Miracle-dz/UniFAR-FTUs huggingface dataset:
Programming quantum computers using design automation

Recent developments in quantum hardware indicate that systems featuring more than 50 physical qubits are within reach. At this scale, classical simulation will no longer be feasible and there is a possibility that such quantum devices may outperform even classical supercomputers at certain tasks. With the rapid growth of qubit numbers and coherence times comes the increasingly difficult challenge of quantum program compilation. This entails the translation of a high-level description of a quantum algorithm to hardware-specific low-level operations which can be carried out by the quantum device. Some parts of the calculation may still be performed manually due to the lack of efficient methods. This, in turn, may lead to a design gap, which will prevent the programming of a quantum computer. In this paper, we discuss the challenges in fully-automatic quantum compilation. We motivate directions for future research to tackle these challenges. Yet, with the algorithms and approaches that exist today, we demonstrate how to automatically perform the quantum programming flow from algorithm to a physical quantum computer for a simple algorithmic benchmark, namely the hidden shift problem. We present and use two tool flows which invoke RevKit. One which is based on ProjectQ and which targets the IBM Quantum Experience or a local simulator, and one which is based on Microsoft's quantum programming language Q#.
Toroidal vector-potential transformer

We investigated the characteristics of a triple coil comprising a very long, thin, flexible solenoid coil wound around a hollow toroid. In ordinal magnetic-toroidal coils, the current flows in the poloidal direction, generating magnetic field in toroidal direction, but in our vector-potential coil, the magnetic flux flows in the poloidal direction, generating a vector-potential in the toroidal direction. A flexible solenoid was used as a primary coil and an alternating current was applied to it. The hollow toroid is made of brass and its wall thickness is thicker than the skin depth in the test-frequency range. Therefore, the brass hollow toroid functions as an electromagnetic shield. In the space inside the brass hollow toroid, a secondary coil was placed to form a transformer. Since the flexible solenoid does not generate a magnetic field outside of it and electromagnetic shielding is provided between the primary coil and the secondary coil, there is no magnetic field at all in the place where the secondary coil was placed. When an alternating current was applied to the primary coil, a voltage proportional to the time derivative of the primary current was observed in the secondary coil. We provide a theory for the design of this transformer.
Exploiting Task Elasticity and Price Heterogeneity for Maximizing Cloud Computing Profits

This paper exploits cloud task elasticity and price heterogeneity to propose an online resource management framework that maximizes cloud profits while minimizing energy expenses. This is done by reducing the duration during which servers need to be left on and maximizing the monetary revenues when the charging cost for some of the elastic tasks depends on how fast these tasks complete, while meeting all the resource requirements. Comparative studies conducted using Google data traces show the effectiveness of our proposed framework in terms of improving resource utilization, reducing energy expenses, and increasing cloud profits.
CAN NANOFIBERS REPLACE CURRENT MICROFILTRATION MATERIALS

Filtration materials are included in recent technologies for water treatment. Huge variety of filters is recently produced. However, a significant technical improvement was achieved in electrospinning of polymeric nanofibers and methods of their parameters definition in the last decade. According to many previous studies and publications, the nanofibrous structures show almost three-times smaller pressure losses than current filtration materials together with excellent filtration. Results of both experimental and pilot-scale application of nanofibrous filtration materials for microfiltration of artificially polluted water are presented and discussed. Nanofibrous filtration materials made of polyurethane (PU) with average diameter of pore size 0.25 µm was used. Filtration efficiency of microbiological pollution removal for both model and real wastewater was evaluated. Our results were compared to values of commonly used filtration material Pragopor 6 in obtained under the same conditions.
A Bioresponsive and Multifunctional Polymer Based Nanodevice for Cancer Nanotheranostics

The main objective of this project is to develop a polymer based near infrared (NIR) particle that can serve as both, diagnostic and therapeutic agents, for fighting cancer. Specifically these particles will be designed to have high-contrast, high signal to noise ratios, long in vivo circulation lifetimes, and facilitate easy attachment of functional and target components. Cancer is a disease where the growth of abnormal cells is uncontrollable and is one of the leading causes of death worldwide. Malignant cancer cells are more effectively treated when identified early in the disease. Identification of tumors using fluorophores, small molecules that emit visible light when excited, is gaining clinical interest. Specific interest is in near-infrared emission due to the lack of absorption of this radiation in human tissue, which facilitates deep tissue imaging. During imaging with small molecule fluorophores, the fluorophores clear from the body quickly reducing imaging effectiveness. The effectiveness of imaging can be enhanced by attaching the fluorophores to a particle. Nanometer sized particles do not clear from the body rapidly and allows the material designer to attach other ”payloads” to the particle. This multifunctional ”nano-device” can be used to deliver diagnostic (fluorophores) and therapeutic (drugs) agents to the afflicted tissue. In this current project sub-100 nm poly(propargyl acrylate) (PA) particles are surfacefunctionalized with fluorophores or targeting molecules through a copper(I) catalyzed azide-alkyne Huisgen 1,3-Dipolar cycloaddition, and used for the following applications: (1) Switching fluorescence of surface modified colloids with near-infrared emitters via protein interaction for contrast-enhanced imaging: The colloidal particles surface-functionalized with fluorophores exhibit a protein triggered activation/deactivation of the emission. Dispersing the particles into an aqueous solution, such as phosphate buffered saline (PBS), results in an aggregation of the hydrophobic fluorophores and a cessation of emission. The emission can be reinstated, or
Utilization efficient game-theoretical handover scheme for macro-femtocell networks

