Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Model of Dark Energy EoS : Constrains on Free Parameters and Related Cosmological Parameters

Since the Ia supernovae observations of late 1990's, it has been predicted that our universe is experiencing a late time cosmic acceleration. To build a theoretical support to this observation, the existence of hypothetical fluid inside the universe is assumed which exerts negative pressure. Several candidates of such an exotic fluid have been prescribed so far. A popular method in this alley is to parametrize the equation of state parameter w=p/rho as a function of redshift. Again some common families of such redshift parametrizations are constructed of which different members have given different properties of universe. Mainly, these were model dependent studies where free parameters are to be constrained by different observations. In the present article, we have considered a new expression for redshift parametrization and have constrained its free parameters for two Hubble parameter vs redshift data sets. These two data sets are obtained depending on two basic methodologies known as differential ages method and baryonic acoustic oscillation method. We locate different confidence contours for our model under the constraints of these two data sets. Besides, we analyse different thermodynamic parameters related to the evolution of our universe. It is noticed that our model indicates towards a delayed dark matter decay model which mimics EoS=-1 phenomena at the present epoch. We study the deceleration parameters behaviors. We compare the outcomes for both the data sets.
Moulding Humans: Non-Parametric 3D Human Shape Estimation From Single Images

In this paper, we tackle the problem of 3D human shape estimation from single RGB images. While the recent progress in convolutional neural networks has allowed impressive results for 3D human pose estimation, estimating the full 3D shape of a person is still an open issue. Model-based approaches can output precise meshes of naked under-cloth human bodies but fail to estimate details and un-modelled elements such as hair or clothing. On the other hand, non-parametric volumetric approaches can potentially estimate complete shapes but, in practice, they are limited by the resolution of the output grid and cannot produce detailed estimates. In this work, we propose a non-parametric approach that employs a double depth map to represent the 3D shape of a person: a visible depth map and a ``hidden'' depth map are estimated and combined, to reconstruct the human 3D shape as done with a ``mould''. This representation through 2D depth maps allows a higher resolution output with a much lower dimension than voxel-based volumetric representations. Additionally, our fully derivable depth-based model allows us to efficiently incorporate a discriminator in an adversarial fashion to improve the accuracy and ``humanness'' of the 3D output. We train and quantitatively validate our approach on SURREAL and on 3D-HUMANS, a new photorealistic dataset made of semi-synthetic in-house videos annotated with 3D ground truth surfaces.
Scintillation Arc Brightness and Electron Density for an Analytical Noodle Model

We show that narrow filaments or sheets of over- or under-dense plasma, or “noodles,” with fluctuations of scattering phase of less than a radian, can form the scintillation arcs seen for many pulsars. The required local fluctuations of electron density are indefinitely small. We assume a cosine profile for the electron column and find the scattered field by analytic Kirchhoff integration. For a large electron column, corresponding to large amplitude of phase variation, the stationary-phase approximation is accurate; we call this regime “ray optics”. For smaller-amplitude phase variation, the stationary-phase approximation is inaccurate or inapplicable; we call this regime “wave optics”. We show that scattering is most efficient when the width of the strip equals that of one pair of Fresnel zones, and in the wave-optics regime. We show that the resolution of present observations is about 100 Fresnel zones on the scattering screen. Incoherent superposition of strips within a resolution element tends to increase the scattered field. We find that observations match a single noodle per resolution element with phase of up to 12 radians; or many noodles per resolution element with arbitrarily small phase variation each, for net phase of less than a radian. Observations suggest a minimum radius for noodles of about 650 km, comparable to the ion inertial scale or the ion cyclotron radius in the scattering plasma.
Modeling and Analysis of Integrated Proactive Defense Mechanisms for Internet-of-Things

