Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending Friend-to-Friend Computing to Mobile Environments

—Friend-to-Friend (F2F) computing is a popular peer to peer computing framework, bootstrapped by instant messaging. Friend-to-Friend (F2F) Computing is a simple distributed computing concept where participants are each others friends, allowing computational tasks to be shared with each other as easily as friendship. The widespread availability of applications and services on mobile phones is one of the major recent developments of the current software industry. Due to the also emerging market for cloud computing services we mainly ﬁnd centralized structures. Friend-to-Friend computing and other Peer-to-Peer (P2P) computing solutions provide a decentralized alternative. However these are not very common in mobile environments. This paper investigates how to extend the Friend-to-Friend computing framework to mobile environments. We describe our process and point out possible pitfalls and achievements. For this investigation, we ported our private cloud environment Friend-to-Friend Computing to Android and Symbian. We demonstrate a mobile gaming application using Friend-to-Friend Mobile.
Deep autoencoders for ATLAS data compression

Storage is one of the main limiting factors to the recording of information from proton-proton collision events at the Large Hadron Collider (LHC), at CERN in Geneva. Hence, the ATLAS experiment at the LHC uses a so-called trigger system, which selects and transfers interesting events to the data storage system while ﬁltering out the rest. However, if interesting events are buried in very large backgrounds and diﬃcult to identify as a signal by the trigger system, they will also be discarded together with the background. To alleviate this problem, diﬀerent compression algorithms are already in use to reduce the size of the data that is recorded. One of those state-of-the-art algorithms is an autoencoder network that tries to implement an approximation to the identity, f ( x ) = x , and given some input data, its goal is to create a lower-dimensional representation of those data in a latent space using an encoder network. This way when collisions happen on the ATLAS Collider, we run the encoder on the produced data and we save only the latent space representation. Then using this latent representation oﬄine the decoder network can reconstruct the original data.
Mean level effects for learning speed of red junglefowl chicks in discriminative and reversal learning tests

S3) Associative learning Simulations of false positive learners. Figure S3-I. Number of false positives when allowing observed max number of choices for all individuals. Figure S3-II. Number of false positives when allowing individual max number of choices as observed in our experiment. Figure S3-III. Number of individuals expected to reach consecutive correct choices by chance (510), and numbers of individuals observed reaching these criteria.
Boundaries Predictability: Noisy Predictive Regressions

: Even if returns are truly forecasted by variables such as the dividend yield, the noise in such a predictive regression may overwhelm the signal of the conditioning variable and render estimation, inference and forecasting unreliable. Unfortunately, traditional asymptotic approximations are not suitable to investigate the small sample properties of forecasting regressions with excessive noise. To systematically analyze predictive regressions, it is useful to quantify a forecasting variable’s signal relative to the noisiness of returns in a given sample. We define an index of signal strength, or information accumulation, by renormalizing the signal-noise ratio. The novelty of our parameterization is that this index explicitly influences rates of convergence and can lead to inconsistent estimation and testing, unreliable R2s, and no out-of-sample forecasting power. Indeed, we prove that if the signal-noise ratio is close to zero, as is the case for many of the explanatory variables previously suggested in the finance literature, model based forecasts will do no better than the corresponding simple unconditional mean return. Our analytic framework is general enough to capture most of the previous findings surrounding predictive regressions using dividend yields and other persistent forecasting variables. Abstract Even if returns are truly forecasted by variables such as the dividend yield, the noise in such a predictive regression may overwhelm the signal of the conditioning variable and render estimation, inference and forecasting unreliable. Unfortu-nately, traditional asymptotic approximations are not suitable to investigate the small sample properties of forecasting regressions with excessive noise. To systematically analyze predictive regressions, it is useful to quantify a forecasting variable’s signal relative to the noisiness of returns in a given sample. We de ﬁ ne an index of signal strength, or information accumulation, by renormalizing the signal-noise ratio. The novelty of our parameterization is that this index explicitly in ﬂ uences rates of convergence and can lead to inconsistent estimation and testing, unreliable R 2 s, and no out-of-sample forecasting power. Indeed, we prove that if the signal-noise ratio is close to zero, as is the case for many of the explanatory variables previously suggested in the ﬁ nance literature, model based forecasts will do no better than the corresponding simple unconditional mean return. Our analytic framework is general enough to capture most of the previous ﬁ ndings surrounding predictive regressions using dividend yields and other persistent forecasting variables. periods ahead, where k=0.0 1 × T. Very similar results are obtained for fractions other than 0.0 1 .
EXPERIMENTAL DISCRETIZATION FOR DETERMINATION OF STRAIN STATE

