Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive MIMO Radar for Target Detection

Since the seminal paper by Marzetta from 2010, the Massive MIMO paradigm in communication systems has changed from being a theoretical scaled-up version of MIMO, with an infinite number of antennas, to a practical technology. Its key concepts have been adopted in the 5G new radio standard and base stations, where 64 fully-digital transceivers have been commercially deployed. Motivated by these recent developments, this paper considers a co-located MIMO radar with <inline-formula><tex-math notation="LaTeX">$M_T$</tex-math></inline-formula> transmitting and <inline-formula><tex-math notation="LaTeX">$M_R$</tex-math></inline-formula> receiving antennas and explores the potential benefits of having a large number of virtual spatial antenna channels <inline-formula><tex-math notation="LaTeX">$N=M_TM_R$</tex-math></inline-formula>. Particularly, we focus on the target detection problem and develop a <italic>robust</italic> Wald-type test that guarantees certain detection performance, regardless of the unknown statistical characterization of the disturbance. Closed-form expressions for the probabilities of false alarm and detection are derived for the asymptotic regime <inline-formula><tex-math notation="LaTeX">$N\rightarrow \infty$</tex-math></inline-formula>. Numerical results are used to validate the asymptotic analysis in the finite system regime with different disturbance models. Our results imply that there always exists a sufficient number of antennas for which the performance requirements are satisfied, without any a-priori knowledge of the disturbance statistics. This is referred to as the Massive MIMO regime of the radar system.
Factors affecting the vertical distribution of eggs

The spatial distribution of eggs and larvae is a function of the properties of the ambient water (the density, current, and turbulent diffusion) and of the physical properties of the eggs (the buoyancy and dimension). Study of the vertical distribution is the first step towards understanding the horizontal transport of eggs and larvae. Two models are applied to demonstrate how the physical and biological conditions influence the vertical distribution of the three main categories of eggs, here defined as pelagic, bathypelagic, and bottom eggs. In particular, the physical conditions affecting the distribution of bathypelagic eggs are studied. Wind-induced turbulence, the most important ambient factor for the vertical distribution of pelagic eggs and larvae, contributes to mixing the buoyant eggs and larvae throughout the wind-mixed layer. The vertical spreading of bathypelagic eggs depends mainly on the buoyancy distribution of the eggs. The model demonstrates that non-adhesive demersal eggs will be partly mixed into the water column, this mechanism contributing to the horizontal transport of demersal eggs.
Phase space-based evaluation of numerical forecasts of cyclone structure evolution

Evaluations of the accuracy of the track and intensity forecasts is the traditional mode of verification for operational model simulations of tropical cyclones. While these characteristics are clearly important for identifying areas at most risk from the high wind core of a particular storm, the regions of damaging winds, precipitation, and wave activity are typicallly much greater in extent than the radius of maximum winds. Since cyclone structure is directly linked to the intensity and distribution of these sensible weather effects, mapping the evolution of the broadscale storm structure provides guidance on the likely intensity and distribution of its associated weather. Although structure is clearly an important cyclone characteristic, validation of cyclone structure forecasts in operational numerical models has not been performed previously. The process of extratropical transition (ET) leads to immense changes in cyclone structure, thus a subset of storms that undergo ET provides an ideal testbed for evaluating structure change forecast skill in an operational model. In this paper we propose a novel method for assessing operational forecasts of tropical cyclone lifecycles. The Cyclone Phase Space (CPS) of Hart (2003) is used to characterize and compare analyzed and forecast cyclone structures from operational numerical models. The CPS provides an objective measure of cyclone structure for model forecast validation and has been shown to capture the onset and completion of ET (Evans and Hart 2003). CPS-derived diagnoses of storm structure forecasts from the AVN and NOGAPS are compared with their verifying analyses out to 36 h. We employ the 7-cluster k-means solution of Arnott et al. (2004) as a baseline for these comparisons, with the goal of developing an objective reference frame for the validation of numerical model forecasts of storm structure.
Galaxy groups in the Two-degree Field Galaxy Redshift Survey: the luminous content of the groups