As a solution to protect and defend a system against inside attacks, many intrusion detection systems (IDSs) have been developed to identify and react to them for protecting a system. However, the core idea of an IDS is a reactive mechanism in nature even though it detects intrusions which have already been in the system. Hence, the reactive mechanisms would be way behind and not effective for the actions taken by agile and smart attackers. Due to the inherent limitation of an IDS with the reactive nature, intrusion prevention systems (IPSs) have been developed to thwart potential attackers and/or mitigate the impact of the intrusions before they penetrate into the system. In this chapter, we introduce an integrated defense mechanism to achieve intrusion prevention in a software-defined Internet-of-Things (IoT) network by leveraging the technologies of cyberdeception (i.e., a decoy system) and moving target defense, namely MTD (i.e., network topology shuffling). In addition, we validate their effectiveness and efficiency based on the devised graphical security model (GSM)-based evaluation framework. To develop an adaptive, proactive intrusion prevention mechanism, we employed fitness functions based on the genetic algorithm in order to identify an optimal network topology where a network topology can be shuffled based on the detected level of the system vulnerability. Our simulation results show that GA-based shuffling schemes outperform random shuffling schemes in terms of the number of attack paths toward decoy targets. In addition, we observe that there exists a tradeoff between the system lifetime (i.e., mean time to security failure) and the defense cost introduced by the proposed MTD technique for fixed and adaptive shuffling schemes. That is, a fixed GA-based shuffling can achieve higher MTTSF with more cost while an adaptive GA-based shuffling obtains less MTTSF with less cost.
Gradient Pursuit-Based Channel Estimation for MmWave Massive MIMO Systems with One-Bit ADCs

In this paper, channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs) is considered. In the mmWave band, the number of propagation paths is small, which results in sparse virtual channels. To estimate sparse virtual channels based on the maximum a posteriori (MAP) criterion, sparsity-constrained optimization comes into play. In general, optimizing objective functions with sparsity constraints is NP-hard because of their combinatorial complexity. Furthermore, the coarse quantization of one-bit ADCs makes channel estimation a challenging task. In the field of compressed sensing (CS), the gradient support pursuit (GraSP) and gradient hard thresholding pursuit (GraHTP) algorithms were proposed to approximately solve sparsity-constrained optimization problems iteratively by pursuing the gradient of the objective function via hard thresholding. The accuracy guarantee of these algorithms, however, breaks down when the objective function is ill-conditioned, which frequently occurs in the mmWave band. To prevent the breakdown of gradient pursuit-based algorithms, the band maximum selecting (BMS) technique, which is a hard thresholder selecting only the "band maxima," is applied to GraSP and GraHTP to propose the BMSGraSP and BMSGraHTP algorithms in this paper.
Directional Measurements in Urban Street Canyons From Macro Rooftop Sites at 28 GHz for 90% Outdoor Coverage

Path gain and effective directional gain in azimuth in urban canyons from actual rooftop base station sites are characterized based on a massive data set of 3000 links on 12 streets in two cities, with over 21 million individual continuous-wave power measurements at 28 GHz using vertically polarized antennas. Large street-to-street path gain variation is found, with median street path gain varying over 30 dB at similar distances. Coverage in the street directly illuminated by a roof edge antenna is found to suffer an average excess loss of 11 dB relative to free space at 200 m, with an empirical slope-intercept fit model representing the data with 7.1 dB standard deviation. Offsetting the base antenna 5 m away from the roof edge, as is common in macrocellular deployments, introduces an additional average loss of 15 dB at 100 m, but this additional loss reduces with distance. Around the corner loss is well modeled by a diffraction formula with an empirically obtained diffraction coefficient. Effective azimuthal gain degradation due to scatter is limited to 2 dB for 90% of data, supporting effective use of high-gain antennas in urban street canyons.
The “Terrascope”: On the Possibility of Using the Earth as an Atmospheric Lens