The Macro-Femtocell system is widely used in current society because the cooperation between Macrocell base stations and Femtocell base stations can improve the communication capacity of certain areas and adapt to the large multi-user demand. To increase the system's communication quality, the handover scheme should be utilized when the user is moving within the limited coverage of Femtocell base stations. Generally, the Markov decision strategy is used for mobile users in the process of handover. However, the Markov decision process can't ensure the maximum utilization balance of Macrocell base stations and Femtocell base stations. In this case, users often choose the base station offering the highest signal strength, which will cause a Macrocell base station's load to be too large and Femtocell base stations to be idle, so the system's utilization is very low. To address this problem, we propose the utilization efficient game-theoretical handover scheme based on Starckberg competition theory for appropriately increasing a Femtocell base station's transmitting power. This can ensure the communication quality of the system and guarantees the system's load balance to induce users to take the initiative in processing the handover scheme. Finally, the simulation results show that our strategy is effective in choosing the best base station to receive the highest signal strength by predicting the user's movement. It could also realize the load balance of the antenna in the system. The overall transmission quality of the user group is optimized.
Type III interferon is a critical regulator of innate antifungal immunity

Type III interferons activate neutrophils to promote antifungal immune responses to Aspergillus fumigatus. Type III interferons prime neutrophils Type I interferons (IFNs) have a well-established role in antiviral immunity. Here, Espinosa et al. demonstrate that type III IFNs (IFN-λs) play an essential role in driving antifungal responses. By studying immune responses to Aspergillus fumigatus (Af) in mice lacking receptors for type I or type III IFNs, the authors found monocyte-derived type I IFNs to be key drivers of IFN-λ production. Although they could not pin down the sources of IFN-λ, they have identified neutrophils as the functional target of IFN-λs. Selective deletion of IFN-λ receptor in neutrophils caused mice to succumb to Af infection. By studying immune responses to Af, the authors have uncovered the importance of type III IFNs in antifungal immunity. Type III interferons (IFN-λs) are the most recently found members of the IFN cytokine family and engage IFNLR1 and IL10R2 receptor subunits to activate innate responses against viruses. We have identified IFN-λs as critical instructors of antifungal neutrophil responses. Using Aspergillus fumigatus (Af) as a model to study antifungal immune responses, we found that depletion of CCR2+ monocytes compromised the ability of neutrophils to control invasive fungal growth. Using an unbiased approach, we identified type I and III IFNs as critical regulators of the interplay between monocytes and neutrophils responding to Af. We found that CCR2+ monocytes are an important early source of type I IFNs that prime optimal expression of IFN-λ. Type III IFNs act directly on neutrophils to activate their antifungal response, and mice with neutrophil-specific deletion of IFNLR1 succumb to invasive aspergillosis. Dysfunctional neutrophil responses in CCR2-depleted mice were rescued by adoptive transfer of pulmonary CCR2+ monocytes or by exogenous administration of IFN-α and IFN-λ. Thus, CCR2+ monocytes promote optimal activation of antifungal neutrophils by initiating a coordinated IFN response. We have identified type III IFNs as critical regulators of neutrophil activation and type I IFNs as early stimulators of IFN-λ expression.
Chemical composition of ambient PM 2 . 5 over China and relationship to precursor emissions during 2005 – 2012