: The determination of working piece strain state at bulk metal forming in open dies of axisymetrical pieces has been carried out in this paper. Preparation pieces made in appropriate way have been used in investigations. Preparation pieces are made out of segments, thus they are physically discreeted, so that such a method is said to be the method of physical discretization (MPD). By a suitable mechanical and chemical forming it is possible to obtain a deformed image of a meridial cross-section and determine a deformation state, which has been done. Finally, the results and given directions for further investigations in the sense of improving the method have been analyzed.
Vital Young Adults: Hopeful and Gritty

The aim of the current study was to explore the relationship among grit, hope and subjective vitality in young adults. It also investigated the gender difference in the aforementioned variables. For this purpose a sample of 120 young adults, 60 males and 60 females, pursuing graduate and post graduate programmes residing in Delhi NCR was taken. Item Grit Scale, Adult Hope Scale and Subjective Vitality Scale were used. In order to find the relationship between the study variables, Pearson correlation was run. To find out the gender difference for the variables under study, t-test was applied. The results indicated a significant relationship between hope and subjective vitality. Non-significant differences between male and female young adults on grit, hope and subjective vitality were obtained.
Research on the Current Situation of Residents' Participation in Garbage Classification and Its Influencing Factors: Take Guangdong Province of China as an Example

: In recent years, with the rapid development of China's economic level, people's material consumption level continues to improve, the corresponding waste production is also growing rapidly. According to garbage statistics released by the Ministry of Housing and Urban-Rural Development, China produces more than 400 million tons of household waste every year, so it is imperative to carry out garbage sorting. In the process of garbage classification and governance, people's participation in the source classification of MSW is the key to the success of garbage classification and governance. This paper uses questionnaire survey to obtain large-scale data, through the construction of multiple linear regression model, to find out the key factors affecting the garbage classification behavior of residents in Guangdong Province. The study showed that 93.69% of respondents supported the classification of MSW; 92.63% of the respondents think it is necessary for Guangdong province to implement household waste classification. The key factors affecting residents' willingness and behavior of garbage classification are different, among which there are 3 cognitive factors, 5 hardware convenience measures and 3 situational factors. The key factors affecting residents' garbage classification behavior include three cognitive factors, one hardware convenience factor and one situational factor. Finally, the paper puts forward countermeasures and suggestions to promote residents' participation in garbage classification behavior.
Yield, composition, fatty acid profile and CLA content of milk from goats fed with different levels of OPF

