Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features.
Classification Approaches in Neuroscience: A Geometrical Point of View

Functional magnetic resonance images (fMRI) are brain scan images by MRI machine which are taken functionally cross the time. Several studies have investigated methods analyzing such images (or actually the drawn data from them) and is interestingly growing up. For examples models can predict the behaviours and actions of people based on their brain pattern, which can be useful in many fields. We do the classification study and prediction of fMRI data and we develop some approaches and some modifications on them which have not been used in such classification problems. The proposed approaches were assessed by comparing the classification error rates in a real fMRI data study. In addition, many programming codes for reading from fMRI scans and codes for using classification approaches are provided to manipulate fMRI data in practice. The codes, can be gathered later as a package in R. Also, there is a steadily growing interest in analyzing functional data which can often exploit Riemannian geometry. As a prototypical example of these kind of data, we will consider the functional data rising from an electroencephalography (EEG) signal in Brain-Computer interface (BCI) which translates the brain signals to the commands in the machine. It can be used for people with physical inability and movement problems or even in video games, which has had increased interest. To do that, a classification study on EEG signals has been proposed, while, the data in hand to be classified are matrices. A multiplicative algorithm (MPM), which is a fast and efficient algorithm, was developed to compute the power means for matrices which is the crucial step in our proposed approaches for classification. In addition, some simulation studies were used to examine the performance of MPM against existing algorithms. We will compare the behavior of different power means in terms of accuracy in our classifications, which had not been discovered previously. We will show that it is hard to have a guess to find the optimal power mean to have higher accuracy depending on the multivariate distribution of data available. Then, we also develop an approach, combination of power means, to have the benefit of all to improve the classification performance. All the codes related to the fast MPM algorithms and the codes for manipulating EEG signals in classification are written in MATLAB and can be developed later as a package.
A simple method for isolation of genomic DNA from fresh and dry leaves of Terminalia arjuna (Roxb.) Wight and Arnot

Current protocols for isolation of genomic DNA from Terminalia arjuna have their own limitations due to the presence of high content of gummy polysaccharides and polyphenols. DNA isolated by these protocols is contaminated with a yellowish, sticky and viscous matrix. In our modified DNA isolation method polysaccharides and polyphenols are removed prior to the precipitation of the DNA. Then the genomic DNA was precipitated using isopropanol. This protocol yielded a high molecular weight DNA isolated from fresh as well as dry leaves of T. arjuna , which was free from contamination and colour. Isolated DNA can be used for restriction digestion and PCR amplification. The main objective of the present protocol is to provide a simple method of isolation of DNA, using in house prepared reagents.
Analysis of Gene Expression in Haemocytes of Shrimp Penaeus monodon Challenged with White Spot Syndrome Virus by cDNA Microarray

A cDNA microarray technique was employed to identify differentially-expressed genes in the black tiger shrimp (Penaeus monodon) challenged with white spot syndrome virus (WSSV). The cDNA chip was composed of 718 unique genes from P. monodon and 308 unique genes from the kuruma shrimp (Marsupenaeus japonicus). The cDNA arrays were hybridized with Cy3/Cy5-labeled probes generated from the haemocyte RNA of P. monodon injected with WSSV. The gene expression profile was determined at different time points after injection. The results showed differential expression in haemocytes of WSSV-challenged P. monodon. The number of up- and down-regulated genes was highest at 24 h after WSSV injection. The pathogen invasion resulted in up-regulation of ribosomal proteins, indicating an increase in protein synthesis. The expressions of several immune-related genes also changed. The mRNA expressions of three highly responsive genes (calmodulin, tubulin and asialoglycoprotein receptor) in response to shrimp pathogens, WSSV and the luminescence bacteria Vibrio harveyi, were verified by real-time PCR and confirmed the involvement of these genes in shrimp defense. Our results suggested the WSSV infection altered the expression of a wide range of cellular genes. The microarray analysis identified several clones with unknown function that were up- regulated by WSSV infection. The roles of these clones in shrimp immune response need to be further investigated.
Development of the national register of radiation workers: subsystem for individual monitoring of external exposure