Distant starlight passing through Earth’s atmosphere is refracted by an angle of just over one degree near the surface. This focuses light onto a focal line starting at an inner (and chromatic) boundary out to infinity, offering an opportunity for pronounced lensing. It is shown here that the focal line commences at ∼85% of the Earth–Moon separation, thus placing an orbiting detector between here and one Hill radius could exploit this refractive lens. Analytic estimates are derived for a source directly behind Earth (i.e., on-axis) showing that starlight is lensed into a thin circular ring of thickness, WHΔ/R, yielding an amplification of 8HΔ/W, where HΔ is Earth’s refractive scale height, R is its geopotential radius, and W is the detector diameter. These estimates are verified through numerical ray-tracing experiments from optical to 30 μm light with standard atmospheric models. The numerical experiments are extended to include extinction from both a clear atmosphere and one with clouds. It is found that a detector at one Hill radius is least affected by extinction, as lensed rays travel no deeper than 13.7 km, within the statosphere and above most clouds. Including extinction, a 1-m Hill radius “terrascope” is calculated to produce an amplification of ∼45,000 for a lensing timescale of ∼20 hr. In practice, the amplification is likely halved to avoid daylight scattering i.e., 22,500 (Δmag = 10.9) for W = 1 m, or equivalent to a 150 m optical/infrared telescope.
Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette

3D shape reconstruction from a single image is a highly ill-posed problem. Modern deep learning based systems try to solve this problem by learning an end-to-end mapping from image to shape via a deep network. In this paper, we aim to solve this problem via an online optimization framework inspired by traditional methods. Our framework employs a deep autoencoder to learn a set of latent codes of 3D object shapes, which are fitted by a probabilistic shape prior using Gaussian Mixture Model (GMM). At inference, the shape and pose are jointly optimized guided by both image cues and deep shape prior without relying on an initialization from any trained deep nets. Surprisingly, our method achieves comparable performance to state-of-the-art methods even without training an end-to-end network, which shows a promising step in this direction.
Two-Stream Video Classification with Cross-Modality Attention

Fusing multi-modality information is known to be able to effectively bring significant improvement in video classification. However, the most popular method up to now is still simply fusing each stream's prediction scores at the last stage. A valid question is whether there exists a more effective method to fuse information cross modality. With the development of attention mechanism in natural language processing, there emerge many successful applications of attention in the field of computer vision. In this paper, we propose a cross-modality attention operation, which can obtain information from other modality in a more effective way than two-stream. Correspondingly we implement a compatible block named CMA block, which is a wrapper of our proposed attention operation. CMA can be plugged into many existing architectures. In the experiments, we comprehensively compare our method with two-stream and non-local models widely used in video classification. All experiments clearly demonstrate strong performance superiority by our proposed method. We also analyze the advantages of the CMA block by visualizing the attention map, which intuitively shows how the block helps the final prediction.
Accelerating CNN Training by Sparsifying Activation Gradients

Gradients to activations get involved in most of the calculations during back propagation procedure of Convolution Neural Networks (CNNs) training. However, an important known observation is that the majority of these gradients are close to zero, imposing little impact on weights update. These gradients can be then pruned to achieve high gradient sparsity during CNNs training and reduce the computational cost. In particular, we randomly change a gradient to zero or a threshold value if the gradient is below the threshold which is determined by the statistical distribution of activation gradients. We also theoretically proved that the training convergence of the CNN model can be guaranteed when the above activation gradient sparsification method is applied. We evaluated our method on AlexNet, MobileNet, ResNet-{18, 34, 50, 101, 152} with CIFAR-{10, 100} and ImageNet datasets. Experimental results show that our method can substantially reduce the computational cost with negligible accuracy loss or even accuracy improvement. Finally, we analyzed the benefits that the sparsity of activation gradients introduced in detail.
Central Similarity Hashing via Hadamard matrix

