Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Image Generation Using Evolution and Deep Learning

There has recently been renewed interest in the paradigm of artist-critic coevolution, or adversarial training, in which an artist tries to generate images which are similar in style to a set of real images, and a critic tries to discriminate between the real images and those generated by the artist. We explore a novel configuration of this paradigm, where the artist is trained by hierarchical evolution using an evolutionary automatic programming language called HERCL, and the critic is a convolutional neural network. The system implicitly solves the constrained optimization problem of generating images which have low algorithmic complexity, but are sufficiently suggestive of real-world images as to fool a trained critic with an architecture loosely modeled on the human visual system. The resulting images are not necessarily photorealistic, but often consist of geometric shapes and patterns which remind us of everyday objects, landscapes or designs in a manner reminiscent of abstract art. We explore the coevolutionary dynamics between artist and critic, and discuss possible combinations of this framework with interactive evolution or other human-in-the-loop paradigms.
Comment on 'Detecting Topology Variations in Networks of Linear Dynamical Systems'

In the above paper [1] , the detectability of topology variations in dynamical networks is studied. This note points out that [1, Proposition 2 and Th. 4], which provide network-theoretic conditions for discerning topology variations, require a stronger assumption than controllability of the subsystem dynamics.
Antibiotic Use in Febrile Children Presenting to the Emergency Department: A Systematic Review

Introduction: While fever is the main complaint among pediatric emergency services and high antibiotic prescription are observed, only a few studies have been published addressing this subject. Therefore this systematic review aims to summarize antibiotic prescriptions in febrile children at the ED and assess its determinants. Methods: We extracted studies published from 2000 to 2017 on antibiotic use in febrile children at the ED from different databases. Author, year, and country of publishing, study design, inclusion criteria, primary outcome, age, and number of children included in the study was extracted. To compare the risk-of-bias all articles were assessed using the MINORS criteria. For the final quality assessment we additionally used the sample size and the primary outcome. Results: We included 26 studies reporting on antibiotic prescription and 28 intervention studies on the effect on antibiotic prescription. In all 54 studies antibiotic prescriptions in the ED varied from 15 to 90.5%, pending on study populations and diagnosis. Respiratory tract infections were mostly studied. Pediatric emergency physicians prescribed significantly less antibiotics then general emergency physicians. Most frequent reported interventions to reduce antibiotics are delayed antibiotic prescription in acute otitis media, viral testing and guidelines. Conclusion: Evidence on antibiotic prescriptions in children with fever presenting to the ED remains inconclusive. Delayed antibiotic prescription in acute otitis media and guidelines for fever and respiratory infections can effectively reduce antibiotic prescription in the ED. The large heterogeneity of type of studies and included populations limits strict conclusions, such a gap in knowledge on the determining factors that influence antibiotic prescription in febrile children presenting to the ED remains.
Role-Specific Brain Activations in Leaders and Followers During Joint Action

Much of social interaction in human life requires that individuals perform different roles during joint actions, the most basic distinction being that between a leader and a follower. A number of neuroimaging studies have examined the brain networks for leading and following, but none have examined what effect prior expertise at these roles has on brain activations during joint motor tasks. Couple dancers (e.g., dancers of Tango, Salsa, and swing) are an ideal population in which examine such effects, since leaders and followers of partnered dances have similar overall levels of motor expertise at dancing, but can differ strikingly in their role-specific skill sets. To explore role-specific expertise effects on brain activations for the first time, we recruited nine skilled leaders and nine skilled followers of couple dances for a functional magnetic resonance imaging study. We employed a two-person scanning arrangement that allowed a more naturalistic interaction between two individuals. The dancers interacted physically with an experimenter standing next to the bore of the magnet so as to permit bimanual partnered movements. Together, they alternated between leading and following the joint movements. The results demonstrated that the brain activations during the acts of leading and following were enhanced by prior expertise at being a leader or follower, and that activity in task-specific brain areas tended to be positively correlated with the level of expertise at the corresponding role. These findings provide preliminary evidence that training at one role of a joint motor task can selectively enhance role-related brain activations.
Diffusion films fabricated by phase separation of polymer blend and their application on color uniformity enhancement of LEDs