Purpose To develop a national registry and reporting system of individual monitoring for workers occupationally exposed to ionizing radiation.Methods In accordance with the relevant law,regulations,standards and the current health supervision practice for radiation workers in China,to ensure more effective collection of information on individual monitoring from all levels of service providers across the country and an easy query and analysis of the collected information for both service providers and administrative institutions,the register consisted of an offline-system and a web-based information system.The off-line system consisted of 8 tables,which could easily make annual and period monitoring reports,and upload individual monitoring data in compressed and encrypted format.Web-based system consisted of 6 modules,could easily make S customized tabulations of monitoring data and show 2 trend figures.SSLVPN secure remote access was used in the system.Arranged by the Ministry of Health,training courses provided to all individual monitoring service providers and provincial administrative institutions.Results A new and individual-based national register and reporting system of individual monitoring for workers occupationally exposed to ionizing radiation was successfully developed,and would be officially run soon.Conclusions The establishment and running of the register would be great improvement on the national radiological health reports and produce a far-reaching impact on the individual monitoring in China. Key words: Occupational external exposure; Individual monitoring; Central information system; Registry; Reporting system
Camera trapping of large mammals in Yenice Forest, Turkey: local information versus camera traps

Abstract Information on large mammals in Turkey is limited, and widely applicable, reliable field methods need to be used to gather appropriate data for conservation and management. To evaluate local information on mammal species we conducted interview and ground surveys, followed by a camera trap survey, during January–May 2006 in Yenice Forest, a globally important and intact region for mammals in Turkey. Interviews with local people provided information on the occurrence of wolf Canis lupus, brown bear Ursus arctos, wild cat Felis silvestris, red fox Vulpes vulpes, Eurasian badger Meles meles, pine marten Martes martes, roe deer Capreolus capreolus, wild boar Sus scrofa and Eurasian lynx Lynx lynx. The ground and camera trap surveys confirmed the presence of all these species except lynx. In addition, the camera trap survey documented the presence of jackal Canis aureus and brown hare Lepus europaeus, signs of which were not found in the ground survey and whose presence was not known by local people. Local information on wildlife is important but management and conservation initiatives should not solely depend on such information and, as our study indicates, interview surveys cannot replace field research. The Turkish Ministry of Environment and Forestry needs to consider the establishment of a protected area large enough to secure the future of the large mammals of Yenice Forest.
Prediction of gas chromatography retention of BTEX and other substituted benzenes based on quantum chemical parameters

Quantitative structure-retention relationship (QSRR) models for gas chromatography retention times of BTEX (benzene, toluene, xylenes and ethylbenzene) and other substituted benzenes on DB-624, DB-wax and DB-1 have been developed. The B3LYP hybrid density functional level in the Gaussian 03 program were used to calculate molecular parameters such as dipole moments (), molecular volumes (V), molecular radius (r), the lowest unoccupied molecular orbital (LUMO), the highest occupied molecular orbital (HOMO), Mulliken charges (Q) and Mulliken negative charges with hydrogens summed into heavy atoms in molecule (QH). Using multiple linear regression method, the correlation between retention time and structural parameters was obtained. Empirical functions with high correlation coefficient are appropriate for the prediction of retention times of BTEX and similar compounds. Key words: Gas chromatography, retention time, quantitative structure-retention relationship, density functional theory.
Supply chain network optimization based on collective intelligence and agent technologies

The supply chain network optimization is a difficult problem to solve in the context of distributed (information across different members) and dynamic (changes in the structure and content of the information) environment with multidisciplinary decisions. In this paper, we address this problem from a dynamic optimization of local decisions point of view, to ensure a global optimum for the supply chain performance. This is done under the frameworks of Collective Intelligence (COIN) theory and Multi-Agent Systems (MAS). By COIN, we mean a large MAS where there is no centralized control and communication, but also, where there is a global task to complete: the global supply chain network optimization. The proposed model focuses on the interactions at local and global levels between agents in order to improve the overall supply chain business process behavior. Besides, collective learning consists of adapting the local behavior of each agent (micro-learning) to the optimization of the behavior globally (macro-learning). Reinforcement learning algorithms are used at the local level, while generalization of the Q-neural algorithm is proposed to optimize the global behavior. The model is implemented within the multiagent framework for supply chain modeling and optimization over JADE agent platform. Experimental results are discussed.
Animating Through Warping: An Efficient Method for High-Quality Facial Expression Animation