Hashing has been widely used for efficient large-scale multimedia data retrieval. Most existing methods learn hashing functions from data pairwise similarity to generate binary hash codes. However, in practice we find only learning from the local relationships of pairwise similarity cannot capture the global distribution of large-scale data, which would degrade the discriminability of the generated hash codes and harm the retrieval performance. To overcome this limitation, we propose a new global similarity metric, termed as \emph{central similarity}, to learn better hashing functions. The target of central similarity learning is to encourage hash codes for similar data pairs to be close to a common center and those for dissimilar pairs to converge to different centers in the Hamming space, which substantially improves retrieval accuracy. In order to principally formulate the central similarity learning, we define a new concept, \emph{hash center}, to be a set of points scattered in the Hamming space with a sufficient distance between each other, and propose to use Hadamard matrix to construct high-quality hash centers efficiently. Based on these definitions and designs, we devise a new hash center network (HCN) that learns hashing functions by optimizing the central similarity w.r. t. \ these hash centers. The central similarity learning and HCN are generic and can be applied for both image and video hashing. Extensive experiments for both image and video retrieval demonstrate HCN can generate cohesive hash codes for similar data pairs and dispersed hash codes for dissimilar pairs, and achieve noticeable boost in retrieval performance, i.e. 4\%-13\% in MAP over latest state-of-the-arts. The codes are in: \url{this https URL}
Melanoma Skin Cancer Segmentation with Image Region Growing Based on Fuzzy Clustering Mean

— Melanoma is the most common dangerous type of skin cancer. On the other hand, if found in an early stage, there is a high likelihood of cure. For that reason, various types of imaging techniques have been investigated. Dermoscopy is one non-invasive imaging technique for diagnosis. The accuracy of diagnosis using dermoscopy is very important and depends on the experience of dermatologists. Visual examination is a waste of time, so there is currently wide attention paid to the development of computer-aided diagnostic systems to aid the clinical evaluation of dermatologists. Image Segmentation is very important in digital-image processing and self-discovery, with an important role to play in solving many difficult problems, particularly those related to chronic diseases, such as skin cancer. Analysis of automatic dermoscopy images usually has three stages: a) feature selection and extraction, b) image segmentation, and c) feature classification.
Cytotoxic effect of methanolic extracts of Fritillaria imperialis bulbs and Eryngium caucasicum leaves on hepatoma and colon cancer cells

Objective: To evaluate antitumor activities of Fritillaria imperialis and Eryngium caucasicum methanolic extracts on human hepatoma (HepG2) and colon cancer (HCT116) cell lines in comparison to human foreskin fibroblasts as the normal cells. Methods: Methanolic extracts of Fritillaria imperialis and Eryngium caucasicum were prepared by the maceration method. The effect of the extracts at various concentrations (100, 200, 400, 600, and 800 μg/mL) on cell survival was evaluated using the MTT method. Besides, fluorescence staining was used to evaluate death patterns of the cells. Results: MTT assay showed that Fritillaria imperialis significantly decreased the viability of all cell lines after 24 and 48 hours of treatments. However, Eryngium caucasicum extract did not show any significant cytotoxicity effect on the cell lines. Fluorescence staining revealed that Fritillaria imperialis induced apoptosis of HCT116 cells at 550 μg/mL. Conclusions: Fritillaria imperialis extract has antiproliferative and cytotoxic effects on HCT116 and HepG2 cancer cells and therefore, may serve as an anticancer agent.
Nuclear ABL Programs Extracellular Vesicles for Transmission of Ionizing Radiation-Induced Bystander Effects