The Two-degree Field Galaxy Redshift Survey (2dFGRS) Percolation-Inferred Galaxy Group (2PIGG) catalogue of ∼29 000 objects is used to study the luminous content of galaxy systems of various sizes. Mock galaxy catalogues constructed from cosmological simulations are used to gauge the accuracy with which intrinsic group properties can be recovered. It is found that a Schechter function is a reasonable fit to the galaxy luminosity functions in groups of different mass in the real data, and that the characteristic luminosity L⋆ is slightly larger for more massive groups. However, the mock data show that the shape of the recovered luminosity function is expected to differ from the true shape, and this must be allowed for when interpreting the data. Luminosity function results are presented in both the bJ and rF wavebands. The variation of the halo mass-to-light ratio, ϒ, with group size is studied in both of these wavebands. A robust trend of increasing ϒ with increasing group luminosity is found in the 2PIGG data. Going from groups with bJ luminosities equal to 1010 h−2 L⊙ to those 100 times more luminous, the typical bJ-band mass-to-light ratio increases by a factor of 5, whereas the rF-band mass-to-light ratio grows by a factor of 3.5. These trends agree well with the predictions of the simulations which also predict a minimum in the mass-to-light ratio on a scale roughly corresponding to the Local Group. The data indicate that if such a minimum exists, then it must occur at L≲ 1010h−2 L⊙, below the range accurately probed by the 2PIGG catalogue. According to the mock data, the bJ mass-to-light ratios of the largest groups are expected to be approximately 1.1 times the global value. Assuming that this correction applies to the real data, the mean bJ luminosity density of the Universe yields an estimate of Ωm= 0.26 ± 0.03 (statistical error only). Various possible sources of systematic error are considered, with the conclusion that these could affect the estimate of Ωm by a few tens of per cent.
Problems with the EFG formalism: a solution attempt using observations

We argue that the extensive-form game (EFG) model isn't powerful enough to express all important aspects of imperfect information games, such as those related to decomposition and online game solving. We present a principled attempt to fix the formalism by considering information partitions that correspond to observations. We show that EFGs cannot be "fixed" without additional knowledge about the original task, and show how to extend the EFG model under this assumption. However, during our investigation, we ultimately concluded that a better solution is to abandon EFGs entirely and instead adopt a model based on partially observable stochastic games. The primary contribution of the text thus lies in exposing the problems with EFGs and presenting a detailed study on introducing imperfect information by adding observations into an underlying perfect-information model.
Video-Driven Speech Reconstruction using Generative Adversarial Networks

Speech is a means of communication which relies on both audio and visual information. The absence of one modality can often lead to confusion or misinterpretation of information. In this paper we present an end-to-end temporal model capable of directly synthesising audio from silent video, without needing to transform to-and-from intermediate features. Our proposed approach, based on GANs is capable of producing natural sounding, intelligible speech which is synchronised with the video. The performance of our model is evaluated on the GRID dataset for both speaker dependent and speaker independent scenarios. To the best of our knowledge this is the first method that maps video directly to raw audio and the first to produce intelligible speech when tested on previously unseen speakers. We evaluate the synthesised audio not only based on the sound quality but also on the accuracy of the spoken words.
THE EFFECTIVE TRIAD: IMPACT, DIGITAL CONTENT AND ADULT EDUCATION. A CASE STUDY APPROACH

Lifelong learning, continuous learning, accessibility and quality of education and training systems in Europe play a decisive role in attaining the development of knowledge-based society. The paradigm of e-learning is expansively growing in adult education. The purpose of this paper is to assess the impact of e-learning and digital technologies in adult education. To achieve this goal a research study was conducted. The study proposed in this paper aims to point out the answers to the following questions: Does the digital content bring significant changes in adult learners? Is this type of educational content valuable for adult training? What are the contexts and conditions to produce effective learning by using digital content and technologies? The paper consists of four parts. The introduction is focused on the state of the art regarding e-learning, digital technologies and adult education. The second section describes the research design: research problem and questions, research goal and hypotheses, research methodology, research instruments and data analysis procedures. Furthermore, the third section discusses the research results. The fourth part reveals the conclusions of the study.
An update on atmospheric ice estimates from satellite observations and reanalyses

