Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these come from a wide range of insect taxa regions, the evidence to assess extent phenomenon sparse. Insect populations challenging study, most monitoring methods labor intensive inefficient. Advances computer vision deep learning provide potential new solutions this global challenge. Cameras other sensors can effectively, continuously, noninvasively perform...

In today's financial markets, where most trades are performed in their entirety by electronic means and the largest fraction of them is completely automated, an opportunity has risen from analyzing this vast amount transactions. Since all transactions recorded great detail, investors can analyze generated data detect repeated patterns price movements. Being able to advance, allows take profitable positions or avoid anomalous events markets. work we proposed a deep learning methodology, based...

Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature market. In high-frequency trading, for trading purposes is even more task, since an automated inference system required to be both accurate fast. this paper, we propose neural network layer architecture that incorporates idea bilinear projection as well attention mechanism enables detect focus on crucial temporal information. The resulting highly interpretable, given...

Abstract The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of topic and multitude ingredients involved therein, besides complexity turning theory into practical implementations, limit use learning paradigm, preventing its widespread adoption across different fields applications. This self-contained survey engages introduces readers to principles algorithms Learning for Neural Networks. It provides an introduction from accessible, practical-algorithmic...

In this paper, a novel view invariant action recognition method based on neural network representation and is proposed. The of videos learning spatially related human body posture prototypes using self organizing maps. Fuzzy distances from are used to produce time representation. Multilayer perceptrons for classification. algorithm trained data multi-camera setup. An arbitrary number cameras can be in order recognize actions Bayesian framework. proposed also applied depicting interactions...

Forecasting financial time-series has long been among the most challenging problems in market analysis. In order to recognize correct circumstances enter or exit markets investors usually employ statistical models (or even simple qualitative methods). However, inherently noisy and stochastic nature of severely limits forecasting accuracy used models. The introduction electronic trading availability large amounts data allow for developing novel machine learning techniques that address some...

Deep learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL degenerate rapidly if data are not appropriately normalized. This issue is even more apparent when for financial forecasting tasks, where nonstationary and multimodal nature pose significant challenges severely affect models. In this brief, a simple, yet effective, neural layer that capable adaptively normalizing input series, while taking into account distribution...

In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace (SL) criteria on optimization process followed calculation network's output weights. The proposed graph embedded ELM (GEELM) naturally exploit both intrinsic and penalty SL have been (or will be) designed under embedding framework. addition, extend GEELM in order be arbitrary (even infinite) dimensional...

In this paper, we propose a novel method aiming at view-independent human action recognition. Action description is based on local shape and motion information appearing spatiotemporal locations of interest in video. representation involves fuzzy vector quantization, while classification performed by feedforward neural network. A algorithm, called minimum class variance extreme learning machine, proposed order to enhance the performance. The can successfully operate situations that may...

In this paper, the problem of multi-view embedding from different visual cues and modalities is considered. We propose a unified solution for subspace learning methods using Rayleigh quotient, which extensible multiple views, supervised learning, non-linear embeddings. Numerous including Canonical Correlation Analysis, Partial Least Sqaure regression Linear Discriminant Analysis are studied specific intrinsic penalty graphs within same framework. Non-linear extensions based on kernels (deep)...

Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, taxonomic resolution. Camera traps can capture habitus images of ground-dwelling insects. However, currently sampling involves manually detecting identifying specimens. Here, we test whether a convolutional neural network (CNN) classify ground beetles species level, estimate how correct classification relates body size, number inside genera, identity.We created an image database...

Abstract Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time‐consuming sorting expert‐based identification taxa pose strong limitations on many samples can be processed. In turn, this affects scale efforts map monitor invertebrate diversity altogether. Given recent advances computer vision, we propose enhance standard human...

Abstract Feed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons are well-known universal approximators. However, their learning performance varies significantly depending on function solution space that they attempt to approximate. This is mainly because of homogenous configuration based solely linear neuron model. Therefore, while learn very well those problems with a monotonous, relatively simple and linearly separable space, may entirely fail do...

Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional (CNNs) such as network homogeneity with sole linear neuron model. ONNs are heterogeneous networks a generalized However operator search method in is not only computationally demanding, but heterogeneity also limited since same set operators will then be used for all neurons each layer. Moreover, performance directly depends on library used, which...

The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational (GOP) was proposed extend the conventional defining diverse activities imitate biological neurons. Together with GOP, progressive (POP) algorithm optimize predefined template multiple homogeneous layers in layerwise manner. In this paper, we propose an...

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and recent years, it become more common other areas such as industry, security, geography. The development computer vision machine learning techniques also made easier automatically process images several learning-based object (anomaly) detection, classification, segmentation methods have recently employed image analysis. Due high potential deep related processing...

Nowadays, with the availability of massive amount trade data collected, dynamics financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage rapid, subtle movement assets in High Frequency Trading (HFT), automatic algorithm analyze detect patterns price change based on transaction records must be available. The multichannel, time-series representation naturally suggests tensor-based learning algorithms. this work, we investigate...

The existing literature provides evidence that limit order book data can be used to predict short-term price movements in stock markets. This paper proposes a new neural network architecture for predicting return jump arrivals one minute ahead equity markets with high-frequency data. architecture, based on Convolutional Long Short-Term Memory Attention, is introduced apply time series representation learning memory and focus the prediction attention most important features improve...