The objective of this study was to evaluate the effect of different inclusion levels of OPF in total mixed ration (TMR)-based diet on yield, composition, FA profile and CLA content of milk in dairy goats. Six nulliparous female dairy goats (Saanen x Thai native, 19-21 months old, first lactation) were randomly set according to a 3x3 Latin square design with 2 replications for receiving diets consisted of 0% OPF (OPF0), 20% OPF (OPF20) and 40% OPF (OPF40). The average weight of the goats was 32.48±1.30 kg (mean±s.d.). The findings of this study showed that dry matter intake (DMI) and all nutrient intake of the goats increased significantly as OPF inclusion was increased ( p <0.05), excluding crude protein intake (CPI) ( p >0.05). Goats from OPF40 group had the highest CPI, however, there was no significant difference ( p >0.05) in CPI of OPF20 and OPF0 groups. Feeding OPF altered the composition of protein and lactose, density and freezing point ( p <0.05). However, it did not influence ( p >0.05) the composition of fat, solid non-fat and minerals in the milk. There were no significant differences in oleic acid, dihomo-γ-linoleic acid, and eicosapentaenoic acid in milk fat of goats ( p >0.05). 40% OPF inclusion increased the production of LnA, DHA, CLA and the ratio of PUFA:SFA; and lowered AA production and n-6:n-3 ratio of goat milk ( p >0.05). It can be concluded that 40% OPF inclusion in TMR-based diet can be used as an alternative to producing goat’s milk with enriched potential health benefits.
EVALUATION OF PRODUCED WATER DISCHARGEIN THE NIGER-DELTA

Water produced during petroleum production often contain chemicals, oil and sometimes, naturally occurring radioactive materials which could harm the environment. Management, which involves significant cost and is guided by strict regulations, is normally aimed at minimizing or reducing the toxicity of discharged volumes. This study evaluated the physico-chemical properties and constituents of produced water from three selected flowstations and two oil terminals in the Niger Delta to determine the extent of compliance with standards and global best practices in the treatment and discharge. It was observed that while physico-chemical properties like pH (8.4±0.25) and BOD (6.2±2.0) were within recommended limits, oil and grease content (60±20), TDS (6200±700) and others exceeded limits. Similarly, ion concentrations of Lead, Nickel, Zinc and Sulphates were within acceptable limits while chloride ions (4100 ±500) exceeded limits. There is therefore the need to improve treatment facilities and procedures, while regulatory agencies must improve current guidelines, ensure proper monitoring and enforcing compliance.
ATLAS NOTE ATLAS-CONF-2010-046 Charged particle multiplicities in pp interactions for track p T > 100 MeV

New measurements are presented from proton-proton collisions at centre-of-mass energies of √ s = 0.9 and 7 TeV recorded with the ATLAS detector at the LHC. The 0.9 TeV data uses the same runs as the ﬁrst publication, while the 7 TeV data sample is increased to a total of ten million events, corresponding to an integrated luminosity of ∼ 190 µ b − 1 . These events were all collected using a single-arm minimum-bias trigger. The charged particle multiplicity, its dependence on transverse momentum and pseudorapidity, and the relation-ship between mean transverse momentum and charged particle multiplicity are measured for events with at least two charged particles in the kinematic range | η | < 2.5 and p T > 100 MeV. This analysis follows closely the strategy used in our earlier √ s = 0 . 9 TeV publication and the √ s = 7 TeV CONF note. The principle improvement with respect to previous analyses is the lowering of the track p T threshold from 500 MeV to 100 MeV. The results are compared to various Monte Carlo models, including the new ATLAS AMBT1 tune. The charged particle multiplicity per event and unit of pseudorapidity for η = 0 is measured to be 5 . 635 ± 0 . 002(stat . ) ± 0 . 149(syst . ) at 7 TeV and 3 . 486 ± 0 . 008(stat . ) ± 0 . 077(syst . ) at 0.9 TeV.
An for Ultralow Power Binary-Weight

—Convolutional Neural Networks (CNNs) have rev- olutionized the world of computer vision over the last few years, pushing image classiﬁcation beyond human accuracy. The computational effort of today’s CNNs requires power-hungry parallel processors or GP-GPUs. Recent developments in CNN accelerators for system-on-chip integration have reduced energy consumption signiﬁcantly. Unfortunately, even these highly optimized devices are above the power envelope imposed by mobile and deeply embedded applications and face hard limi- tations caused by CNN weight I/O and storage. This prevents the adoption of CNNs in future ultra-low power Internet of Things end-nodes for near-sensor analytics. Recent algorithmic and theoretical advancements enable competitive classiﬁcation accuracy even when limiting CNNs to binary (+1/-1) weights during training. These new ﬁndings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as reducing I/O bandwidth and storage. In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1.5TOp/s at 1.2V on a core area of only 1.33MGE (Million Gate Equivalent) or 1.9mm 2 and with a power dissipation of 895µW in UMC 65nm technology at 0.6V. Our accelerator signiﬁcantly outperforms the state-of-the-art in terms of energy and area efﬁciency achieving 61.2TOp/s/W@0.6V and 1.1TOp/s/MGE@1.2V, respectively.
DYNAMIC MODELLING AND SIMULATION OF A THREE-PHASE GRAVITY SEPARATOR

