Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Online Learning via Frank-Wolfe Algorithm

Online convex optimization (OCO) encapsulates supervised learning when training sets are large-scale or dynamic, and has grown essential as data has proliferated. OCO decomposes learning into a sequence of sub-problems, each of which must be solved with limited information. To ensure safe model adaption or to avoid overfitting, constraints are often imposed, which are often addressed with projections or Lagrangian relaxation. To avoid this complexity incursion, we propose to study Frank-Wolfe (FW), which operates by updating in collinear directions with the gradient but guaranteed to be feasible. We specifically focus on its use in non-stationary settings, motivated by the fact that its iterates have structured sparsity that may be employed as a distribution-free change-point detector. We establish performance in terms of dynamic regret, which quantifies cost accumulation as compared with the optimal at each individual time slot. Specifically, for convex losses, we establish ${\mathcal O}(T^{1/2})$ dynamic regret up to metrics of non-stationarity. We relax the algorithm's required information to only noisy gradient estimates, i.e., partial feedback. We also consider a mini-batching ‘Meta-Frank Wolfe’, and characterize its dynamic regret. Experiments on matrix completion problem and background separation in video demonstrate favorable performance of the proposed scheme. Moreover, the structured sparsity of FW is experimentally observed to yield the sharpest tracker of change points among alternative approaches to non-stationary online convex optimization.
Dietary vitamin A requirement of juvenile greasy

A 10-week growth study was conducted to determine the dietary vitamin A requirement of juvenile greasy grouper. Eight semipurified diets containing 210, 476, 914, 1774, 3764. 7063, 14,342 and 29,193 lii vitamin A (as retinyl acetate)/kg diet were fed twice daily to triplicate groups (20 fish/group) of fish (initial weight 5.84 ± 1.5 g/flsh). Fish fed the basal diet (210 IU vitamin A/kg diet) developed haemorrhages in the skin overlaying the base of the fins and erosion on the caudal peduncle. None of these deficiency signs were observed in fish fed the vitamin A supplemented diets. Significantly higher weight gin, protein efficiency ratio and survival and lower feed conversion ratio were observed in fish fed the 3764 LU vitamin A/kg diet than fish fed the other diets. Whole body crude fat concentrations significantly decreased in fish fed increasing levels of dietary vitamin A. Addition of vitamin A to the basal diet did not significantly affect whole body protein and ash concentrations of the fish. However, dietary vitamin A supplementation did not significantly affect whole body moisture concentration. Serum vitamin A concentration was affected by dietary vitamin A level. Increasing serum vitamin A concentration was observed as dietary vitamin A level increased from 210 to 3764 lU/kg and tended to plateau in fish fed higher vitamin A levels. Brokenline regression analysis showed that juvenile greasy grouper require a minimum of 3101 [U vitamin A/kg diet for maximal growth. The associated serum vitamin A concentration was 179 tg/ml. © 2003 Published by Elsevier Science B.V. KeworcLc: Epinephelus lamina; Greasy grouper; Nutrition; Retinal; Vitamin A * Corresponding author. Present address: The University of Texas at Austin, Marine Science Institute, 750 Channel View Drive, Port Aransas, Texas 78373-5015. Tel. : +91-4652-353078; fax: +91-4652-353078. E-mail address: shaikmohained@redjffinail.com (J. Shaik Mohamed). 0044-8486,03/$ see fronmatter © 2003 Published by Elsevier Science B.V. doi: 10. 1016/S0044-8486(02)00665-8 694 J. Shuik Mohumt'd et al. /Aquueulture 219 (2003) 693-701
Residual stresses in glass after molding and its influence on optical properties

Compression molding of glass optics is gradually becoming a viable manufacturing process for high precision optical lenses. However, during cooling, glass properties undergo a small amount of changes due to structural relaxation. As a result, residual stresses are trapped inside the molded glass lenses. In this research, compression molded lenses were studied using the birefringence method assisted by the finite element analysis. The experimental and simulation results showed that compression molding process can be controlled to a very high precision providing that the proper cooling is applied during cooling. In addition, numerical analysis using finite element analysis also provides a better understand of the cooling process and offers an economic solution for process optimization for low cost glass lens production. © 2012 Published by Elsevier Ltd. Selection and peer-review under responsibility of Prof. E. Brinksmeier
HIGH-RESOLUTION SATELLITE IMAGE ANALYSIS BASED ON CLASSIFICATION AND REGRESSION TREES ALGORITHM

