Miracle-dz/UniFAR-FTUs huggingface dataset:
What can we expect from a classical V1-MT feedforward architecture for optical flow estimation?

Motion estimation has been studied extensively in neurosciences in the last two decades. The general consensus that has evolved from the studies in the primate vision is that it is done in a two stage process involving cortical areas V1 and MT in the brain. Spatio temporal filters are leading contenders in terms of models that capture the characteristics exhibited in these areas. Even though there are many models in the biological vision literature covering the optical flow estimation problem based on the spatio-temporal filters little is known in terms of their performance on the modern day computer vision datasets such as Middlebury. In this paper, we start from a mostly classical feedforward V1-MT model introducing a additional decoding step to obtain an optical flow estimation. Two extensions are also discussed using nonlinear filtering of the MT response for a better handling of discontinuities. One essential contribution of this paper is to show how a neural model can be adapted to deal with real sequences and it is here for the first time that such a neural model is benchmarked on the modern computer vision dataset Middlebury. Results are promising and suggest several possible improvements. \code{We think that this work could act as a good starting point for building bio-inspired scalable computer vision algorithms. For that reason we propose to also share the code in order to encourage research in this direction.
Quality estimation of process with usage control charts type XR and quality capability of process

In this paper the selected problems related of modern quality monitoring and quality control in Polish companies have been presented. It has been found that monitoring of production processes is a necessary element of company proquality policy. Such approach is connected with continuous improvement of company. Possibility of usage of statistical techniques of quality estimation process has been discussed. For this purpose creation of control charts type X-R and also calculation of index quality capability of process Cp; Cpk. Upon analyse suitable example has been shown, that their application permits to avoidance of many production defects already on the stage of production.
Exploring The Potential of Asian Low Cost Carriers Forming Strategic Alliances

Strategic air alliance and Low cost carriers (LCC) are two successful operation models for airline companies over the past decades. With the hybridization of low cost carriers, our research realized that the new characteristics might enable LCC to utilize the cooperation methods traditional full service carriers used to form strategic alliances and therefore form a new alliance. We take the approach from network setting, load factor, utilization rate and frequent flyer programs to identify the potential of low cost carriers forming strategic alliances. There has been no research working to discuss such combination. In addition, with limited research in the Asian airspace, we would use research from US and EU air industry to support our theoretical setting of such implementation. By using further content analysis and web scraper for real-time data, we shall discuss such proposal within Asia region. Our results were able to provide a concrete and clear implication that LCC indeed has the capability and potential to expand and grow with a formation of LCC strategic air alliance.
The Melody of Spatial Memory Honors Thesis

Music has been studied extensively in psychology, including research on musically-induced emotion. However, the project reported here looked at new effects of musically-induced emotions on spatial memory. Trochidis and Bigand (2013) established that music and emotions are associated. Music of a major mode, or an Ionian mode, is usually associated with positive emotions. Music of minor modes, such the Locrian or Aoelian modes, are associated with negative emotions. These associations with the specific modes are seen by the lowering of the third and sixth scale tones. Participants for the present study completed a spatial memory task (called the “Tower of Hanoi”) while listening to music of different valences (found in previous research to create feelings of positive and negative emotions). Participants completed the task both with and without music playing. Following completion of the tasks, participants reported on emotional state using the Positive and Negative Affect Scale (PANAS; Watson, Clark, & Tellegen, 1988). I hypothesized that those who listened to the positivevalanced music while completing the spatial task would perform better (i.e., faster) than those who listened to the negative-valanced music. Results found were not significant when looking at the tower scores and music valence, however data was indicative of supporting my hypothesis. An interaction was found where participants generally reported that mood score on the PANAS decreased, regardless of the induced mood (positiveor negative-valenced) after working on the spatial memory task. This result appears to be related to the inability to complete the task or the length of time needed to complete the task.
WDXY SUBMISSION FOR DCASE-2019 : ACOUSTIC SCENE CLASSIFICATION WITH CONVOLUTION NEURAL NETWORKS