In this work, we presented the characteristics of PM2.5 chemical composition over China for the period of 2005–2012 by synthesis of in situ measurement data collected from literatures and satellite-based estimates using aerosol optical depth (AOD) data and the GEOS-Chem chemical transport model. We revealed the spatiotemporal variations in PM2.5 composition during 2005–2012 and investigated the driving forces behind the variations by examining the changes in precursor emissions using a bottom-up emission inventory. Both in situ observations and satellitebased estimates identified that secondary inorganic aerosols (i.e., sulfate, nitrate, and ammonium; SNA) ranked as the highest fraction of dust-free PM2.5 concentrations, followed by organic matter (OM) and black carbon (BC). For instance, satellite-based estimates found that SNA, OM, and BC contributed to 59, 33, and 8 %, respectively, of national population-weighted mean dust-free PM2.5 concentrations during 2005–2012. National population-weighted mean PM2.5 concentration increased from 63.9 μg m−3 in 2005 to 75.2 μg m−3 in 2007 and subsequently decreased to 66.9 μg m−3 from 2007 to 2012. Variations in PM2.5 concentrations are mainly driven by the decrease in sulfate and the increase in nitrate. Population-weighted mean sulfate concentration decreased by 2.4 % yr−1 during 2005–2012 (from 14.4 to 12.9 μg m−3), while population-weighted mean nitrate concentration increased by 3.4 % yr−1 during 2005– 2012 (from 9.8 to 12.2 μg m−3), largely offsetting the decrease in sulfate concentrations. By examining the emission data from the Multi-resolution Emission Inventory for China (MEIC), we found that the changes in sulfate and nitrate concentrations were in line with the decrease in SO2 emissions and the increase in NOx emissions during the same period. The desulfurization regulation in power plants enforced around 2005 has been the primary contributor to the SO2 emission reduction since 2006. In contrast, growth of energy consumption and lack of control measures for NOx resulted in a persistent increase in NOx emissions until the installation of denitrification devices on power plants late in 2011, which began to take effect in 2012. The results of this work indicate that the synchronized abatement of emissions for multipollutants is necessary for reducing ambient PM2.5 concentrations over China.
Implementation of Naive Bayes classifier algorithm on social media (Twitter) to the teaching of Indonesian hate speech

Twitter is a social media that is widely used as a sharing medium on the internet. There are tweets containing sentences shared by the user, thus they can be read by the other users. A lot of information can be obtained from Twitter. Twitter users can connect with the other Twitter users in an international scale. Technology that is growing as today can be used for various things, especially regarding the information distributed in social media, specifically Twitter. One of the problems derived from social media is that Twitter tweets containing speechesin the form of both positive and negative utterances. From the problems above, a research is required to classify tweets that contain positive and negative speeches orutterances using naive bayes classifier method. The results of this study are implemented into a system that can classify tweets on Twitter. The system is built using js Node technology and Naive Bayes classifier as the calculation method of classification. Based on the tests performed, the best accuracy generated by the systems using the Naive Bayes Classifier is 93%.
Experimental investigation of pitch angle controller for DFIG based wind energy conversion system

Because of the developing of electricity request, wind energy is accepting much concentration everywhere throughout the world. This paper realizes the investigation of the performance of double fed induction generator (DFIG) with pitch angle controller. Comprehensive models of wind speed, wind turbine, DFIG accompanied by its controller strategy are implemented in MATLAB/SIMULINK package. Verification analysis is achieved experimentally to clarify the harmony between the results. The pitch angle controller is a required strategy for regulating the angle of the wind turbine blade whilst the wind power surpasses its nominal rate. In this manner, pitch position control is initiated to constrain the capacity of the power resulted to be equivalent to the nominal power, and hence ensure the generator protection when the wind speed reach to gusts level. The pitch point controller is just initiated at excessive wind speeds. Further, the response of the pitch-controller system to wind velocity variations is declared. Both results obtained from simulation and experiments show that the reduction behaviour of the power coefficient Cp with the increase in pitch angle consequently the active power and rotor speed are decreased.
One tool to spatialize all: sense of place, social capital and civic engagement

The spatialization of social concepts in to the city context is becoming a need. However, currently, there are few tools to directly spatialize environmental psychology concepts, such as sense of place, or social and participatory concepts, such as social capital and civic engagement. Furthermore, most existing tools are not taking full advantage of Geographical Information Science (GISc) capabilities mixed with online possibilities. This article presents a tool an internet map-based application with an intuitive user interface to deal with the pervasive lack of spatializing subjective citizens’ feeling, perceptions and experiences. Our approach successfully merges GISc framework and tools with questions and indicators from literature in social concepts. As such, the tool allows to identify and spatialize sense of place, social capital (discerning between bonding and bridging) and civic engagement of citizens, and attach meaningful information to them. It is a first step towards understanding and studying the social-spatial layer which undeniably ties a city and its citizens together.
Robust Sparse Unmixing for Hyperspectral Imagery

