Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy in Data Service Composition

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users’ legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a <italic>mediator</italic>, with which the services can share their data and delegate the enforcement of their privacy policies. In this article, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="perera-ieq1-2963309.gif"/></alternatives></inline-formula>-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.
Scalable Gradients for Stochastic Differential Equations

The adjoint sensitivity method scalably computes gradients of solutions to ordinary differential equations. We generalize this method to stochastic differential equations, allowing time-efficient and constant-memory computation of gradients with high-order adaptive solvers. Specifically, we derive a stochastic differential equation whose solution is the gradient, a memory-efficient algorithm for caching noise, and conditions under which numerical solutions converge. In addition, we combine our method with gradient-based stochastic variational inference for latent stochastic differential equations. We use our method to fit stochastic dynamics defined by neural networks, achieving competitive performance on a 50-dimensional motion capture dataset.
Review of single-cell RNA-seq data clustering for cell-type identification and characterization

In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data

Many real-world phenomena are observed at multiple resolutions. Predictive models designed to predict these phenomena typically consider different resolutions separately. This approach might be limiting in applications where predictions are desired at fine resolutions but available training data is scarce. In this paper, we propose classification algorithms that leverage supervision from coarser resolutions to help train models on finer resolutions. The different resolutions are modeled as different views of the data in a multi-view framework that exploits the complementarity of features across different views to improve models on both views. Unlike traditional multi-view learning problems, the key challenge in our case is that there is no one-to-one correspondence between instances across different views in our case, which requires explicit modeling of the correspondence of instances across resolutions. We propose to use the features of instances at different resolutions to learn the correspondence between instances across resolutions using an attention mechanism.Experiments on the real-world application of mapping urban areas using satellite observations and sentiment classification on text data show the effectiveness of the proposed methods.
CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task. Appropriate data structure and unsupervised deep learning are the keys to achieve an easy adjusted LiDAR odometry solution with high performance. Utilizing compact 2D structured spherical ring projection model and voxel model which preserves the original shape of input data, we propose a fully unsupervised Convolutional Auto-Encoder based LiDAR Odometry (CAE-LO) that detects interest points from spherical ring data using 2D CAE and extracts features from multi-resolution voxel model using 3D CAE. We make several key contributions: 1) experiments based on KITTI dataset show that our interest points can capture more local details to improve the matching success rate on unstructured scenarios and our features outperform state-of-the-art by more than 50% in matching inlier ratio; 2) besides, we also propose a keyframe selection method based on matching pairs transferring, an odometry refinement method for keyframes based on extended interest points from spherical rings, and a backward pose update method. The odometry refinement experiments verify the proposed ideas' feasibility and effectiveness.
Joint Beamforming and Computation Offloading for Multi-User Mobile-Edge Computing

Mobile edge computing (MEC) is considered as an efficient method to relieve the computation burden of mobile devices. In order to reduce the energy consumption and time delay of mobile devices (MDs) in MEC, multiple users multiple input and multiple output (MU-MIMO) communications is considered to be applied to the MEC system. The purpose of this paper is to minimize the weighted sum of energy consumption and time delay of MDs by jointly considering the offloading decision and MU-MIMO beamforming problems. And the resulting optimization problem is a mixed-integer non- linear programming problem, which is NP-hard. To solve the optimization problem, a semidefinite relaxation based algorithm is proposed to solve the offloading decision problem. Then, the MU-MIMO beamforming design problem is handled with a newly proposed fractional programming method. Simulation results show that the proposed algorithms can effectively reduce the energy consumption and time delay of the computation offloading.
Long term γ-ray variability of blazars