Advances in deep neural networks have considerably improved the art of animating a still image without operating in 3D domain. Whereas, prior arts can only animate small images (typically no larger than 512x512) due to memory limitations, difficulty of training and lack of high-resolution (HD) training datasets, which significantly reduce their potential for applications in movie production and interactive systems. Motivated by the idea that HD images can be generated by adding high-frequency residuals to low-resolution results produced by a neural network, we propose a novel framework known as Animating Through Warping (ATW) to enable efficient animation of HD images. Specifically, the proposed framework consists of two modules, a novel two-stage neural-network generator and a novel post-processing module known as Animating Through Warping (ATW). It only requires the generator to be trained on small images and can do inference on an image of any size. During inference, an HD input image is decomposed into a low-resolution component(128x128) and its corresponding high-frequency residuals. The generator predicts the low-resolution result as well as the motion field that warps the input face to the desired status (e.g., expressions categories or action units). Finally, the ResWarp module warps the residuals based on the motion field and adding the warped residuals to generates the final HD results from the naively up-sampled low-resolution results. Experiments show the effectiveness and efficiency of our method in generating high-resolution animations. Our proposed framework successfully animates a 4K facial image, which has never been achieved by prior neural models. In addition, our method generally guarantee the temporal coherency of the generated animations. Source codes will be made publicly available.
Discovering indicators of dark horse of soccer games by deep learning from sequential trading data

It is not surprise for machine learning models to provide decent prediction accuracy of soccer games outcomes based on various objective metrics. However, the performance is not that decent in terms of predicting difficult and valuable matches. A deep learning model is designed and trained on a real sequential trading data from the real prediction market, with the assumption that such trading data contain critical latent information to determine the game outcomes. A new loss function is proposed which biases the selection toward matches with high investment return to train our model. Full investigation of 4669 top soccer league matches showed that our model traded off prediction accuracy for high value return due to a certain ability to detect dark horses. A further try is conducted to depict some indicators discovered by our model for describing key features of big dark horses and regular hot horses.
A Combinatorial Design for Cascaded Coded Distributed Computing on General Networks

Coding theoretic approaches have been developed to significantly reduce the communication load in modern distributed computing system. In particular, coded distributed computing (CDC) introduced by Li <italic>et al.</italic> can efficiently trade computation resources to reduce the communication load in MapReduce like computing systems. For the more general <italic>cascaded</italic> CDC, Map computations are repeated at <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> nodes to significantly reduce the communication load among nodes tasked with computing <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> Reduce functions <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> times. In this paper, we propose a novel low-complexity combinatorial design for cascaded CDC which 1) determines both input file and output function assignments, 2) requires significantly less number of input files and output functions, and 3) operates on heterogeneous networks where nodes have varying storage and computing capabilities. We provide an analytical characterization of the computation-communication tradeoff, from which we show the proposed scheme can outperform the state-of-the-art scheme proposed by Li <italic>et al.</italic> for the homogeneous networks. Further, when the network is heterogeneous, we show that the performance of the proposed scheme can be better than its homogeneous counterpart. In addition, the proposed scheme is optimal within a constant factor of the information theoretic converse bound while fixing the input file and the output function assignments.
An ultrasonic measurement of stress in steel without calibration: the angled shear wave identity

Measuring stress levels in loaded structures is crucial to assess and monitor their health, and to predict the length of their remaining structural life. However, measuring stress non-destructively has proved quite challenging. Many ultrasonic methods are able to accurately predict in-plane stresses in a controlled laboratory environment, but struggle to be robust outside, in a real world setting. That is because they rely either on knowing beforehand the material constants (which are difficult to acquire) or they require significant calibration for each specimen. Here we present a simple ultrasonic method to evaluate the in-plane stress in situ directly, without knowing any material constants. This method only requires measuring the speed of two angled shear waves. It is based on a formula which is exact for incompressible solids, such as soft gels or tissues, and is approximately true for compressible "hard" solids, such as steel and other metals. We validate the formula against virtual experiments using Finite Element simulations, and find it displays excellent accuracy, with a small error of the order of 1%.
Adaptive Bitrate Video Streaming for Wireless nodes: A Survey

