as the algorithm for sampling from independent sets on certain graphs can be naturally implemented using Rydberg atom arrays.
Acquisition of Vocabulary in Primary Schools Via GoPic with QR Code

Vocabulary acquisition is paramount in English language education yet becomes challenging especially for primary school students in Malaysia. Prior to this, the advancement and integration of information and communication technology (ICT) in 21st-century learning is crucial in teachers’ pedagogical skills and students’ knowledge resulting in student-centered strategies. However, students are still lacking in vocabulary skills as they are learning through traditional methods. This action research conducted in two cycles using GoPic with QR Code investigates the importance of improving students’ language experiences in their acquisition of vocabulary. The first cycle of the intervention was carried out through group approach. The second cycle was carried out using individual approach. The term ‘GoPic with QR Code’ is generated from the blending concept of using ‘BINGO’ word game with picture cards to complement the words (concrete nouns) being applied and assessed for the intervention. The respondents comprise of a total of 64 low proficiency primary school students, that is, 58 Year 1 students from Selangor and 6 Year 5 students from Sabah. Pre and post-test, survey questionnaire and field notes were instruments used in the data collection. The findings concluded that by using GoPic with QR Code, students revealed increment in their English language vocabulary. It also reflected positive implications on the teachers, students and school administrators.
Bitcoin Returns and the Weekday Effect

This paper examines whether the well-known Monday Effect found in stock andTreasury Bills markets also occurs in the Bitcoin market, which differs markedlyfrom other markets due to its continuous trading. The findings of the paper suggestthat the Bitcoin market is not efficient and provides the opportunity to generatestrategies with high abnormal returns. The paper uses a Student’s t-test for astatistically significant difference in the average daily returns of weekdays and find-ing that Tuesday and Wednesday has higher returns as compared with other days.This is corroborated by a regression analysis indicating that there are above averagereturns on Tuesday and Wednesday.
A Privacy-Preserving Multi-Task Framework for Knowledge Graph Enhanced Recommendation

Multi-task learning (MTL) is a learning paradigm which can improve generalization performance by transferring knowledge among multiple tasks. Traditional collaborative filtering recommendation methods suffer from cold start, sparsity and scalability problems. The latest research has shown that applying side information of knowledge graph can not only solve the problems above, but also improve the accuracy of recommendation. However, existing multi-task methods for knowledge graph enhanced recommendation expose obvious issues of disclosing the private information of training samples. In order to solve these problems, we put forward a privacy-preserving multi-task framework for knowledge graph enhanced recommendation. In specific, Laplacian noise is added into the recommendation module to guarantee the privacy of sensitive data and knowledge graph is utilized to improve the accuracy of recommendation. Extensive experimental results on three datasets demonstrate that the proposed method can not only preserve the privacy of sensitive training data, but also have little effect on the prediction accuracy of the model.
Scaling Video Analytics on Constrained Edge Nodes.

As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure. When per-camera bandwidth is limited, it is infeasible for applications such as traffic monitoring and pedestrian tracking to offload high-quality video streams to a datacenter. This paper presents FilterForward, a new edge-to-cloud system that enables datacenter-based applications to process content from thousands of cameras by installing lightweight edge filters that backhaul only relevant video frames. FilterForward introduces fast and expressive per-application microclassifiers that share computation to simultaneously detect dozens of events on computationally constrained edge nodes. Only matching events are transmitted to the cloud. Evaluation on two real-world camera feed datasets shows that FilterForward reduces bandwidth use by an order of magnitude while improving computational efficiency and event detection accuracy for challenging video content.
Non-Markovianity of quantum Brownian motion

We study quantum non-Markovian dynamics of the Caldeira-Leggett model, a prototypical model for quantum Brownian motion describing a harmonic oscillator linearly coupled to a reservoir of harmonic oscillators. Employing the exact analytical solution of this model one can determine the size of memory effects for arbitrary couplings, temperatures, and frequency cutoffs. Here, quantum non-Markovianity is defined in terms of the flow of information between the open system and its environment, which is quantified through the Bures metric as distance measure for quantum states. This approach allows us to discuss quantum memory effects in the whole range from weak to strong dissipation for arbitrary Gaussian initial states. A comparison of our results with the corresponding results for the spin-boson problem show a remarkable similarity in the structure of non-Markovian behavior of the two paradigmatic models.
Dominant Reaction Pathways by Quantum Computing

Characterizing thermally activated transitions in high-dimensional rugged energy surfaces is a very challenging task for classical computers. Here, we develop a quantum annealing scheme to solve this problem. First, the task of finding the most probable transition paths in configuration space is reduced to a shortest-path problem defined on a suitable weighted graph. Next, this optimization problem is mapped into finding the ground state of a generalized Ising model. A finite-size scaling analysis suggests this task may be solvable efficiently by a quantum annealing machine. Our approach leverages on the quantized nature of qubits to describe transitions between different system's configurations. Since it does not involve any lattice space discretization, it paves the way towards future biophysical applications of quantum computing based on realistic all-atom models.
Code Characterization With Graph Convolutions and Capsule Networks

We propose SiCaGCN, a learning system to predict the similarity of a given software code to a set of codes that are permitted to run on a computational resource, such as a supercomputer or a cloud server. This code characterization allows us to detect abusive codes. Our system relies on a structural analysis of the control-flow graph of the software codes and two different graph similarity measures: Graph Edit Distance (GED) and a singular values based metric. SiCaGCN combines elements of Graph Convolutional Neural Networks (GCN), Capsule networks, attention mechanism, and neural tensor networks. Our experimental results include a study of the trade-offs between the two similarity metrics and two variations of our learning networks, with and without the use of capsules. Our main findings are that the use of capsules reduces mean square error significantly for both similarity metrics. Use of capsules reduces the runtime to calculate the GED while increases the runtime of singular values calculation.
Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks

In recent years, user's trust has gained attention in recommender systems.Trust plays a vital role in the recommendation of online products.Trust is a dynamic feature which evolves with passage of time and varies from person to person.Trust-based cross domain recommender systems suggest items to the users usually by ratings, provided by similar users, often not available in the same domain.However, due to the sparse rating scores, recommender systems cannot generate up-to-the-mark recommendations.In this research, we solved a user cold start problem, mainly by modeling preference drift on a temporal basis.We tried to solve this problem by adopting one of the scenarios of cross domain of 'No Overlap' using cross domain information.In this work, we proposed a model called Trust Aware Cross Domain Temporal Recommendations (TrustCTR) that predict the rating of an item about an active user from the most recent time.We generated user features and item features by using latent factor model and trained the proposed model.We also introduced the concept of trust relevancy that shows the degree of trust, computed the trusted neighbors in target domain for an active user belonging to a source domain, and predicted the ratings of items for cold start users.We performed experiments on public datasets Ciao and Epinions and used these datasets in cross domain form such as the categories of Ciao as source domain and Epinions as the target domain.We selected five different domains, having a higher proportion of rating sparsity, for observing the performance of our approach using MAE, RMSE, and F-measure.Our approach is a viable solution of cold start problem and offers effective recommendations We also compared the model with state-of-the-art methods; the model generates satisfactory results.