This study assesses the global distribution of mean atmospheric ice mass from current state-of-the-art estimates and its variability on daily and seasonal timescales. Ice water path (IWP) retrievals from active and passive satellite platforms are analysed and compared with estimates from two reanalysis data sets, ERA5 (European Centre for Medium-range Weather Forecasts Reanalysis 5, ECMWF) and MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications 2). Large discrepancies in IWP exist between the satellite data sets themselves, making validation of the model results problematic and indicating that progress towards a consensus on the distribution of atmospheric ice has been limited. Comparing the data sets, zonal means of IWP exhibit similar shapes but differing magnitudes, with large IWP values causing much of the difference in means. Diurnal analysis centred on A-Train overpasses shows similar structures in some regions, but the degree and sign of the variability varies widely; the reanalyses exhibit noisier and higher-amplitude diurnal variability than borne out by the satellite estimates. Spatial structures governed by the atmospheric general circulation are fairly consistent across the data sets, as principal component analysis shows that the patterns of seasonal variability line up well between the data sets but disagree in severity. These results underscore the limitations of the current Earth observing system with respect to atmospheric ice, as the level of consensus between observations is mixed. The large-scale variability of IWP is relatively consistent, whereas disagreements on diurnal variability and global means point to varying microphysical assumptions in retrievals and models alike that seem to underlie the biggest differences.
Stochastic Proximal AUC Maximization

In this paper we consider the problem of maximizing the Area under the ROC curve (AUC) which is a widely used performance metric in imbalanced classification and anomaly detection. Due to the pairwise nonlinearity of the objective function, classical SGD algorithms do not apply to the task of AUC maximization. We propose a novel stochastic proximal algorithm for AUC maximization which is scalable to large scale streaming data. Our algorithm can accommodate general penalty terms and is easy to implement with favorable $O(d)$ space and per-iteration time complexities. We establish a high-probability convergence rate $O(1/\sqrt{T})$ for the general convex setting, and improve it to a fast convergence rate $O(1/T)$ for the cases of strongly convex regularizers and no regularization term (without strong convexity). Our proof does not need the uniform boundedness assumption on the loss function or the iterates which is more fidelity to the practice. Finally, we perform extensive experiments over various benchmark data sets from real-world application domains which show the superior performance of our algorithm over the existing AUC maximization algorithms.
Optically-athermalized construction optical design for the IMACS short camera

The optical performance of a large, optically fast, all-refracting spectrograph camera is extremely sensitive to potential temperature changes which might occur during an extended signle observation, over the duration of an observing run, and/or on seasonal time scales. A small temprature change, even at the level of a few degrees C, will lead to changs in the rerfractive indices of the glasses and the coupling medium, changes in the lens-element geometries and in the dimensions of the lens cell. These effects combine in a design-specific manner to cause potential changes of focus and magnification within the camera as well as inherent loss of image quality. We have used an optical design technique originally developed for the Smithsonian Astrophysical Observatory's BINOSPEC instrument in order to produce a construction optical design for the Carnegie IMACS Short camera. This design combines the above-mentioned temperature-dependent parameter variations in such a way that their net effect upon focus and magnification is passively reduced to negligible residuals, without the use of high-expansion plastics, "negative-c.t. e. " mechanisms or active control within the lens cell. Simultaneously, the design is optimized for best inherent image quality at any temperature within the designated operating range. The optically-athermalized IMACS Short camera is under construction. We present its quantitative optical design together with our assessment of its expected performance over a (T = -4.0 to +20.0) C temperature range.
Joint Concept Matching-Space Projection Learning for Zero-Shot Recognition