We used the data from the Fermi Gamma-ray Space Telescope to characterise the γ-ray flux variability of blazars on month-like time scales. Our sample consists of 1120 blazars of which 481 are flat spectrum radio quasars (FSRQs) and 639 are BL Lac objects (BL Lacs). We generated monthly binned light curves of our sample for a period of approximately nine years from 2008 August to 2017 December and quantified variability by using excess variance (Fvar). On month-like time scales, 371/481 FSRQs are variable (80%), while only about 50% (304/639) of BL Lacs are variable. This suggests that FSRQs are more variable than BL Lac objects. We find a mean Fvar of 0.55 ± 0.33 and 0.47 ± 0.29 for FSRQs and BL Lacs respectively. Large Fvar in FSRQs is also confirmed from the analysis of the ensemble structure function. By Dividing our sample of blazars based on the position of the synchrotron peak in their broad-band spectral energy distribution, we find that the low synchrotron peaked (LSP) sources have the largest mean Fvar value of 0.54 ± 0.32 while the intermediate synchrotron peaked (ISP) and high synchrotron peaked sources have mean Fvar values of 0.45 ± 0.25 and 0.47 ± 0.33 respectively. On month-like time scales, we find FSRQs to show a high duty cycle (DC) of variability of 66% relative to BL Lacs that show a DC of 36%. We find that both the Fvar and time scale of variability (τ) do not correlate with MBH. We note that Fvar is found to be weakly correlated with Doppler factor (δ) and τ is also weakly correlated with δ. Most of the sources in our sample have τ of the order of days, which might be related to processes in the jet. We find marginal difference in the distribution of τ between FSRQs and BL Lacs.
Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping

Cashier-free shopping systems like Amazon Go improve shopping experience, but can require significant store redesign. In this paper, we propose Grab, a practical system that leverages existing infrastructure and devices to enable cashier-free shopping. Grab needs to accurately identify and track customers, and associate each shopper with items he or she retrieves from shelves. To do this, it uses a keypoint-based pose tracker as a building block for identification and tracking, develops robust feature-based face trackers, and algorithms for associating and tracking arm movements. It also uses a probabilistic framework to fuse readings from camera, weight and RFID sensors in order to accurately assess which shopper picks up which item. In experiments from a pilot deployment in a retail store, Grab can achieve over 90% precision and recall even when 40% of shopping actions are designed to confuse the system. Moreover, Grab has optimizations that help reduce investment in computing infrastructure four-fold.
Consistent Batch Normalization for Weighted Loss in Imbalanced-Data Environment

In this study, classification problems based on feedforward neural networks in a data-imbalanced environment are considered. Learning from an imbalanced dataset is one of the most important practical problems in the field of machine learning. A weighted loss function (WLF) based on a cost-sensitive approach is a well-known and effective method for imbalanced datasets. A combination of WLF and batch normalization (BN) is considered in this study. BN is considered as a powerful standard technique in the recent developments in deep learning. A simple combination of both methods leads to a size-inconsistency problem due to a mismatch between the interpretations of the effective size of the dataset in both methods. A simple modification to BN, called weighted BN (WBN), is proposed to correct the size mismatch. The idea of WBN is simple and natural. The proposed method in a data-imbalanced environment is validated using numerical experiments.
UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats

Advanced Persistent Threats (APTs) are difficult to detect due to their "low-and-slow" attack patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based APT detector that effectively leverages data provenance analysis. From modeling to detection, UNICORN tailors its design specifically for the unique characteristics of APTs. Through extensive yet time-efficient graph analysis, UNICORN explores provenance graphs that provide rich contextual and historical information to identify stealthy anomalous activities without pre-defined attack signatures. Using a graph sketching technique, it summarizes long-running system execution with space efficiency to combat slow-acting attacks that take place over a long time span. UNICORN further improves its detection capability using a novel modeling approach to understand long-term behavior as the system evolves. Our evaluation shows that UNICORN outperforms an existing state-of-the-art APT detection system and detects real-life APT scenarios with high accuracy.
Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation

This work aims to help resolve the two main stumbling blocks in the application of Deep Neural Networks (DNNs), that is, the exceedingly large number of trainable parameters and their physical interpretability. This is achieved through a tensor valued approach, based on the proposed Tucker Tensor Layer (TTL), as an alternative to the dense weight-matrices of DNNs. This allows us to treat the weight-matrices of general DNNs as a matrix unfolding of a higher order weight-tensor. By virtue of the compression properties of tensor decompositions, this enables us to introduce a novel and efficient framework for exploiting the multi-way nature of the weight-tensor in order to dramatically reduce the number of DNN parameters. We also derive the tensor valued back-propagation algorithm within the TTL framework, by extending the notion of matrix derivatives to tensors. In this way, the physical interpretability of the Tucker decomposition is exploited to gain physical insights into the NN training, through the process of computing gradients with respect to each factor matrix. The proposed framework is validated on both synthetic data, and the benchmark datasets MNIST, Fashion-MNIST, and CIFAR-10. Overall, through the ability to provide the relative importance of each data feature in training, the TTL back-propagation is shown to help mitigate the "black-box" nature inherent to NNs. Experiments also illustrate that the TTL achieves a 66.63-fold compression on MNIST and Fashion-MNIST, while, by simplifying the VGG-16 network, it achieves a 10\% speed up in training time, at a comparable performance.
On Transferability of Machine Learning Force Fields: A Case Study on Silicon

In this article, we present a systematic study in developing machine learning interatomic potential (MLIAP) for elemental silicon. Previously, many of MLIAP applications involve fitting models to small or localized training sets, which do not allow for transferability. The need for transferability of MLIAP is crucial in sampling the vast phase space in the application of crystal structure prediction. To address for this need, we indicate that two primary factors can have major influences on the transferability of the MLIAP. First, transferability depends strongly on the diversity of the training data set. In particular, the use of a random structures generating algorithm based on crystal symmetry is a suitable methodology for generating a training data set with sufficient diversity. Second, we introduce a framework that allows Gaussian symmetry functions and bispectrum coefficients descriptors to be fitted with generalized linear regression or neural network. According to our substantial benchmark, the best accuracy trained on the diverse data set can be achieved by using a flexible machine learning method coupled with highly orthogonal descriptors. Therefore, we will show that neural network potential fitting with bispectrum coefficients as the descriptor is a feasible method for obtaining high-quality and transferable MLIAP.
A Robot that Learns Connect Four Using Game Theory and Demonstrations

Teaching robots new skills using minimal time and effort has long been a goal of artificial intelligence. This paper investigates the use of game theoretic representations to represent and learn how to play interactive games such as Connect Four. We combine aspects of learning by demonstration, active learning, and game theory allowing a robot to learn by presenting its understanding of the structure of the game and conducting a question/answer session with a person. The paper demonstrates how a robot can be taught the win conditions of the game Connect Four and its variants using a single demonstration and a few trial examples with a question and answer session led by the robot. Our results show that the robot can learn any arbitrary win conditions for the Connect Four game without any prior knowledge of the win conditions and then play the game with a human utilizing the learned win conditions. Our experiments also show that some questions are more important for learning the game's win conditions.
What Caging Force Cells Feel in 3D Hydrogels: A Rheological Perspective

It has been established that the mechanical properties of hydrogels control the fate of (stem) cells. However, despite its importance, a one‐to‐one correspondence between gels' stiffness and cell behavior is still missing from literature. In this work, the viscoelastic properties of poly(ethylene‐glycol) (PEG)‐based hydrogels are investigated by means of rheological measurements performed at different length scales. The outcomes of this work reveal that PEG‐based hydrogels show significant stiffening when subjected to a compressional deformation, implying that conventional bulk rheology measurements may overestimate the stiffness of hydrogels by up to an order of magnitude. It is hypothesized that this apparent stiffening is caused by an induced “tensional state” of the gel network, due to the application of a compressional normal force during sample loading. Moreover, it is shown that the actual stiffness of the hydrogels is instead accurately determined by means of both passive‐video‐particle‐tracking (PVPT) microrheology and nanoindentation measurements, which are inherently performed at the cell's length scale and in absence of any externally applied force in the case of PVPT. These results underpin a methodology for measuring hydrogels' linear viscoelastic properties that are representative of the mechanical constraints perceived by cells in 3D hydrogel cultures.
Deep learning for brake squeal: vibration detection, characterization and prediction