A linear sparse unmixing method based on spectral library has been widely used to tackle the hyperspectral unmixing problem, under the assumption that the spectrum of each pixel in the hyperspectral scene can be expressed as a linear combination of pure endmembers in the spectral library. However, because of the ion (atom) substitution in the geological process, there often exists spectral variability between the measured endmembers in the real environment and corresponding ones in the spectral library, which poses a significant challenge to linear sparse unmixing. Physically, the substitution leads to the variation of absorption peaks of endmembers, making the spectral variation of sparse property. To address the above problem, we introduce redundant spectrum to represent the spectral variation caused by ion (atom) substitution and develop a sparse redundant unmixing model by adding the redundant regularization into the classical sparse regression formulation. Based on the alternating direction method of multipliers, we develop a unified algorithm called sparse redundant unmixing to obtain the solution. Both simulation experiment and real data experiment demonstrate that the proposed method can effectively use the redundant spectrum to address the spectral variation problem caused by the ion (atom) substitution.
Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review

Background. There is growing interest to establish recovery biomarkers, especially neurological biomarkers, in order to develop new therapies and prediction models for the promotion of stroke rehabilitation and recovery. However, there is no consensus among the neurorehabilitation community about which biomarker(s) have the highest predictive value for motor recovery. Objective. To review the evidence and determine which neurological biomarker(s) meet the high evidence quality criteria for use in predicting motor recovery. Methods. We searched databases for prognostic neuroimaging/neurophysiological studies. Methodological quality of each study was assessed using a previously employed comprehensive 15-item rating system. Furthermore, we used the GRADE approach and ranked the overall evidence quality for each category of neurologic biomarker. Results. Seventy-one articles met our inclusion criteria; 5 categories of neurologic biomarkers were identified: diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), conventional structural MRI (sMRI), and a combination of these biomarkers. Most studies were conducted with individuals after ischemic stroke in the acute and/or subacute stage (~70%). Less than one-third of the studies (21/71) were assessed with satisfactory methodological quality (80% or more of total quality score). Conventional structural MRI and the combination biomarker categories ranked “high” in overall evidence quality. Conclusions. There were 3 prevalent methodological limitations: (a) lack of cross-validation, (b) lack of minimal clinically important difference (MCID) for motor outcomes, and (c) small sample size. More high-quality studies are needed to establish which neurological biomarkers are the best predictors of motor recovery after stroke. Finally, the quarter-century old methodological quality tool used here should be updated by inclusion of more contemporary methods and statistical approaches.
Assault-Related Shame Mediates the Association Between Negative Social Reactions to Disclosure of Sexual Assault and Psychological Distress

Objective: Several studies have identified associations between social reactions to disclosure of sexual assault and psychological distress; however, no studies have evaluated shame as a mediator of this association. This study evaluated assault-related shame as a mediator of the associations between negative social reactions to disclosure of sexual assault and symptoms of posttraumatic stress disorder (PTSD), depression, and global distress and hypothesized that there would be an indirect effect of social reactions to disclosure upon symptoms of psychopathology via assault-related shame. Method: Participants were 207 female psychology undergraduates who reported past history of completed or attempted sexual assault and had disclosed the assault to at least 1 other person. Participants completed self-report measures of social reactions to sexual assault disclosure, assault-related shame, and symptoms of psychopathology. Results: Participants reported significant histories of attempted or completed sexual assault and indicated clinically significant symptoms of depression and subthreshold symptoms of PTSD and global distress, on average. Evaluation of structural models confirmed the hypothesized indirect effect of negative social reactions to sexual assault disclosure upon symptoms of PTSD (z = 5.85, p < .001), depression (z = 4.56, p < .001), and global distress (z = 4.82, p < .001) via assault-related shame. Conclusions: These findings offer new insight concerning the intervening role of assault-related shame and highlight the importance of shame as a target for therapeutic intervention. This study suggests the need for future research concerning the role of shame in the etiology of PTSD and process of disclosure among survivors of attempted or completed sexual assault.
Polymerized Crystalline Colloidal Arrays Photonic Cosmetic Contact Lenses

We demonstrate the feasibility of making contact lenses from polymerized crystalline colloidal array materials to produce lenses with beautiful sparkling colors. We utilize poly[HEMA-(ethyleneglycol)200dimethacrylate] hydrogel with a crystalline colloidal array (CCA) embedded in it. The CCA material is composed of mesoscopic spheres of polystyrene latex particles dispersed in water. These particles self assemble in an aqueous medium to form a threedimensional array that efficiently Bragg diffracts visible light producing colors from blue to red as determined by its interparticle spacing. The monodisperse spherical particles have diameters between 106 to 170 nm. The CCA structure is well preserved within the poly(HEMA) hydrogel giving the contact lens models beautiful sparkling violet, blue, blue-greenish, green and yellow colors, all without the use of dyes.