Zero-shot learning (ZSL) has been widely researched and achieved a great success in machine learning, which aims to recognize unseen object classes by only training on seen object classes. Most existing ZSL methods are typically to learn a projection function between visual feature space and semantic space and mainly suffer a projection domain shift problem, as there is often a large domain gap between seen and unseen classes. In this paper, we proposed a novel inductive ZSL model based on project both visual and semantic features into a common distinct latent space with class-specific knowledge and reconstruct both visual and semantic features by such a distinct common space to narrow the domain shift gap. We show that all these constraints of the latent space, class-specific knowledge, reconstruction of features and their combinations enhance the robustness against the projection domain shift problem and improve the generalization ability to unseen object classes. Comprehensive experiments on four benchmark datasets demonstrate that our proposed method is superior than state-of-the-art algorithms.
Microbial autotrophic and heterotrophic eucaryotes in Antarctic waters : relationships between biomass and chlorophyll, adenosine triphosphate and particulate organic carbon

M~croscopical examination of near-surface eucaryotic microbial populations in circumcontinental waters of Antarctica indicated that nanoplankton ((20 btm diameter) dominated in regions with low chlorophyll concentrations ( 20 pm diameter) was generally less significant. The variation in phytoplankton biomass was the result primarily of changes in cell density of pennate diatoms in the East Wind Drift, and of centric diatoms in the Weddell Sea and the Scotia Ridge region. Autotrophic and heterotrophic carbon as determined by microscopical analysis were compared with data for total particulate carbon, chlorophyll a, and adenosine triphosphate. Estimates for the C:chl ratio of autotrophs increased with decreasing concentrations of chlorophyll a, with mean values of 46 in bloom waters and 144 in 'blue water'. A C:ATP ratio for heterotrophic nanoplankton was estimated to be about 100, while that for heterotrophic microplankton may be lower. Algorithms, incorporating concentrations of chlorophyll a and ATP, are described which allow estimates of autotrophc and heterotrophic microbial biomass.
EVALUATION OF ANTIBACTERIAL ACTIVITY OF ETHANOLIC EXTRACTS OF MIMOSA PUDICA LEAVES

Objective: This study was carried out with an objective to investigate the antibacterial activity of ethanolic extracts of mimosa pudica. Methods: In the present study, the anti microbial activity of ethanolic extracts of Mimosa pudica was evaluated against medically important bacterial strains, two Gram-positive— Staphylococcus aureus and Streptococcus pyogenes and two Gram-negative—Escherichia coli and Pseudomonas aeruginosa. The antimicrobial activity was determined with the extract using agar disc diffusion method. The antibacterial activities of extracts (5, 25, 50, 100, 250 μgm/ml) of Mimosa pudica were tested against human pathogenic bacteria. Zone of inhibition of extracts were compared with the standard drug ampicillin for antibacterial activity. Results: The results showed that the Mimosa pudica has the antibacterial activity. Conclusion: The antimicrobial activity of Mimosa pudica was due to the presence of various secondary metabolites. Hence, these plants can be used to discover bioactive natural products that may serve as leads in the development of new pharmaceuticals research activities.
The Effect of Genibo Program Based Robot Learning on a Pre-Schoolers' Emotional Development

Abstract The purpose of this study was to identify the effect of Genibo program robot-based learning(R-Learning)on a pre-schooler’s mental state. To achieve above study purpose, the subject of this study was selected 46(teacher2, five years old pre-schooler 44) from pre-school childrens in Kyongki Y city(R-Learning activity participants group 21: boys 10, girls 11. non-participants 25: boys 13, girls 12). R-Learning program is consist of 5 field about 20 contents using Genibo robot, were applied to the experimental group and the pre-post test was conducted using the EQ assessment tool and observations. The data were analyzed by t-test using the SPSS(ver 18.0) program. The results were as follows: First, the exposure of robots to pre-schoolers in practical situation has shown positive influence tothe children's emotional well-being. Positive improvements were observed in the four sub categories of the EQ assessment after exposure. Second, the Genibo used for this study, is a biomimetic AI based robot mimicking thebehavior of a pet dog. This is related more or less to the specifications of a pre-school education where animals are used as a 'friendly medium' to facilitate the learning process. Third, the robot exposure gave benefit to all the onesin the sample, regardless of sex. Furthermore, It is suggested that promising potential for robots to be utilized as anew educational media plus facilitator, R-Learning is related more or less to the specifications of a pre-school education where animals are used as a ‘friendly medium' to facilitate the learning process, and when applying themfor education, stereotyping the likes of sex is overrated - instead, the focus should be more on the pre-schoolers' /childrens' individual traits, learning curve differences and alike. 요 약
A Monte Carlo Approach to the Simulation of Characteristic X-ray Yields for the APXS Instrument