Despite significant advances in modeling of friction-induced vibrations and brake squeal, the majority of industrial research and design is still conducted experimentally, since many aspects of squeal and its mechanisms involved remain unknown. We report here for the first time on novel strategies for handling data-intensive vibration testings to gain better insights into friction brake system vibrations and noise generation mechanisms. Machine learning-based methods to detect and characterize vibrations, to understand sensitivities and to predict brake squeal are applied with the aim to illustrate how interdisciplinary approaches can leverage the potential of data science techniques for classical mechanical engineering challenges. In the first part, a deep learning brake squeal detector is developed to identify several classes of typical friction noise recordings. The detection method is rooted in recent computer vision techniques for object detection based on convolutional neural networks. It allows to overcome limitations of classical approaches that solely rely on instantaneous spectral properties of the recorded noise. Results indicate superior detection and characterization quality when compared to a state-of-the-art brake squeal detector. In the second part, a recurrent neural network is employed to learn the parametric patterns that determine the dynamic stability of an operating brake system. Given a set of multivariate loading conditions, the RNN learns to predict the noise generation of the structure. The validated RNN represents a virtual twin model for the squeal behavior of a specific brake system. It is found that this model can predict the occurrence and the onset of brake squeal with high accuracy and that it can identify the complicated patterns and temporal dependencies in the loading conditions that drive the dynamical structure into regimes of instability.
Issues with rounding in the GCC implementation of the ISO 18037:2008 standard fixed-point arithmetic

We describe various issues caused by the lack of round-to-nearest mode in the gcc compiler implementation of the fixed-point arithmetic data types and operations. We demonstrate that round-to-nearest is not performed in the conversion of constants, conversion from one numerical type to a less precise type and results of multiplications. Furthermore, we show that mixed-precision operations in fixed-point arithmetic lose precision on arguments, even before carrying out arithmetic operations. The ISO 18037:2008 standard was created to standardize C language extensions, including fixed-point arithmetic, for embedded systems. Embedded systems are usually based on ARM processors, of which approximately 100 billion have been manufactured by now. Therefore, the observations about numerical issues that we discuss in this paper can be rather dangerous and are important to address, given the wide ranging type of applications that these embedded systems are running.
AN5D: automated stencil framework for high-degree temporal blocking on GPUs

Stencil computation is one of the most widely-used compute patterns in high performance computing applications. Spatial and temporal blocking have been proposed to overcome the memory-bound nature of this type of computation by moving memory pressure from external memory to on-chip memory on GPUs. However, correctly implementing those optimizations while considering the complexity of the architecture and memory hierarchy of GPUs to achieve high performance is difficult. We propose AN5D, an automated stencil framework which is capable of automatically transforming and optimizing stencil patterns in a given C source code, and generating corresponding CUDA code. Parameter tuning in our framework is guided by our performance model. Our novel optimization strategy reduces shared memory and register pressure in comparison to existing implementations, allowing performance scaling up to a temporal blocking degree of 10. We achieve the highest performance reported so far for all evaluated stencil benchmarks on the state-of-the-art Tesla V100 GPU.
The Lie Detector.

How many free variables do we really need to build a credible model of a physical system? Currently there is no systematic approach; we appeal to some physical principles, tune free variables by comparing with canonical cases, and hope our real-world applications interpolate between them. In this work we combine two pioneering and entirely disparate pieces of mathematics: the century-old techniques of Sophus Lie for solving differential equtions and recent work initiated by Field's medallist Terence Tao on converting NP-complete combinatorical problems into neighbouring convex optimisations. We present a novel and fully systematic procedure for designing models of physical systems with necessary and just-sufficient complexity, in marked contrast with the approach to function approximation taken by neural networks and other current approaches to machine learning. Our methodology replaces the ad-hoc development of models to recover structure and understanding from observational, experimental or simulated data. At its core, our method seeks to find invariant properties of differential equations known as Lie symmetries, and for this reason we have called our algorithm the Lie Detector.