Spectral features, shape and texture features of objects in high-resolution remote sensing images can be extracted by object-oriented classification. In order to obtain a feature set which include as minimum feature number as possible, but not harm the classification accuracy remarkably, the primary feature set need to be optimized. Comparing the accuracy of Sequential backward search with Max-relevance and Min-redundancy, the classification rules were obtained by Classification and Regression Trees (CART) with the optimal feature subset. With the Worldview2 high-resolution remote sensing imagery, experimental result shows, Sequential Backward Search is suitable for the study area to rank features. Using the optimized subset to build CART model is an effective way to maintain the classification accuracy and improve image processing efficiency.
Odd-Even Message Bit Sequence Based Image Steganography

Many individuals and business people use to transfer business documents, important information using internet. These documents are sensitive and if the documents get tempered it may result in serious misuse. Most Steganography techniques have respective strong and weak points. It is the art of hiding messages in the cover material. Hidden message may be in the form of text or image, which is only revealed by the authentic user. In this paper, we have proposed a method of splitting the message into a bit sequence, which then hides into the RGB planes of the image using 2-2-4 Least Significant Bit substitution method. This combination have proved to be a brand new combination in the approach. The above described techniques have different implementations in various spheres of day to day life. Keywords— Steganography, LSB (least significant bit), RGB planes, Data hiding.
Cash Transfers and Health: Mechanisms and Impacts

Why have conditional cash transfers only selectively improved health outcomes and investments in developing countries? We examine this question by exploiting the randomized introduction of a transfer program in Tanzania in 2010. We find several reasons for inconsistent results in the existing literature. First, health improvements took time to materialize; we find no improvements after 1.5 years of transfers, but significant improvements after 2.5 years, suggesting the importance of considering longer-term impacts. Second, health benefits were greatest in villages with more baseline health workers per capita, consistent with health improvements being sensitive to clinic capacity constraints. Finally, health did not improve due to more clinic visits, but instead due to more timely visits made when individuals are ill as opposed to when they have money, and these changes in attendance patterns are likely facilitated by significant uptake of health insurance. The results are robust to adjustments for multiple hypothesis testing. JEL Classification: I18, I13, O12, H42
IMPROVED RATES FOR MULTI-AGENT ONLINE KERNEL LEARNING IN HETEROGENEOUS NETWORKS

In this paper, we consider a decentralized heterogeneous network of multiple agents that tries to learn optimal regression function belonging to reproducing kernel Hilbert space (RKHS). To handle the heterogeneity in the network, non-linear proximity constraints are introduced. In literature, to solve the constrained optimization problems using decentralized functional stochastic variant of the primal-dual (Arrow-Hurwicz), an upper bound of O ( T 3 / 4 ) on the constraint violation exists. In this paper, we have improved this upper bound to O ( √ T ) , where T is the number of samples processed. The decentralized algorithms performance is validated on real world data set for estimating temperature and salinity from depth measurements of the Gulf of Mexico.
A Methodology of Signal Coordination at Network Level

Goal of coordination is to get the greatest number of vehicles through the system with the fewest stops in a comfortable manner But the problems like congestion, delay, energy consumption, environmental pollution, etc still remain in question if the traffic signals are not coordinated. Coordination of signals is achieved when the flow of traffic on a given phase of movement at one intersection is accommodated by a “go’’ phase on its arrival at the next signalized intersection. It enhances progressive movement of traffic streams at some specific speed without enforced halts and reduced overall delay. It reduces the speed variations and provides smooth traffic operation, which increases capacity, decreases energy consumption and reduces air and noise pollution. In this paper, a methodology has been suggested for the pre-timed signal coordination at network level by adopting different phase plans to reduce the vehicular delay.
Some Problems about the Source of Mass in the Electroweak Theory

We review the electroweak theory to find out some noteworthy issues. In this theory, the Higgs mechanism makes the gauge bosons obtain their mass. If the vacuum states of the Higgs fields are the sources of mass for the massive gauge bosons W and Z even electron e-, then this lowest energy v of the Higgs field must be smaller than the Higgs boson of 125 GeV even smaller than the electron’s rest mass of 0.511 MeV. It shall be like the zero-point energy of a linearly harmonic oscillator and those massive gauge bosons consist of many such lowest-energy quanta. However, substituting the weak coupling constant g=0.77 into the mass equation of the W boson, it directly gives v equal to 208 GeV much heavier than the Higgs boson and the similar results have been revealed in some textbooks about twenty years ago. If it means v lower than the Higgs boson, then the lowest-energy of the Higgs field is negative! Furthermore, the scalar Higgs boson is a charge-zero (q=0) and spin-zero (S=0) massive particle so the vacuum states of the Higgs fields have the same characteristics if they were treated as the lowest-energy quanta. However, the massive gauge bosons W and Z are all spin-1 (S=1) particles and moreover, W bosons are charged. Therefore, how to constitute those massive gauges bosons from the vacuum states of the Higgs fields becomes a questionable concept. On the other hand, due to the local gauge invariance, all mass terms have to be removed for fermions and the Yukawa coupling can provide their mass through the Higgs mechanism. It is also a similar problem that the fermion like electron is a spin-1/2 (S=1/2) massive particle and how to constitute the mass of electron from the vacuum states like the spin-zero Higgs bosons is another serious problem. Those considerations cause seriously ponder whether the Yukawa coupling is the way to provide the mass of fermion? Especially, the electron-positron pair production from two photons directly tells us that the mass of electron and positron is much easier from the photon fields through the coupling above 1.02 MeV. Even for the scalar Higgs boson H0, it can come from different parent particles. We also mathematically discuss the symmetry of the scalar field Φ under the gauge transformation and find the Lagrangian still holding its symmetry even Φ ⟶ -Φ.
A Compressive Sensing Photoacoustic Imaging System Based on Digital Micromirror Device

