New time-domain surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe millions of transient alerts each night, making standard approaches of visually identifying new and interesting transients infeasible. We present two novel methods of automatically detecting anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first modelling approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We demonstrate our methods' ability to provide anomaly scores as a function of time on light curves from the Zwicky Transient Facility. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model. The parametric model is able to identify anomalies with respect to common supernova classes with high precision and recall scores, achieving area under the precision-recall curves (AUCPR) above 0.79 for most rare classes such as kilonovae, tidal disruption events, intermediate luminosity transients, and pair-instability supernovae. Our ability to identify anomalies improves over the lifetime of the light curves. Our framework, used in conjunction with transient classifiers, will enable fast and prioritised followup of unusual transients from new large-scale surveys.

An important use case is offered by vehicular ground users are static and have known locations. The same wireless networks, in which UABSs serve as relays authors in [28] extended their previous work by considering between vehicular users and the network, enabling the users multiple UABSs. Unlike these previous works, in this paper, to upload data collected by on-board sensors [5]-[11]. Such we consider traffic conditions characterized by vehicular users user-generated data are collected by the network, and then with a priori unknown locations and we move beyond conventional forwarded to other vehicles by means of BSs or road side meta-RL by accounting for the constraint that simulators units (RSUs). Being able to offer stronger, possibly line-ofsight for previous traffic configurations cannot be revisited. The (LoS), links to vehicles as compared to (static) ground rest of the paper is organized as follows. The system model BSs, UABSs can support demanding vehicle-to-everything and the problem formulation are described in Section II.

Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation. Recently, the advancement of deep learning induces expressive methods that can learn the underlying representation and properties of data. Such capability provides new opportunities in figuring out the mutual relationship between the structural patterns and functional properties of the data and leveraging such relationship to generate structural data given the desired properties. This article provides a systematic review of this promising research area, commonly known as controllable deep data generation. Firstly, the potential challenges are raised and preliminaries are provided. Then the controllable deep data generation is formally defined, a taxonomy on various techniques is proposed and the evaluation metrics in this specific domain are summarized. After that, exciting applications of controllable deep data generation are introduced and existing works are experimentally analyzed and compared. Finally, the promising future directions of controllable deep data generation are highlighted and five potential challenges are identified.

Role requiring'No experience data provided' months of experience in Memphis Pay if you succeed in getting hired and start work at a high-paying job first. Get Paid to Read Emails, Play Games, Search the Web, $5 Signup Bonus. Voca has an immediate contract FOR HIRE position opening (contract term approximately 12 months) for an IT Software Developer resource for our insurance industry client, located in Memphis, TN. This resource can work remotely, with a slight preference for candidates residing in MEMPHIS, TN or COLUMBUS, OH! To develop, maintain, test and debug software to meet generally defined requirements in a Windows client/server environment utilizing software development languages/environments such as PL/SQL, Oracle, Progress 4 GL Application Development Environment (ADE), .Net, Microsoft SQL, or other platforms; to formulate and define system scope and objectives through research and fact-finding for the purpose of developing or modifying moderately complex information systems; to prepare detailed specification from which programs will be written; and to design, code, test, debug, document and maintain programs. Becomes familiar with most aspects of the application including reports, parameters, claims management, intake services, carrier/client interfaces and vendor/business partner interfaces.

We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space. Sequential decision-making requires reasoning about the possible future consequences of current behavior. Consequently, capturing the relationship between key evolving features for a given task is conducive to recovering effective policies. To this end, hyperbolic geometry provides deep RL models with a natural basis to precisely encode this inherently hierarchical information. However, applying existing methodologies from the hyperbolic deep learning literature leads to fatal optimization instabilities due to the non-stationarity and variance characterizing RL gradient estimators. Hence, we design a new general method that counteracts such optimization challenges and enables stable end-to-end learning with deep hyperbolic representations. We empirically validate our framework by applying it to popular on-policy and off-policy RL algorithms on the Procgen and Atari 100K benchmarks, attaining near universal performance and generalization benefits. Given its natural fit, we hope future RL research will consider hyperbolic representations as a standard tool.

Timely recognition of plant pests from field images is significant to avoid potential losses of crop yields. Traditional convolutional neural network-based deep learning models demand high computational capability and require large labelled samples for each pest type for training. On the other hand, the existing lightweight network-based approaches suffer in correctly classifying the pests because of common characteristics and high similarity between multiple plant pests. In this work, a novel double attention-based lightweight deep learning architecture is proposed to automatically recognize different plant pests. The lightweight network facilitates faster and small data training while the double attention module increases performance by focusing on the most pertinent information. The proposed approach achieves 96.61%, 99.08% and 91.60% on three variants of two publicly available datasets with 5869, 545 and 500 samples, respectively. Moreover, the comparison results reveal that the proposed approach outperforms existing approaches on both small and large datasets consistently.

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

The COVID-19 crisis has emphasized the need for scientific methods such as machine learning to speed up the discovery of solutions to the pandemic. Harnessing machine learning techniques requires quality data, skilled personnel and advanced compute infrastructure. In Africa, however, machine learning competencies and compute infrastructures are limited. This paper demonstrates a cross-border collaborative capacity building approach to the application of machine learning techniques in discovering answers to COVID-19 questions.

Cloud removal is an essential task in remote sensing data analysis. As the image sensors are distant from the earth ground, it is likely that part of the area of interests is covered by cloud. Moreover, the atmosphere in between creates a constant haze layer upon the acquired images. To recover the ground image, we propose to use scattering model for temporal sequence of images of any scene in the framework of low rank and sparse models. We further develop its variant, which is much faster and yet more accurate. To measure the performance of different methods {\em objectively}, we develop a semi-realistic simulation method to produce cloud cover so that various methods can be quantitatively analysed, which enables detailed study of many aspects of cloud removal algorithms, including verifying the effectiveness of proposed models in comparison with the state-of-the-arts, including deep learning models, and addressing the long standing problem of the determination of regularisation parameters. The latter is companioned with theoretic analysis on the range of the sparsity regularisation parameter and verified numerically.

Machine learning based traffic forecasting models leverage sophisticated spatiotemporal auto-correlations to provide accurate predictions of city-wide traffic states. However, existing methods assume a reliable and unbiased forecasting environment, which is not always available in the wild. In this work, we investigate the vulnerability of spatiotemporal traffic forecasting models and propose a practical adversarial spatiotemporal attack framework. Specifically, instead of simultaneously attacking all geo-distributed data sources, an iterative gradient-guided node saliency method is proposed to identify the time-dependent set of victim nodes. Furthermore, we devise a spatiotemporal gradient descent based scheme to generate real-valued adversarial traffic states under a perturbation constraint. Meanwhile, we theoretically demonstrate the worst performance bound of adversarial traffic forecasting attacks. Extensive experiments on two real-world datasets show that the proposed two-step framework achieves up to $67.8\%$ performance degradation on various advanced spatiotemporal forecasting models. Remarkably, we also show that adversarial training with our proposed attacks can significantly improve the robustness of spatiotemporal traffic forecasting models. Our code is available in \url{https://github.com/luckyfan-cs/ASTFA}.