This paper presents a first principles dynamic model of a horizontal three-phase gravity separator equipped with an interface control and weir. A number of assumptions were made: phase separation is complete; the vapour phase behaves like an ideal gas and liquid densities are constant. In practice, separators are designed with internal baffles to promote laminar flow, but it was assumed that no baffles are present. Nevertheless, the resulting set of equations is quite comprehensive and easily tailored to similar separators of different dimensions. The simulation results revealed that the separator has an interesting mix of self-regulating and non-self regulating dynamics, giving insight into the behaviour of the separator especially when it is subject to slugs of gas in the feed. Linear transfer functions between input and output variables were extracted by subjecting the first principles model to a series of step tests. These will allow the interactions between the variables to be quantified as well as controllability analyses to be performed, thus facilitating the design of linear control strategies.
Breaking CAPTCHA using Neural Network

—CAPTCHAs has been used widely to determine human users from computer ones. In order to secure a system, a CAPTCHA must hold the property of being hard to read by a computer. In this paper we tried to encode a set of simple CAPTCHAs automatically by employing preprocessing and segmentation methods followed by a recognition phase. Experiment results show that a simple multilayer perceptron network with few hidden nodes can recognize a simpliﬁed CAPTCHA with very high accuracy. This result might help a CAPTCHA designer to add more complicated noise and features to make the segmentation hard to achieve.
Memory CD8 T Cell Responses In Vivo Imatinib Mesylate Inhibits Antigen-Specific

Imatinib mesylate (IM) is effective at inducing complete cytogenetic remission in patients with chronic myelogenous leukemia. Because its inﬂuence on CD8 T cell responsiveness in vivo is unknown, we investigated the effects of IM by analyzing the response of OT-1 CD8 T cells to Listeria monocytogenes (LM) that express the cognate epitope OVA 257–264 (LM-OVA). In vitro, IM had no effect on Ag-speciﬁc expansion, cell division, cell cycle progression, or IFN- (cid:1) expression in naive or memory OT-1 T cells. However, IM induced apoptosis of naive and memory OT-1 T cells at doses of > 5 (cid:2) M. At 15 (cid:2) M IM, OT-1 T cells did not survive in in vitro cultures. The primary response of OT-1 T cells in vivo to LM-OVA infection was unaltered. In contrast, continuous IM treatment resulted in a diminished memory OT-1 response. The expression of IL-7R (cid:3) , a receptor required for memory cell survival, was lower (on OT-1 cells) in animals receiving IM. These results indicate that IM treatment affects the ability of the CD8 memory pool to respond to Ag and has the potential to increase susceptibility to infection. The Journal of Immunology, 2007, 178: 2028–2037.
Evaluating the Performance of Electric Submersible Pump under Various Speeds of Operation

Electric submersible pumps (ESPs) are widely used in agricultural, petroleum and various other industries. These pumps are mostly driven at a constant speed since an ac motor is normally used as the drive. However, ESPs can also be operated at various speeds by the use of a variable frequency drive (VFD). The scope of this study is to experimentally investigate the performance characteristics of a multistage electric submersible pump with its power supply through a VFD. First a load test was conducted on the motor alone as per relevant Indian Standard at various supply frequencies. the data of load test on motor was used to evolve a novel method to calculate slip of the motor from the input power. This was applied to the test results of pump obtained at various frequency settings to determine its performance at different speeds.
Automatic Discrimination for Screening of the Eyes in a Deep Learning-Based Ensemble Model Using Optical Coherence Tomography Images