A Monte Carlo Approach to the Simulation of Characteristic X-Ray Yields for the APXS Instrument David D. Thomson Advisor: University of Guelph, 2014 Professor J. L. Campbell In this thesis a new Monte Carlo simulation (MCYAC) is created and employed, which simulates measurements taken by the Alpha Particle X-ray Spectrometer (APXS) on the Mars Science Laboratory rover. Several consistency checks are conducted to test the validity of MCYAC. The checks verify that the simulation gives consistently reasonable results. The results of the Monte Carlo simulation are compared to those found experimentally, with experimental X-ray yields per second calculated using the GUAPX least squares fitting program. The largest source of discrepancy between these programs is thought to come from the geometry values of the APXS instrument used in the Monte Carlo simulation, some of which are unknown.
Pathological Contribution of Spinal Macrophage Migration Inhibitory Factor to Neuropathic Hypersensitivity through Interacting with TNF-α in the Rat

BACKGROUND Our previous data demonstrated that the proinflammatory cytokine macrophage migration inhibitory factor (MIF), a pleiotropic cytokine produced mainly by nonneuronal tissue, has been implicated in the pathogenesis of inflammatory and neuropathic hypersensitivity, whereas the precise underlying mechanisms are not totally elucidated. The aim of this study was to examine the interacting role for spinal MIF with TNF-α in neuropathic pain. METHODS After approval by the institutional Animal Care and Use Committee, the randomized Sprague-Dawley rats underwent prophylactic intrathecal administration of recombinant MIF (rMIF), TNF-α, MIF mAb, TNF-α mAb, or in combination prior to the spared nerve injury (SNI). Thermal hyperalgesia with hot plate and tactile allodynia using von Frey filaments were assessed after different interventions. Spinal cord levels of MIF and TNF-α were measured using Western Blotting and immunocytochemistry. RESULTS Exogenous rMIF potentiated SNI-induced nociceptive behavior that were not evoked by single use of rMIF without SNI, and this potential effect could be blocked by MIF antibody in part. After giving rMIF combined with TNF-α to SNI animals, the perception of thermal and tactile stimuli was maximized. Spinal MIF mAb inhibited TNF-α expression, and vise versa for TNF-α Ab on MIF expression after SNI
A Note on Parallel Distinguishability of two Quantum Operations

In this work, the authors consider a homogeneous system of linear equations of the form $A_\alpha^{\otimes N} \mathbf{x} = 0$ arising from the distinguishability of two quantum operations by $N$ uses in parallel, where the coefficient matrix $A_\alpha$ depends on a real parameter $\alpha$. It was conjectured by Duan et al. that the system has a non-trivial nonnegative solution if and only if $\alpha$ lies in a certain interval $R_N$ depending on $N$. The authors affirm the necessity part of the conjecture and establish the sufficiency of the conjecture for $N\leq 10$ by presenting explicit non-trivial nonnegative solutions for the linear system.
Electricity Price Forecasting with Neural Networks on EPEX Order Books

ABSTRACT This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected total demand. Appropriate feature extraction for the order book data is developed proceeding from existing literature. Using cross-validation to optimize hyperparameters, neural networks and random forests are fit to the data. Their in-sample and out-of-sample performance is compared to statistical reference models. The machine learning models outperform traditional approaches.