Acoustic Scene Classification (ASC) is the task of identifying the scene from which the audio signal is recorded. It is one of the core research problems in the field of Computational Sound Scene Analysis. Most of current best performing Acoustic Scene Classification systems utilize Mel scale spectrograms with Convolutional Neural Networks (CNNs). In this paper, we demonstrate how we applied convolutional neural network for DCASE 2019 task1, acoustic scene classification. First, we applied Mel scale spectrogram to extract acoustic features. Mel scale is a common way to suit frequency warping of human ears, with strict decreasing frequency resolution on low to high frequency range. Second, we generate Mel spectrogram from binaural audio, adaptively learn 5 Convolutional Neural Networks. The best classification result of the proposed system was71.1% for Development dataset and 73.16% for Leaderboard dataset.
CNN-based System for Speaker Independent Cell-Phone Identification from Recorded Audio

This paper proposes a cell-phone identification system independent of speech content as well as the speaker. Audio recorded from a cell-phone contains specific signatures corresponding to that cell-phone. These unique signatures of the cell-phone implicitly captured in the recorded audio can be utilized to identify the cellphone. These signatures of a cell-phone obtained from the recorded audio are visually more distinct in the frequency domain than in the time domain signal. Thus, by utilizing the distinctiveness of the signatures in the frequency domain and learning capability of the Convolutional Neural Network (CNN), we propose a system which learns unique signatures of the cell-phones from the frequency domain representation of the audio. In particular, we have used the magnitude of the Discrete Fourier Transform (DFT) as the frequency representation of an audio signal. An extensive set of experiments performed on a large duration dataset shows that the proposed system outperforms the existing state-of-theart systems, notably in the cases where recordings used for training and testing the systems contain mutually exclusive audio content as well as speakers.
WrightEagle 2008 Simulation 2 D Team Description Paper

In WrightEagle2008, we continue to research based on the previous WrightEagle simulation 2D team. WrightEagle has won the runner-up of Robocup2007, the Champion of RoboCup2006, and the runner-up of RoboCup2005, as well as three consecutive championships of RoboCup China Open in recent years. In this paper, we present the innovations of our team since the last simulation league competitions, and related previous work that developed by other simulated RoboCup teams and ourself.
VimicroABCnet: An Image Coder Combining A Better Color Space Conversion Algorithm and A Post Enhancing Network

The framework of combining a better color space conversion (ABC) algorithm,and a post enhancing network for image coding, called VimicroABCnet[1] , is described in this paper. The ABC algorithm employs the principle component analysis[9] method, to find a new primary base axis offering the highest variance for each individual image. The RGB values of each pixel are pre-processed by a 64x64 template filtering. The pixels are then converted by the proposed ABC algorithm, before being encoded by an open source coder[2]. During decoding, the least square method (LSM) has been introduced to estimate the optimal inverse conversion, instead of using a matrix inversion directly. Another feature of the VimicroABCnet is the enhancing network, which adopts the architecture of a classic ResNet[3], and post-processes the decoded RGB image after ABC. Experiments on the CLIC2019 valid dataset have shown significant RGB-PSNR boost of 0.26db or 7.4% bits save@0.145bpp, and 1.2db/22.5%@1.0bpp, making use of the ABC algorithm; and a RGB-PSNR boost of 0.30db@0.15bpp, making use of the enhancing network, respectively. Combining both techniques, an improvement of 0.56db or 12% bits save@0.15bpp; and a decrease in the compressed file size of about 17.8% are achieved in the transparent track. It is noted that each of the two techniques contributes equally. Methods to speed up the decoder model are also discussed.
A Co-guided Neural Network for Person Name Recognition in