The average life expectancy has increased globally, and the risk of visual impairment is expected to increase as well. Therefore, eye checkups have become increasingly important to maintain good vision and quality of life. As the population requiring eye checkups increases, so does the clinical work burden of physicians. Hence, an automatic discrimination algorithm to reduce the clinicians' workload is necessary. The convolutional neural network (CNN), a deep learning algorithm, has recently become one of the chief techniques for automated image recognition and is a helpful tool for identifying ocular diseases. However, the accuracy of a single CNN model ranges within 70%–95%, and it may fail to identify some diseases. In this study, we aimed to compare the diagnostic performances of four CNN models trained with optical coherence tomography (OCT) images, the machine-learning (ML) model trained with data on the retinal and choroidal area, and the ensemble model that integrated the CNNs and ML models using OCT images obtained during eye checkups. Our results show that the ensemble model had a superior diagnostic performance over the CNN and ML models. The ML model, which evaluated diseases using data regarding the temporal peripheral retinal area, improved on the CNN model, which misrecognized the temporal peripheral retinal structures. Our study indicates the strong potential of the ensemble model combining the CNN and ML models in accurately predicting abnormalities during eye checkups.
Signaling and Posttranslational Events Responsiveness by Altering Both Early Mycolactone Suppresses T Cell

Mycolactone is a diffusible lipid toxin produced by Mycobacterium ulcerans , the causative agent of a necrotizing skin disease referred to as Buruli ulcer. Intriguingly, patients with progressive lesions display a systemic suppression of Th1 responses that resolves on surgical excision of infected tissues. In this study, we examined the effects of mycolactone on the functional biology of T cells and identiﬁed two mechanisms by which mycolactone suppresses cell responsiveness to antigenic stimulation. At noncytotoxic concentrations, mycolactone blocked the activation-induced production of cytokines by a posttranscriptional, mammalian target of rapamycin, and cellular stress-independent mechanism. In addition, mycolactone triggered the lipid-raft association and activation of the Src-family kinase, Lck. Mycolactone-mediated hyperactivation of Lck resulted in the depletion of intracellular calcium stores and downregulation of the TCR, leading to impaired T cell responsiveness to stimulation. These biochemical alterations were not observed when T cells were exposed to other bacterial lipids, or to structurally related immunosuppressors. Mycolactone thus constitutes a novel type of T cell immunosuppressive agent, the potent activity of which may explain the defective cellular responses in Buruli ulcer patients. The Journal of Immunology , 2010, 184: 1436–1444.
A NONLINEAR MULTIGRID METHOD FOR CURVATURE EQUATIONS RELATED TO TOTAL VARIATION MINIMIZATION

Image restoration has been an active research topic in the last few years. While much theory as well as many useful models and methods have been developed, there still exists a need to develop fast iterative solvers. This paper proposes to use piecewise linear function spanned subspace correction to design a multilevel method for directly solving the total variation minimisation. Both the theoretical and experimental results are presented.
Deep Learning for Object Tracking in 3D Point Clouds

Great progress has been achieved in computer vision tasks within image and video, however technological advances in LiDAR sensors have created a whole new area of computer vision research devoted to it. With applications in many industries, such as transportation, agriculture or healthcare. This thesis studies object tracking in 3D point clouds. Pairs of point cloud observations are feed to a neural network to estimate pose and translation between the observations. Then this estimations, together with external features, are processed with Kalman Filter and RNN to extract spatial-temporal redundancies and improve the results. The system has been tested in the KITTI dataset, with pre-segmented observations, on different types of objects and paths. The results show that the neural network estimated pose gives a very accurate tracking, but the best results are achieved when combining the estimated pose and translations with a recurrent neural network.