In this paper, a compressive sensing photoacoustic imaging scheme based on Digital Micromirror Device(DMD) is built. In compressive sensing photoacoustic imaging, DMD is used as an optical mask. The mask is placed between a short-pulsed laser and biological tissues to realize the coded illumination. To realize the random illumination, the coded pattern of the mask should be changed for each laser pulse. Based on the DMD, random code patterns of the mask can be changed quickly by controlling a digital logical circuit. The illuminated tissue absorbs the optical energy to generate the ultrasonic waves. The generated ultrasonic waves along the same arc are compressed and detected by an unfocused ultrasonic transducer. After certain measurements, the photoacoustic image can be reconstructed by a suitable CS reconstruction algorithm.
Synthesis of Vibration Signals with Prescribed Power Spectral Density and Kurtosis Value

In the field of vibration qualification testing, random excitations are typically set as input in terms of a PSD profile. The physical motion at the shaker head is obtained through the application of the Inverse Fourier Transform in combination with randomized phases. The overall probability distribution of the input signal tends toward Gaussian, whereas distinctive peaks are often present in real-life random excitations, causing the probability distribution to be non-Gaussian. The parameter known as kurtosis is usually exploited to quantify the feature of non-Gaussianity. Several methods have been proposed to control kurtosis, still maintaining the desired PSD profile, in order to synthesize more realistic signals. However, kurtosis control implemented by some of these methods may be ineffective. In fact, in some cases, the response of a lightly damped system can prove closer to Gaussian than the applied excitation. This work presents two novel algorithms to effectively control kurtosis in random vibration tests are.
Finding English Equivalents of Hungarian Light Verb Constructions WG3

In this study we present our machine learningbased method to automatically identify the English equivalents of Hungarian light verb constructions (LVCs) based on a Hungarian – English parallel corpus. The main difficulties of the automatic identification of LVC translations lie in the fact that the meaning of LVCs can only partially be computed on the basis of the meanings of their parts and the way they are related to each other (semi-compositionality). Thus, the result of translating their parts literally can hardly be considered as the proper translation of the original expression. Earlier studies like Seretan (2015) evaluated the efficacy of translating multiword expressions by machine translation approaches and they showed that the automatic translation of such expressions can be complicated in the case of many languages. For this reason, various studies have been conducted that aimed at the translation of multiword expressions, mostly with the purpose of supporting machine translation systems (Monti et al., 2013). Most of these methods (Monti et al., 2015; Wehrli and Villavicencio, 2015) can automatically identify multiword expressions in various languages by using automatic approaches, then they provide different solutions for the selection of possible translation pairs. Here, our approach is based on similar principles and we present our machine learning based methods to automatically identify the English equivalents of Hungarian light verb constructions in the SzegedParalellFX corpus (Vincze, 2012), which contains manually annotated LVCs in both languages. First, we made use of the gold standard annotation of Hungarian LVCs, and generated their potential English equivalents. We supposed that the same sentence alignment unit will contain the translational equivalent of the LVC, so we applied a syntax-based method (Vincze et al., 2013) to create all the possible LVCs in the English part of the text. From the candidates, a linguist selected Table 1: Results of the baseline method and machine learning-based approach Approach Precision Recall F-measure
Rapid Path-Planning for Unstable Concentric Tube Robot Guidance