Academic homepages are important channels for learning researchers’ profiles. Knowing the person names in academic homepages is essential to the extraction of other entities such as contacts, publications, and biography. Traditional NER models are trained on newswire corpora such as CoNLL-2003, which contain well-formed names in consistent and complete syntax. However, academic homepages often contain text with incomplete syntax and names of various forms. Few studies address person name recognition in this context. To fill this gap, we start with proposing a finegrained name annotation scheme. This scheme further labels detailed name forms including first, middle, or last names, and name words in full or name initials. We then propose a Co-guided Neural Network (CogNN) model to learn from homepages labelled with our fine-grained annotations. CogNN uses coattention mechanisms to co-guide two jointly trained neural networks, each focusing on different dimensions of the name forms. It thus takes full advantage of our annotation scheme and can accurately recognise person names in academic homepages. Experimental results on real datasets show that CogNN significantly outperforms state-of-the-art NER models in extracting person names from academic homepages, while achieving comparable performance on a traditional NER dataset.
Bayesian Counterfactual Risk Minimization ( Supplemental Material )

Our proof will leverage the following version of the PAC-Bayesian theorem, due to McAllester (2003). Lemma 3. Let D denote a fixed distribution on an instance space, Z . Let L : H×Z → [0, 1] denote a loss function. For a distribution, Q, on the hypothesis space, H, and a dataset, S , (z1, . . . , zn) ∈ Z, let R(Q) , Eh∼Q Ez∼D[L(h, z)] and R̂(Q, S) , Eh∼Q [ 1 n ∑n i=1 L(h, zi) ] denote the risk and empirical risk, respectively. For any n ≥ 1, δ ∈ (0, 1), and fixed prior, P, onH, with probability at least 1− δ over draws of S ∼ D, the following holds simultaneously for all posteriors, Q, onH:
Machine learning in agroecology

Simulation models are a widely used tool for modelling systems for which it is hard to obtain real data. However, the simulation models are usually complex and it is not an easy task to induce new knowledge and find relationships and dependencies among different parts of the simulation model. Previous attempts to analyze the outputs from simulation models were mainly focused on speeding up the simulation process. In this monograph we are proposing a methodology for analyzing results of complex simulation models. The methodology combines simulation outputs, background knowledge, and machine learning, to obtain new and interesting knowledge about a certain problem of interest. We apply our methodology to three different simulation models that simulate the co-existence between genetically-modified and conventional crops at different levels. The induced machine learning models provide us with new co-existence knowledge about the positive and negative influences on the co-existence between genetically-modified and conventional crops. The results encourage us to try the same methodology on different types of simulation models and different scientific areas.
Feature Preserving Octree‐Based Hexahedral Meshing

We propose an octree‐based algorithm to tessellate the interior of a closed surface with hexahedral cells. The generated hexahedral mesh (1) explicitly preserves sharp features of the original input, (2) has a maximal, user‐controlled distance deviation from the input surface, (3) is composed of elements with only positive scaled jacobians (measured by the eight corners of a hex [SEK*07]), and (4) does not have self‐intersections.
The Effects of Security Management on Security Events

Although the desired outcome of security management is better security, empirical evidence for this link is scarce. The scarcity arises from lack of data at the firm level for either security posture or incidents across a broad sample of companies. To address this, we use a novel dataset of daily firm-level security information for 480 of the Fortune 500 enterprises that consists of over 33 million security events. The dataset, obtained from a security monitoring company, contains daily measures of security management and negative security events for a 294 day period, yielding 133,248 firm/day observations. Empirical analysis finds that the number of open ports in a firm is associated with higher incidences of botnet activity, potential exploitation, and unsolicited communications, with some analyses also showing a link to malware activity. The findings are robust to several alternative specifications using hidden Markov and hierarchical linear models. This paper thus finds empirical evidence for a fundamental assumption of security practice — a link between security management and improved security.
Online-to-Batch , Optimism and Acceleration

