Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Stationary Point Concentration Technique for Leakage Mitigation and Small Drone Detection With FMCW Radar

With the increase in threats posed by small drones, the development of radars to detect such drones has become a key necessity. In earlier studies, we proposed the stationary point concentration (SPC) technique for small drone detection using frequency-modulated continuous-wave (FMCW) radar. The SPC technique is a novel approach for mitigating leakage, which is an inherent problem in FMCW radar. The SPC technique improves the signal-to-noise ratio of small drones by reducing the noise floor and provides accurate distance and velocity information of such drones. However, this technique has shortcomings in its realization. In this article, we present the drawbacks of this technique and propose an advanced SPC (A-SPC) technique. The proposed technique can overcome the drawbacks of the SPC technique while implementing the benefits of the SPC technique. The experimental results verify the proposed A-SPC technique and demonstrate its robustness and usefulness.
'Arbuscular Mycorrhizal Fungi Association in Homestead Bamboo species of Assam, India

Abstract Arbuscular mycorrhizal fungi (AMF) association in roots of four bamboo species Bambusa tulda Roxb. , B. nutans Wall ex Munro, Bambusa pallida Munro and B. balcooa Roxb. grown in homestead area of five different localities of Assam were studied. AM spores were isolated from root rhizosphere soils of four bamboo species and identified tentatively and classified up to the genus level. Soil physico-chemical properties such as soil texture, pH, EC and per cent organic carbon were determined using standard methods. Five genera of AM spores including Glomus, Gigaspora, Acaulospora, Sclerocystis and one unidentified species were isolated from rhizosphere of bamboo species. Spores of Glomus species were found dominated over others. AM fungi spore number varied between sites and among four bamboo species. Variation in AM root colonization was not as marked as spore number and no correlation could be established in between AM spore and root colonization. Rhizosphere soils of B. nutans contained more diverse AM spore types i.e. 9) genera followed by B. pallida with 8 genera, 5 genera in B. tulda and least in B. balcooa with 4 genera. Texture of the soil is sandy loam and loamy. pH of the rhizosphere soils range from 5.0 to 7.3. E.C (µMho) ranges between 0.0918- 0.5529 and % soil organic carbon ranges between 1.81-3.49.
Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components

Adversarial attacks are inputs that are similar to original inputs but altered on purpose. Speech-to-text neural networks that are widely used today are prone to misclassify adversarial attacks. In this study, first, we investigate the presence of targeted adversarial attacks by altering wave forms from Common Voice data set. We craft adversarial wave forms via Connectionist Temporal Classification Loss Function, and attack DeepSpeech, a speech-to-text neural network implemented by Mozilla. We achieve 100% adversarial success rate (zero successful classification by DeepSpeech) on all 25 adversarial wave forms that we crafted. Second, we investigate the use of PCA as a defense mechanism against adversarial attacks. We reduce dimensionality by applying PCA to these 25 attacks that we created and test them against DeepSpeech. We observe zero successful classification by DeepSpeech, which suggests PCA is not a good defense mechanism in audio domain. Finally, instead of using PCA as a defense mechanism, we use PCA this time to craft adversarial inputs under a black-box setting with minimal adversarial knowledge. With no knowledge regarding the model, parameters, or weights, we craft adversarial attacks by applying PCA to samples from Common Voice data set and achieve 100% adversarial success under black-box setting again when tested against DeepSpeech. We also experiment with different percentage of components necessary to result in a classification during attacking process. In all cases, adversary becomes successful.
Scalable Planning with Deep Neural Network Learned Transition Models