Author(s): Chan, Josolyn | Advisor(s): Wang, Jean Y.J.; Kiger, Amy | Abstract: Radiation therapy is used to treat many diseases such as cancer. However, off-targeted side-effects called bystander effects are observed in patients post radiotherapy. Bystander effects are when non-irradiated cells exhibit similar effects as directly irradiated cells. Understanding how these bystander effects are mediated can help improve cancer treatment. Previous work from our lab has shown that extracellular vesicles derived from conditioned media of irradiated mouse embryonic fibroblasts mediate radiation-induced bystander effects by inhibiting clonogenic survival, increase γH2AX, and increase ROS. However, when the protein Abl is mutated so that it no longer can enter the nucleus, the EVs from the conditioned media of these irradiated cells do not cause bystander effects. When nuclear Abl is reconstituted into these mutant cells, the EVs from the conditioned media of these irradiated cells cause bystander effects such as increase γH2AX and increase ROS. This suggests that nuclear Abl is required for extracellular vesicle mediated radiation-induced bystander effects. Abl inhibitors are FDA approved and might be viable treatments to reduce bystander effects in patients undergoing radiation therapy. Additionally, in a paper previously published by our lab, nuclear Abl is required for miR-34c processing. Since EVs can contain miRNAs as cargo, miR-34c could be involved in the nuclear Abl dependent extracellular vesicle mediated radiation-induced bystander effects. This offers another possible treatment target to counter radiation-induced bystander effects.
Prediction of product success: explaining song popularity by audio features from Spotify data

This research investigates the relationship between song data - audio features from the Spotify database (e.g. key and tempo) - and song popularity measured by the number of streams a song has on Spotify. Previous research on the topic of new product success prediction have identified multiple approaches to asking this question. Especially for products in cultural markets like music, prediction modelling is very complex. A relatively novel approach, the attribute-approach was used to explore whether song attributes have an explanatory power on stream count. Research in this specific field called Hit Song Science (HSS) has not before measured similar audio features with song popularity in stream count, which is very important for record companies and which makes this research unique. Furthermore, beneficial implications from HSS can be far-reaching to consumers, record companies and for Spotify in new value creation. From the Spotify database API, a 1000 songs were analyzed from 10 different genres. By regression, a prediction model was built. We can conclude that our results suggest that audio features from Spotify have little to moderate explanatory power for a higher stream count, with this research design. Some significant relationships however were found, which lays a promising foundation for the research in prediction with these variables. This research contributes to further understanding in the field of HSS and the new product success prediction. Creating effective prediction models is an interesting next step to this research and so would be to expand on the variables used. Practical implications include that Spotify can further develop its database and calculations of the variables for in the future their databases will play an important role in new value creation.
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM

1 Instituto de Ingeniería Química, Universidad Nacional de San Juan, Consejo Nacional de Investigaciones Científicas y Técnicas, San Juan J5400ARL, Argentina. E-mail: mcfernandez@unsj.edu.ar, ORCID: 0000-0003-2312-0163; npantano@unsj.edu.ar, ORCID: 0000-0003-2549-6535; rortiz@unsj.edu.ar, ORCID: 0000-0002-6660-9674; gscaglia@unsj.edu.ar, ORCID: 0000-0002-0188-0017 2 Instituto de Automática, Universidad Nacional de San Juan, Consejo Nacional de Investigaciones Científicas y Técnicas, San Juan J5400ARL, Argentina. E-mail: frosoma@inaut.unsj.edu.ar, ORCID: 0000-0002-7792-8101
On the Performance Degradation of Speaker Recognition System due to Variation in Speech Characteristics Caused by Physiological Changes

Speaker recognition is the process of identifying a person using their speech characteristics (voice biometrics). Speech characteristics of an individual can vary due to physiological changes which may be caused by health changes, physical activity as well as emotional changes. Such changes in speech characteristics are likely to affect the accuracy of speaker recognition systems. In this paper, the performance degradation of a speaker recognition system is quantified, empirically, when the characteristics of an individual's speech change due to physiological changes caused by 'physical activity'. The speaker recognition system used in this work is based on Mel-Frequency Cepstrum Coefficients (MFCC's) and Vector Quantization (VQ). When the speech sample of a user is obtained soon after high intensity physical activity, the changes in the individual’s speech characteristics affect the accuracy of speaker recognition systems. It is necessary to understand how speaker recognition systems are affected by changes in speech characteristics in order to improve their immunity to such changes. From speech recorded after physical activity, it is found that the duration of ‘voiced component’ which has prominent discriminative characteristics of speech is shortened and it has an effect on the accuracy of speaker recognition system.