A standard way to obtain convergence guarantees in stochastic convex optimization is to run an online learning algorithm and then output the average of its iterates: the actual iterates of the online learning algorithm do not come with individual guarantees. We close this gap by introducing a black-box modification to any online learning algorithm whose iterates converge to the optimum in stochastic scenarios. We then consider the case of smooth losses, and show that combining our approach with optimistic online learning algorithms immediately yields a fast convergence rate of O(L/T 3/2 + σ/ √ T ) on L-smooth problems with σ variance in the gradients. Finally, we provide a reduction that converts any adaptive online algorithm into one that obtains the optimal accelerated rate of Õ(L/T 2 + σ/ √ T ), while still maintaining Õ(1/ √ T ) convergence in the nonsmooth setting. Importantly, our algorithms adapt to L and σ automatically: they do not need to know either to obtain these rates. 1. Online-to-Batch Conversions We consider convex stochastic optimization problems, where our objective is to minimize some convex function L : D → R where D is some convex domain. We do not have true access to L, however. Instead, we have a stochastic gradient oracle that given a point x ∈ D will provide a random value g such that E[g] = ∇L(x). Our objective is to use this noisy information to optimize L. A simple and extremely effective method for solving stochastic optimization problems is through online learning and online-to-batch conversion (Shalev-Shwartz, 2011; Cesa-Bianchi et al., 2004). These techniques require remarkably few assumptions about the nature of the expected loss or the stochasticity in the system and yet still obtain Google Research, California, USA. Correspondence to: Ashok Cutkosky . Proceedings of the 36 th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019. Copyright 2019 by the author(s). optimal or near-optimal guarantees. This has helped fuel the widespread adoption of online learning algorithms as the method-of-choice in training machine learning models. Briefly, an online learning algorithm accepts a sequence of convex loss functions `1, . , `T and outputs a sequence of iterates w1, . , wT ∈ D where D is some convex space and wt is output before the algorithm observes `t. Performance is measured by the regret:
Removing ROP Gadgets from OpenBSD

Return Oriented Programming (ROP) is a common exploitation technique that reuses existing code fragments (gadgets) to construct shellcode in a compromised program. Recent changes in OpenBSD’s compiler have started to reduce the number of gadgets in x86 and arm64 binaries, with the aim of making ROP exploitation more difficult or impossible. This paper will cover how ROP gadgets emerge from legitimate code, how OpenBSD’s compiler removes these gadgets, and the effects on performance, code size, and ROP tool capabilities. We find that it is possible to meaningfully reduce the number of ROP gadgets in programs, and to effectively hinder ROP tool capabilities.
Analysis of Amino Acid Sequence of SARS Coronavirus , Feline Coronavirus , Rat Coronavirus , Turkey Coronavirus , MERS Coronavirus with Decision Tree , Apriori Algorithm

Coronaviruses primarily infect the upper respiratory and gastrointestinal tract of mammals and birds, according to its host, it can be catagorized, Porcine coronavirus(TGEV), Infectious bronchitis virus(IBV), Bovine coronavirus(BCV), Feline coronavirus(FCoV) and many other types. Among these diverse kinds, we selected 5 viruses which is: SARS, MERS, Feline coronavirus, Rat coronavirus, Turkey coronavirus. It is a proven fact that H4UK and MERS are almost equal genetically but one factor which is cell-surface protein is what makes these two viruses different. By this, we were able to come up with an idea that there might be a possibility, due to differences in amino acid sequence of cell-surface protein, it may allow the coronavirus to be infectious to humans even if it’s not the proper host. So, we selected these 5 coronaviruses mentioned in order to compare their amino acid sequence and predict the possibility by using a proper algorithm: Decision tree, Neural Network, Apriori algorithm.
The relationship of simplified acute physiology score 3 (SAPS 3) and C-reactive protein (CRP) levels with mortality rates and length of stay of patients in surgical intensive care unit