In today's Internet, video is the most dominant application and in addition to this, wireless networks such as WiFi, Cellular, and Bluetooth have become ubiquitous. Hence, most of the Internet traffic is video over wireless nodes. There is a plethora of research to improve video streaming to achieve high Quality of Experience (QoE) over the Internet. Many of them focus on wireless nodes. Recent measurement studies often show QoE of video suffers in many wireless clients over the Internet. Recently, many research papers have presented models and schemes to optimize the Adaptive BitRate (ABR) based video streaming for wireless and mobile users. In this survey, we present a comprehensive overview of recent work in the area of Internet video specially designed for wireless network. Recent research has suggested that there are some new challenges added by the connectivity of clients through wireless. Also these challenges become more difficult to handle when these nodes are mobile. This survey also discusses new potential areas of future research due to the increasing scarcity of wireless spectrum.
Single ion-qubit exceeding one hour coherence time.

Realizing a long coherence time quantum memory is a major challenge of current quantum technology. Here, we report a single \Yb ion-qubit memory with over one hour coherence time, an order of improvement compared to the state-of-the-art record. The long coherence time memory is realized by addressing various technical challenges such as ambient magnetic-field noise, phase noise and leakage of the microwave oscillator. Moreover, systematically study the decoherence process of our quantum memory by quantum process tomography, which enables to apply the strict criteria of quantum coherence, relative entropy of coherence. We also benchmark our quantum memory by its ability in preserving quantum information, i.e., the robustness of quantum memory, which clearly shows that over 6000 s, our quantum memory preserves non-classical quantum information. Our results verify the stability of the quantum memory in hours level and indicate its versatile applicability in various scenarios.
Adaptive Hierarchical Decomposition of Large Deep Networks

Deep learning has recently demonstrated its ability to rival the human brain for visual object recognition. As datasets get larger, a natural question to ask is if existing deep learning architectures can be extended to handle the 50+K classes thought to be perceptible by a typical human. Most deep learning architectures concentrate on splitting diverse categories, while ignoring the similarities amongst them. This paper introduces a framework that automatically analyzes and configures a family of smaller deep networks as a replacement to a singular, larger network. Class similarities guide the creation of a family from course to fine classifiers which solve categorical problems more effectively than a single large classifier. The resulting smaller networks are highly scalable, parallel and more practical to train, and achieve higher classification accuracy. This paper also proposes a method to adaptively select the configuration of the hierarchical family of classifiers using linkage statistics from overall and sub-classification confusion matrices. Depending on the number of classes and the complexity of the problem, a deep learning model is selected and the complexity is determined. Numerous experiments on network classes, layers, and architecture configurations validate our results.
Dipole excitation of collective modes in viscous two-dimensional electron systems

We describe the structure of the time-harmonic electromagnetic field of a vertical Hertzian electric dipole source radiating over an infinite, translation invariant two-dimensional electron system. Our model for the electron flow takes into account the effects of shear and Hall viscosities as well as an external static magnetic field perpendicular to the sheet. We identify two wave modes, namely, a surface plasmon and a diffusive mode. In the presence of an external static magnetic field, the diffusive mode combines the features of both the conventional and Hall diffusion and may exhibit a negative group velocity. In our analysis, we solve exactly a boundary value problem for the time-harmonic Maxwell equations coupled with linearized hydrodynamic equations for the flat, two-dimensional material. By numerically evaluating the integrals for the electromagnetic field on the sheet, we find that the plasmon contribution dominates in the intermediate-field region of the dipole source. In contrast, the amplitude of the diffusive mode reaches its maximum value in the near-field region, and quickly decays with the distance from the source. We demonstrate that the diffusive mode can be distinguished from the plasmon in the presence of the static magnetic field, when the highly oscillatory plasmon is gapped and tends to disappear.
Improved BBN constraints on Heavy Neutral Leptons.

We constrain the lifetime of thermally produced Heavy Neutral Leptons (HNLs) from primordial nucleosynthesis. We show that even a small fraction of mesons present in the primordial plasma leads to the over-production of the primordial helium. This puts an upper bound on the lifetime of HNLs $\tau_{N} m_{\pi}$ (as compared to 0.1 s reported previously). In combination with accelerator searches, this allows us to put a new lower bound on the HNLs masses and defining the "bottom line" for HNL searches at the future Intensity Frontier experiments.