Based on the phase separation and surface roughening of polymer blend, diffusion films are developed to improve the angular color uniformity (ACU) of light-emitting diodes (LEDs). By controlling the weight ratio of polymethyl methacrylate-polystyrene, diffusion films with different scattering properties can be obtained. Results show that the haze increases with the increasing of microstructure density. The haze can be tuned from 0% to 52% by controlling the material weight ratio. When the film with the highest haze is applied to a LED lamp with a correlated color temperature (CCT) of ~6000 K, the CCT deviation is reduced from 1111k to 532k from −90° to 90°. In conclusion, the diffusion films can be applied as remote films on LED lamps to improve their ACU.
Highlights of eosinophilic chronic rhinosinusitis with nasal polyps in definition, prognosis, and advancement

Tissue eosinophils are characteristic of inflammation in most but not all patients with chronic rhinosinusitis with nasal polyps (CRSwNP) and may be useful for defining subgroups and making treatment choices. However, no consistent diagnostic criteria for CRSwNP with eosinophilic inflammation have been established.
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models

Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an informationtheoretic variational bound on the true underlying distribution. Once so-obtained, the model can be putatively used to generate new samples from this distribution, or to provide a low-dimensional latent representation of existing samples. While quite effective in numerous application domains, certain important mechanisms which govern the behavior of the VAE are obfuscated by the intractable integrals and resulting stochastic approximations involved. Moreover, as a highly non-convex model, it remains unclear exactly how minima of the underlying energy relate to original design purposes. We attempt to better quantify these issues by analyzing a series of tractable special cases of increasing complexity. In doing so, we unveil interesting connections with more traditional dimensionality reduction models, as well as an intrinsic yet underappreciated propensity for robustly dismissing sparse outliers when estimating latent manifolds. With respect to the latter, we demonstrate that the VAE can be viewed as the natural evolution of recent robust PCA models, capable of learning nonlinear manifolds of unknown dimension obscured by gross corruptions.
Development and Test of a 400-kV PFN Marx With Compactness and Rise Time Optimization

Repetitive high-voltage square pulses are of great importance for producing long-pulse electron beams and high-power microwaves. One of possible technologies for the generation of such pulses is a Marx generator using pulse forming network (PFN) stages, often combined with a pulse sharpening technique to reduce the rise time to a few nanoseconds (peaking stage). This paper presents an innovative design, named the “zigzag design,” for the optimization of the compactness and of the rise time of 400-kV–85-ns PFN-Marx. Thanks to this design, the 16 stages of this generator, which delivers an open circuit output voltage of 720 kV, fit in a 650-mm length. For a slightly overmatched load ( $Z_{\mathrm {load}} = 100 ~\Omega$ ), the output voltage reaches 400 kV with a rise time as less as 5 ns. The inductance reduction associated with the innovative zigzag design, which allows this sharp rise time with no need for a peaking stage, is described. The 85-ns plateau duration of the pulse is given by the PFN construction of each stage, which is based on six ceramic capacitors (2.1 nF–45 kV) connected within a strip line. The 16 PFN stages are housed in a 360-mm diameter gas pressurized vessel. Burst mode operation for a duration of 10 s at a pulse repetition frequency of 100 Hz is reported, for a resistive load and for the electron beam diode of a X-band relativistic backward-wave oscillator (BWO). To reach further compactness, the BWO system is integrated on side of the generator vessel and a U-shaped gas pressurized line connects both systems through a compact conical vacuum insulator.
Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach

In complex terrain, ground-based lidar wind speed measurements sometimes show noticeable differences compared to measurements made with in-situ sensors mounted on meteorological masts. These differences are mostly caused by the inhomogeneities of the flow field and the applied reconstruction methods. This study investigates three different methods to optimize the reconstruction algorithm in order to improve the agreement between lidar measurements and data from sensors on meteorological masts. The methods include a typical velocity azimuth display (VAD) method, a leave-one-out cross-validation method, and a linear model which takes into account the gradients of the wind velocity components. In addition, further aspects such as the influence of the half opening angle of the scanning cone and the scan duration are considered. The measurements were carried out with two different lidar systems, that measured simultaneously. The reference was a 100 m high meteorological mast. The measurements took place in complex terrain characterized by a 150 m high escarpment. The results from the individual methods are quantitatively compared with the measurements of the cup anemometer mounted on the meteorological mast by means of the three parameters of a linear regression (slope, offset, R 2 ) and the width of the 5th–95th quantile. The results show that expanding the half angle of the scanning cone from 20 ∘ to 55 ∘ reduces the offset by a factor of 14.9, but reducing the scan duration does not have an observable benefit. The linear method has the lowest uncertainty and the best agreement with the reference data (i.e., lowest offset and scatter) of all of the methods that were investigated.
BAFF and BAFF-Receptor in B Cell Selection and Survival