Objectives: The individual risk of surgical patients is more often underestimated and < 15% of patients who underwent surgery were admitted to ICU. The prognostic scores were developed to assess the mortality rate and prognosis for critical patients including surgical ones. T he Acute Physiology and Chronic Health Evaluation (APACHE) score and the Simplified Acute Physiology Score (SAPS) were most popular ones and they were revised with the improvements in health care opportunities. As a prognostic scoring system SAPS 3’ results were defined as excellent in high risk surgical patient study group. CRP is useful as a prognostic indicator or an index of disease progression but its value has not been tested in acute settings adequately. The aim of this study is to test the calibration power of SAPS 3 score and identify correlations between hospital mortality and patient outcomes with SAPS 3 scores and CRP levels. Methods: This retrospective and analytical study was conducted one year period in surgical ICUs of tertiary level of attention in a public institution. It was a case–control medical record review and the patients included in this study were those who admitted in the surgical ICU for any reason. Results: A total of 806 patients admitted to the Gastroenterological surgical ICU was included in the study between March 2016 and March 2017. The relation between mortality rate, length of stay in ICU and SAPS 3 score was significant statistically and the relation of CRP levels with SAPS score and mortality rate was found significant statistically. Conclusion: The discriminative power of SAPS 3 score was very good and the calibration was appropriate.
Chapter 5 Inferring Orthology and Paralogy

The distinction between orthologs and paralogs, genes that started diverging by speciation versus duplication, is relevant in a wide range of contexts, most notably phylogenetic tree inference and protein function annotation. In this chapter, we provide an overview of the methods used to infer orthology and paralogy. We survey both graph-based approaches (and their various grouping strategies) and tree-based approaches, which solve the more general problem of gene/species tree reconciliation. We discuss conceptual differences among the various orthology inference methods and databases and examine the difficult issue of verifying and benchmarking orthology predictions. Finally, we review typical applications of orthologous genes, groups, and reconciled trees and conclude with thoughts on future methodological developments.
Discovery Of Anomalous Patterns Within Multidimensional, Asynchronous Time-series With An Emphasis On The "internet Of Things"

DISCOVERY OF ANOMALOUS PATTERNS WITHIN MULTIDIMENSIONAL, ASYNCHRONOUS TIME-SERIES WITH AN EMPHASIS ON THE “INTERNET OF THINGS” Stephen P. Emmons Jr., Ph.D. The University of Texas at Arlington, 2014 Supervising Professor: Farhad Kamangar In this dissertation we examine “Internet-scale” systems that present us with multidimensional time-series data characterized by many sources sending symbols at irregular intervals over a common channel. We explore a unique method for the discovery of hidden populations of similar sources and their previously-unknown behavioral patterns, and using these discoveries, reveal anomalous sources and/or time-frames based on their statistical properties. To do so, we employ several well-studied mechanisms, such as k-means and Principle Component Analysis (PCA), and bring to bear analysis tools from other disciplines, such as the use of n-grams and “motifs,” that have not previously been considered in these contexts. While applicable to the study of any system whose attributes can map to the generalized model we present, the approach is of particular interest when dealing with large numbers of remote devices that make up the “Internet of Things” (IoT). The method is applied to several discrete layers of cellular wireless communications infrastructure for a diverse set of commercial Machine-to-Machine (M2M) applications where the behavior of the de-
Impacts of Rural to Urban Migration on the Health of Working-Age Adult Migrants in Ho Chi Minh City , Vietnam

The rural-to-urban migration experience is believed to significantly affect various dimensions of the migrants’ well-being. Potential health impacts have not been thoroughly and systematically explored. A multidimensional health assessment instrument, the SF-36, is employed in this paper to systematically investigate these potential impacts. The paper compares eight dimensions of health status for 69 recent migrants living in Ho Chi Minh City with 85 long term urban residents in the city using data collected during 2001. As expected, recent migrants are disadvantaged with respect to longer-term residents on six of the eight dimensions of health status, including physical functioning; role limitations due to physical health problems; bodily pain; general mental health; role limitations due to emotional problems; and general health perceptions. These differences are maintained in models that control for differences in distributions of age, sex, marital status, and socioeconomic status between the two groups. While rural to urban migration often results in substantial economic benefits to those remaining in the sending areas, important health disadvantages accrue to the migrants themselves.