The complex, non-intuitive kinematics of concentric tube robots can make their telemanipulation challenging. Collaborative control schemes that guide the operating clinician via repulsive and attractive force feedback based on intraoperative path-planning can simplify this task. Computationally efficient algorithms, however, are required to perform rapid path-planning and to solve the inverse kinematics of the robot at interactive rates. Until now, ensuring stable and collision-free robot configurations required long periods of pre-computation to establish kinematic look-up tables. This paper presents a high-performance robot kinematics software architecture, which is used together with a multi-node computational framework to rapidly calculate dense pathplans for safe telemanipulation of unstable concentric tube robots. The proposed software architecture enables on-thefly incremental inverse-kinematics estimation at interactive rates, and is tailored to modern computing architectures with efficient multi-core CPUs. The effectiveness of the architecture is quantified with computational-complexity metrics, and in a clinically demanding simulation inspired from neurosurgery for hydrocephalus treatment. By achieving real-time pathplanning we can generate active constraints on-the-fly and support the operator in faster and more reliable execution of telemanipulation tasks.
CORRELATING LARGE SETS OF EXPERIMENTAL DATA WITH HIGH RESOLUTION COMPUTATIONAL WELD MECHANICS MODELS

The validity of 3D transient computer models to predict the mechanics of welds is judged by correlating predictions made by the computer model with data acquired from experiments, i.e., correlating virtual and real data. The authors suspect that by far the greatest uncertainty in the predictions from these computational models has been due to the sparsity in the available experimental data on material properties and the welding process itself. The goal of this paper is to present a few examples that demonstrate how large sets of experimental data provided by recent developments in sensor technology and data acquisition systems such as distributed fiber optic temperature and strain sensing optical fiber, in addition to full field and transient data such as thermographic and Digital Image Correlation (DIC) cameras can be correlated with predictions from VrWeld, a computer model for computational weld mechanics. A fiber optic system using Rayleigh backscattering is used to measure temperature and strain with a spatial resolution of 0.65 mm at a data acquisition rate of 10 Hz but it could be as high as 250 Hz. The experimental data has noise that must be filtered. To correlate the virtual data the 3D transient Computational Weld Mechanics (CWM) models had to use time steps smaller than 1 second and element size less than 1 mm. With this data, one can correlate the clocks for the experiment and the computer model. In addition, one can run Design of Experiments (DOEs) to correlate material properties such as temperature dependent thermal conductivity and boundary conditions such as convection coefficients. Some of the challenges in synchronizing real and virtual data in space and time are discussed.
Functional Analysis of Haptic Devices

This paper presents an attempt of classification of the different functional approaches one can find in the field of haptic research. Our methodology is based on a comparison of the natural interaction scheme involving a human and an object/environment with the very similar interaction scheme that exists in the mediated situation involving the haptic device. Four functional approaches emerge from our classification: Object, Human, Interaction and Task. The task-based approach is then divided into four sub-categories on the basis of topological criteria: Environment, ToolHandled, Command and Object.
NAYEL@APDA: Machine Learning Approach for Author Profiling and Deception Detection in Arabic Texts

In this paper, we describe the methods and experiments that have been used in development of our system for Author Proﬁling and Deception Detection in Arabic shared task. There are two tasks, Author Proﬁling in Arabic Tweets and Deception Detection in Arabic Texts. We have submitted three runs for each task. The proposed system depends on classical machine learning approaches namely Linear Classiﬁer, Support Vector Machine and Multilayer Perceptron Classiﬁer. Bag-of-Word with range of n-grams model has been used for feature extraction. Our sub-missions for the ﬁrst task achieved the second, seventh and third ranks. For the second task, one of our submissions outperformed all other sub-missions developed by other teams.
Augmentation techniques for video surveillance in the visible and thermal spectral range

In intelligent video surveillance, cameras record image sequences during day and night. Commonly, this demands different sensors. To achieve a better performance it is not unusual to combine them. We focus on the case that a long-wave infrared camera records continuously and in addition to this, another camera records in the visible spectral range during daytime and an intelligent algorithm supervises the picked up imagery. More accurate, our task is multispectral CNN-based object detection. At first glance, images originating from the visible spectral range differ between thermal infrared ones in the presence of color and distinct texture information on the one hand and in not containing information about thermal radiation that emits from objects on the other hand. Although color can provide valuable information for classification tasks, effects such as varying illumination and specialties of different sensors still represent significant problems. Anyway, obtaining sufficient and practical thermal infrared datasets for training a deep neural network poses still a challenge. That is the reason why training with the help of data from the visible spectral range could be advantageous, particularly if the data, which has to be evaluated contains both visible and infrared data. However, there is no clear evidence of how strongly variations in thermal radiation, shape, or color information influence classification accuracy. To gain deeper insight into how Convolutional Neural Networks make decisions and what they learn from different sensor input data, we investigate the suitability and robustness of different augmentation techniques. We use the publicly available large-scale multispectral ThermalWorld dataset consisting of images in the long-wave infrared and visible spectral range showing persons, vehicles, buildings, and pets and train for image classification a Convolutional Neural Network. The training data will be augmented with several modifications based on their different properties to find out which ones cause which impact and lead to the best classification performance.