In many complex planning problems with factored, continuous state and action spaces such as Reservoir Control, Heating Ventilation and Air Conditioning (HVAC), and Navigation domains, it is difficult to obtain a model of the complex nonlinear dynamics that govern state evolution. However, the ubiquity of modern sensors allows us to collect large quantities of data from each of these complex systems and build accurate, nonlinear deep neural network models of their state transitions. But there remains one major problem for the task of control – how can we plan with deep network learned transition models without resorting to Monte Carlo Tree Search and other black-box transition model techniques that ignore model structure and do not easily extend to continuous domains? In this paper, we introduce two types of planning methods that can leverage deep neural network learned transition models: Hybrid Deep MILP Planner (HD-MILP-Plan) and Tensorflow Planner (TF-Plan). In HD-MILP-Plan, we make the critical observation that the Rectified Linear Unit (ReLU) transfer function for deep networks not only allows faster convergence of model learning, but also permits a direct compilation of the deep network transition model to a Mixed-Integer Linear Program (MILP) encoding. Further, we identify deep network specific optimizations for HD-MILP-Plan that improve performance over a base encoding and show that we can plan optimally with respect to the learned deep networks. In TF-Plan, we take advantage of the efficiency of auto-differentiation tools and GPU-based computation where we encode a subclass of purely continuous planning problems as Recurrent Neural Networks and directly optimize the actions through backpropagation. We compare both planners and show that TF-Plan is able to approximate the optimal plans found by HD-MILP-Plan in less computation time. Hence this article offers two novel planners for continuous state and action domains with learned deep neural net transition models: one optimal method (HD-MILP-Plan) and a scalable alternative for large-scale problems (TF-Plan).
Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal Recovery

Consider a spectrally sparse signal <inline-formula><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula> that consists of <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> complex sinusoids with or without damping. We study the robust recovery problem for the spectrally sparse signal under the fully observed setting, which is about recovering <inline-formula><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula> and a sparse corruption vector <inline-formula><tex-math notation="LaTeX">$\boldsymbol{s}$</tex-math></inline-formula> from their sum <inline-formula><tex-math notation="LaTeX">$\boldsymbol{z}=\boldsymbol{x}+\boldsymbol{s}$</tex-math></inline-formula>. In this paper, we exploit the low-rank property of the Hankel matrix formed by <inline-formula><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula>, and formulate the problem as the robust recovery of a corrupted low-rank Hankel matrix. We develop a highly efficient nonconvex algorithm, coined accelerated structured alternating projections (ASAP). The high computational efficiency and low space complexity of ASAP are achieved by fast computations involving structured matrices, and a subspace projection method for accelerated low-rank approximation. Theoretical recovery guarantee with a linear convergence rate has been established for ASAP, under some mild assumptions on <inline-formula><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\boldsymbol{s}$</tex-math></inline-formula>. Empirical performance comparisons on both synthetic and real-world data confirm the advantages of ASAP, in terms of computational efficiency and robustness aspects.
Conditioning of Reinforcement Learning Agents and its Policy Regularization Application

The role of conditioning was studied for supervised learning problems (Pennington et al., 2017). It also was shown that conditioning regularization can help to avoid the “mode-collapse” problem in Generative Adversarial Networks (Odena et al., 2018). In this paper, we try to answer the following question: Can information about policy conditioning help to shape a more stable and general policy of reinforcement learning agents? To answer this question, we conduct a study of conditioning behavior during policy optimization. To the best of our knowledge, this is the ﬁrst work that research condition number in reinforcement learning agents. We propose a conditioning regularization algorithm and test its performance on the range of continuous con-trol tasks. Finally, we compare algorithms on the CoinRun (Cobbe et al., 2019) environment with separated train end test levels to analyze how conditioning regularization contributes to agents’ generalization.
Enabling Adaptive and Enhanced Acoustic Sensing using Nonlinear Dynamics

Transmission of real-time data is strongly increasing due to remote processing of sensor data, among other things. A route to meet this demand is adaptive sensing, in which sensors acquire only relevant information using pre-processing at sensor level. We present here adaptive acoustic sensors based on mechanical oscillators with integrated sensing and actuation. Their dynamics are shifted into a nonlinear regime using feedback or coupling. This enhances dynamic range, frequency resolution and signal-to-noise ratio. Combining tunable sensing properties with sound analysis could enable acquiring of only relevant information rather than extracting this from irrelevant data by post-processing.
AQD: Towards Accurate Fully-Quantized Object Detection

Network quantization allows inference to be conducted using low-precision arithmetic for improved inference efficiency of deep neural networks on edge devices. However, designing aggressively low-bit (e.g., 2-bit) quantization schemes on complex tasks, such as object detection, still remains challenging in terms of severe performance degradation and unverifiable efficiency on common hardware. In this paper, we propose an Accurate Quantized object Detection solution, termed AQD, to fully get rid of floating-point computation. To this end, we target using fixed-point operations in all kinds of layers, including the convolutional layers, normalization layers, and skip connections, allowing the inference to be executed using integer-only arithmetic. To demonstrate the improved latency-vs-accuracy trade-off, we apply the proposed methods on RetinaNet and FCOS. In particular, experimental results on MS-COCO dataset show that our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes, which is of great practical value. Source code and models are available at: https://github.com/ziplab/QTool
Gaussian upper bounds for the heat kernel on evolving manifolds