The BAFF-receptor (BAFFR) is encoded by the TNFRSF13C gene and is one of the main pro-survival receptors in B cells. Its function is impressively documented in humans by a homozygous deletion within exon 2, which leads to an almost complete block of B cell development at the stage of immature/transitional B cells. The resulting immunodeficiency is characterized by B-lymphopenia, agammaglobulinemia, and impaired humoral immune responses. However, different from mutations affecting pathway components coupled to B cell antigen receptor (BCR) signaling, BAFFR-deficient B cells can still develop into IgA-secreting plasma cells. Therefore, BAFFR deficiency in humans is characterized by very few circulating B cells, very low IgM and IgG serum concentrations but normal or high IgA levels.
CEO Exit after IPO: Which Wealth Matters?

IPOs are typically viewed as the peak of entrepreneurial success. However, CEOs in those entrepreneurial firms often leave after the IPOs to harvest their investment. The understanding of such voluntary exit remains rudimentary in small business and entrepreneurial settings. We examine the impact of both CEOs’ financial and socioemotional wealth on their exit decisions after IPOs. The findings show that CEOs with greater financial wealth are more likely to stay with the firm initially, but they become more likely to leave after certain threshold. We also find that socioemotional wealth negatively moderates the predications of financial wealth on exit.
Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape

Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape. In this study, an unmanned aerial vehicle (UAV) equipped with Red Green Blue (RGB) and multispectral cameras was used to acquire a series of field images at the flowering stage, and the flower number was manually counted as a reference. Images of the rape field were first classified using K-means method based on Commission Internationale de l’Éclairage (CIE) L*a*b* space, and the result showed that classified flower coverage area (FCA) possessed a high correlation with the flower number (r2 = 0.89). The relationships between ten commonly used vegetation indices (VIs) extracted from UAV-based RGB and multispectral images and the flower number were investigated, and the VIs of Normalized Green Red Difference Index (NGRDI), Red Green Ratio Index (RGRI) and Modified Green Red Vegetation Index (MGRVI) exhibited the highest correlation to the flower number with the absolute correlation coefficient (r) of 0.91. Random forest (RF) model was developed to predict the flower number, and a good performance was achieved with all UAV variables (r2 = 0.93 and RMSEP = 16.18), while the optimal subset regression (OSR) model was further proposed to simplify the RF model, and a better result with r2 = 0.95 and RMSEP = 14.13 was obtained with the variable combination of RGRI, normalized difference spectral index (NDSI (944, 758)) and FCA. Our findings suggest that combining VIs and image classification from UAV-based RGB and multispectral images possesses the potential of estimating flower number in oilseed rape.
Effect of electric current stressing on the shear strength and fracture behavior of micro-scale BGA structured Cu/Sn–3.0Ag–0.5Cu/Cu joints

Shear performance and fracture of BGA structured Cu/Sn–3.0Ag–0.5Cu(SAC305)/Cu joints under current stressing were characterized. Solder joints undergoing electric current stressing have lower shear strength, and fracture tends to take place near the interface between the solder and the intermetallic compound (IMC), and shear fracture strength of the joints decreases with increasing current density. In addition, under electric current stressing, fracture largely occurred mainly by mixed ductile-brittle mode. The temperature of the joints increases greatly upon applying electric current as confirmed by finite element simulation (FE simulation), which is induced by the Joule heating effect. Moreover, the phenomenon of local melting has been observed in solder joints with electric current stressing.
Learning To Simulate

Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.
Smart Street Lighting with Reduced Sensors for Sustainable and Efficient Smart Cities