In this article, we prove a general and rather flexible upper bound for the heat kernel of a weighted heat operator on a closed manifold evolving by an intrinsic geometric flow. The proof is based on logarithmic Sobolev inequalities and ultracontractivity estimates for the weighted operator along the flow, a method that was previously used by Davies (Amer. J. Math. 109 (1987) 319–334) in the case of a non‐evolving manifold. This result directly implies Gaussian‐type upper bounds for the heat kernel under certain bounds on the evolving distance function; in particular we find new proofs of Gaussian heat kernel bounds on manifolds evolving by Ricci flow with bounded curvature or positive Ricci curvature. We also obtain similar heat kernel bounds for a class of other geometric flows.
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models

In classical computer vision, rectification is an integral part of multi-view depth estimation. It typically includes epipolar rectification and lens distortion correction. This process simplifies the depth estimation significantly, and thus it has been adopted in CNN approaches. However, rectification has several side effects, including a reduced field of view (FOV), resampling distortion, and sensitivity to calibration errors. The effects are particularly pronounced in case of significant distortion (e.g., wide-angle fisheye cameras). In this paper, we propose a generic scale-aware self-supervised pipeline for estimating depth, euclidean distance, and visual odometry from unrectified monocular videos. We demonstrate a similar level of precision on the unrectified KITTI dataset with barrel distortion comparable to the rectified KITTI dataset. The intuition being that the rectification step can be implicitly absorbed within the CNN model, which learns the distortion model without increasing complexity. Our approach does not suffer from a reduced field of view and avoids computational costs for rectification at inference time. To further illustrate the general applicability of the proposed framework, we apply it to wide-angle fisheye cameras with 190° horizontal field of view. The training framework UnRectDepthNet takes in the camera distortion model as an argument and adapts projection and unprojection functions accordingly. The proposed algorithm is evaluated further on the KITTI rectified dataset, and we achieve state-of-the-art results that improve upon our previous work FisheyeDistanceNet [1]. Qualitative results on a distorted test scene video sequence indicate excellent performance1.
An Interpretable Baseline for Time Series Classification Without Intensive Learning

Recent advances in time series classification have largely focused on methods that either employ deep learning or utilize other machine learning models for feature extraction. Though such methods have proven powerful, they can also require computationally expensive models that may lack interpretability of results, or may require larger datasets than are freely available. In this paper, we propose an interpretable baseline based on representing each time series as a collection of probability distributions of extracted geometric features. The features used are intuitive and require minimal parameter tuning. We perform an exhaustive evaluation of our baseline on a large number of real datasets, showing that simple classifiers trained on these features exhibit surprising performance relative to state of the art methods requiring much more computational power. In particular, we show that our methodology achieves good performance on a challenging dataset involving the classification of fishing vessels, where our methods achieve good performance relative to the state of the art despite only having access to approximately two percent of the dataset used in training and evaluating this state of the art.
Model-Agnostic Interpretable and Data-driven suRRogates suited for highly regulated industries

Highly regulated industries, like banking and insurance, ask for transparent decision-making algorithms. At the same time, competitive markets push for sophisticated black box models. We therefore present a procedure to develop a Model-Agnostic Interpretable Data-driven suRRogate, suited for structured tabular data. Insights are extracted from a black box via partial dependence effects. These are used to group feature values, resulting in a segmentation of the feature space with automatic feature selection. A transparent generalized linear model (GLM) is fit to the features in categorical format and their relevant interactions. We demonstrate our R package maidrr with a case study on general insurance claim frequency modeling for six public datasets. Our maidrr GLM closely approximates a gradient boosting machine (GBM) and outperforms both a linear and tree surrogate as benchmarks.
Learning Frame Level Attention for Environmental Sound Classification

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The classification performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose a convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend our convolutional RNN model with a frame-level attention mechanism to learn discriminative feature representations for ESC. We investigated the classification performance when using different attention scaling function and applying different layers. Experiments were conducted on ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness of the proposed method and our method achieved the state-of-the-art or competitive classification accuracy with lower computational complexity. We also visualized our attention results and observed that the proposed attention mechanism was able to lead the network tofocus on the semantically relevant parts of environmental sounds.
Magnolia nuevoleonensis sp. nov. (Magnoliaceae) from northeastern Mexico and a key to species of section Macrophylla

We describe and illustrate Magnolia nuevoleonensis A. Vazquez & Dominguez-Yescas, a new deciduous tree species that belongs to M. sect. Macrophylla Figlar & Nooteboom. It is morphologically similar to M. ashei Weath. , but differs from the latter in being a larger tree, with smaller flowers, larger and broadly ovoid (vs cylindrical to ellipsoid) fruits, prominently beaked (vs shortly beaked) carpels, and firm and short (vs flexible and elongated) styles.
Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow

The security-constrained optimal power flow (SCOPF) is fundamental in power systems and connects the automatic primary response (APR) of synchronized generators with the short-term schedule. Every day, the SCOPF problem is repeatedly solved for various inputs to determine robust schedules given a set of contingencies. Unfortunately, the modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs, which are hard to solve. To address this challenge, leveraging the wealth of available historical data, this paper proposes a novel approach that combines deep learning and robust optimization techniques. Unlike recent machine-learning applications where the aim is to mitigate the computational burden of exact solvers, the proposed method predicts directly the SCOPF implementable solution. Feasibility is enforced in two steps. First, during training, a Lagrangian dual method penalizes violations of physical and operations constraints, which are iteratively added as necessary to the machine-learning model by a Column-and-Constraint-Generation Algorithm (CCGA). Second, another different CCGA restores feasibility by finding the closest feasible solution to the prediction. Experiments on large test cases show that the method results in significant time reduction for obtaining feasible solutions with an optimality gap below 0.1%.
Black-Box Control for Linear Dynamical Systems

We consider the problem of controlling an unknown linear time-invariant dynamical system from a single chain of black-box interactions, and with no access to resets or offline simulation. Under the assumption that the system is controllable, we give the first efficient algorithm that is capable of attaining sublinear regret in a single trajectory under the setting of online nonstochastic control. We give finite-time regret bound of our algorithm, as well as a nearly-matching lower bound that shows this regret to be almost best-attainable by any algorithm.
Enterprise Architecture in Healthcare Systems: A systematic literature review

Enterprise architecture (EA) has been present in scientific literature since the 1980s and has branched out into several research fields. EA delivers value by presenting business and ICT leaders with recommendations for adjusting policies and projects to achieve business goals. Although there are many works on the EA application in healthcare systems, the literature lacks studies that provide a systematic approach to this topic specifically. This work presents a deep and broad Systematic Literature Review (SLR) to select studies demonstrating current EA practices in healthcare systems. The researchers established an SLR protocol returning 280 primary studies after the first step of the Data Selection and a consolidated inclusion of 46 articles after the second step. They assessed the level of disagreement during the team's evaluations using Cohen's Kappa. This SLR revealed essential aspects of state-of-the-art EA application in healthcare systems, such as the most used methodologies and tools, best practices, and criteria considered for their choice. It also analyzed the main positive impacts, challenges, and critical success factors described by the studies' authors based on empirical approaches. Besides, this work brings the main publication channels and the most influential authors on the topic of EA in Healthcare systems.
Learning hidden influences in large-scale dynamical social networks: A data-driven sparsity-based approach

Interpersonal influence estimation from empirical data is a central challenge in the study of social structures and dynamics. Opinion dynamics theory is a young interdisciplinary science that studies opinion formation in social networks and has a huge potential in applications, such as marketing, advertisement and recommendations. The term social influence refers to the behavioral change of individuals due to the interactions with others in a social system, e.g. organization, community, or society in general. The advent of the Internet has made a huge volume of data easily available that can be used to measure social influence over large populations. Here, we aim at qualitatively and quantitatively infer social influence from data using a systems and control viewpoint. First, we introduce some definitions and models of opinions dynamics and review some structural constraints of online social networks, based on the notion of sparsity. Then, we review the main approaches to infer the network's structure from a set of observed data. Finally, we present some algorithms that exploit the introduced models and structural constraints, focusing on the sample complexity and computational requirements.