Street Lights are absolutely important and without them, people would be risking their life every minute they spend on the roads. The main problem involved in street light hardware system is excessive consumption of power. This amount of power consumption can be minimized by designing smart street lighting systems in order to have smart cities. To enable the growth of smart cities and help them shape into self-sustaining, energy harvesting, green hubs, the energy must be conserved. To solve this problem there are many smart street lighting methods which discussed efficient ways to conserve power. To support the financial needs of some of the proposed methods, a struggling economy like India would not be able to afford the costs. However, to make it truly smart and affordable, the number of sensors involved in the street lighting system can be reduced strategically to account for driver’s visibility range as well as power consumed by the lights. The algorithm discussed in this paper, takes advantage of Cellular Automata [CA], to simulate the traffic similar to real-time traffic and utilized Bernoulli’s probability distribution to analyze the randomized the traffic present on the roads. This helped to visualize the situation as aptly as possible. According to the statistics street lights use up nearly 38% of the electricity produced. If this energy is saved using an energy saving scheme such as the one discussed in this paper, that energy can be utilized elsewhere – where there is an absolute and immediate need, for instance smart cities. According to the results obtained through the utilization of the method discussed in this paper, nearly 50 percent of the energy consumed by the existing smart street lighting systems can be saved. If the energy thus saved is transferred to the development of the smart cities, more than 100 of them can achieve full transition from an urban city into a smart city.
A 0.6-V 10-bit 200-kS/s SAR ADC With Higher Side-Reset-and-Set Switching Scheme and Hybrid CAP-MOS DAC

This paper presents a low-power and area efficient 10-bit SAR ADC with higher side-reset-and-set (HSRS) switching scheme and hybrid capacitive-MOS (CAP-MOS) DAC. The HSRS switching scheme consumes zero switching energy for the two most-significant bits and skips unnecessary switching without using any auxiliary circuit. It is further verified in this paper that the HSRS switching scheme shows lower nonlinearity compared with the conventional CDAC structure with the same unit capacitor size and matching condition. This additional advantage permits using DAC topologies with lower total capacitance under given linearity requirement. A hybrid CAP-MOS DAC is adopted to reduce the total capacitance of the DAC, which consumes lower static current and chip area than the conventional hybrid capacitive-resistive DAC under the same settling requirement. The prototype 10-bit SAR ADC is implemented in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology, showing an SNDR/SFDR of 56.43 dB/71 dB at 90-kHz input, under a 0.6-V power supply, while consuming <inline-formula> <tex-math notation="LaTeX">$1.01~\mu \text{W}$ </tex-math></inline-formula> at 200 kS/s for a figure of merit of 9.32 fJ/conv. -step. The ADC occupies only a small active area of 0.0675 mm<sup>2</sup>.
Are machine learning based intrusion detection system always secure? An insight into tampered learning

Machine learning is successful in many applications including securing a network from unseen attack. The application of learning algorithm for detecting anomaly in a Network has been fundamental since few years. With increasing use of machine learning techniques it has become important to study to what extent it is good to be dependent on them. Altogether a different discipline called ‘Adversarial Learning’ have come up as a separate dimension of study. The work in this paper is to test the robustness of online machine learning based IDS to carefully crafted packets by attacker called poison packets. The objective is to observe how a remote attacker can deviate the normal behavior of machine learning based classifier in the IDS by injecting the network with carefully crafted packets externally, that may seem normal by the classification algorithm and the instance made part of its future training set. This behavior eventually can lead to a poison learning by the classification algorithm in the long run, resulting in misclassification of true attack instances. This work explores one such approach with SOM and SVM as the online learning based classification algorithms.
Deep Learning Modeling for Top-N Recommendation With Interests Exploring

Recommender systems (RS) currently play a crucial role in information filtering and retrieval, and have been ubiquitously applied in many domains, although suffering from such data sparsity and cold start problems. There are plenty of studies that try to make efforts to improve the performance of RS through different aspects, such as traditional matrix factorization technique and deep learning methods in recent years, however, it’s still a challenging issue under research. In this paper, motivated by this, a two-stage deep learning-based model for top-N recommendation with interests exploring (DLMR) is proposed: 1) DLMR explores latent interests for each user, captures factors from reviews and contextual information via convolutional neural network, and performs convolutional matrix factorization to generate the candidates list; 2) In order to enhance the recommendation performance, DLMR further conducts candidates ranking through a three-layer denoising autoencoder, with taking account of heterogeneous side information. The DLMR provides a flexible scheme to leverage the available resources for recommendation, which is able to explore user’s latent interests, capture the intricate interactions between users and items, and provide accurate and personalized recommendations. Experimental analysis over real world data sets demonstrates that DLMR could provide high performance top-N recommendation in sparse settings and outperform state-of-the-art recommender approaches significantly.