Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrothermal Processing of Phase Pure and Doped Hydroxyapatite and its Characterization

Hydroxyapatite (HA) is a synthetic ceramic which is used in numerous biomedical applications. However, its use is restricted in load bearing applications. A novel batch hydrothermal method is indigenously developed to synthesize fine size, crystalline hydroxyapatite (HA) and titania doped hydroxyapatite (TiO2-HA) powders with distinct phase formation. Powders were characterized using XRD, FTIR and DSC-TGA. Sharp peaks in HA XRD pattern after sintering at 1000°C indicate significant crystallinity while sharp peaks in TiO2 XRD pattern at 27°, 36° and 5° after sintering indicate anatase to rutile transformation. This fact is also confirmed by FTIR and DSC-TGA Plots.
CdS(Se) and PbS(Se) Quantum Dots with High Room Temperature Quantum Efficiency in the Fluorine-Phosphate Glasses

In this study, the luminescent properties of the CdS(Se) quantum dots (QDs) with the mean size of 2–4 nm, the (PbS)n/(PbSe)n molecular clusters (MCs) and PbS(Se) quantum dots (QDs) with the mean size of 2–5 nm embedded in the fluorine-phosphate glass are investigated. The dependence of the photo luminescence absolute quantum yield (PL AQY) on the sizes of the CdS(Se) QDs are studied. It is found that the PL AQY of the CdSe QDs increases monotonically to its maximum height and then falls down. The PL AQY of the CdS(Se) QDs can reach 50–65%. Luminescence of (PbS)n/(PbSe)n MCs embedded in fluorine-phosphate matrix and excited by UV radiation is obtained in the visible spectral region and its absolute quantum yield was up to 10%. The PbSe QDs have broadband photoluminescence with quantum efficiency about five times more than MCs (~50%) in the spectral range of 1–1.7 µm. Glasses doped with PbS(Se) QDs provide potential application as infrared fluorophores, which are both efficient and possess short life times. The glass matrix protects the QDs from external influence and their optical properties remain unchanged for a long time. was shown that heat treatment of the FP glasses leads to formation of (PbS) n and (PbSe) n molecular clusters, which exhibit luminescent properties in visible range with quantum efficiency from 2 to 10%. Increasing the heat-treatment temperature results in the PbS and PbSe QDs (sizes of 3–5 nm) formation with high concentration (~1 mol%). The QDs have broadband photoluminescence with quantum efficiency about five times more than MCs (~50%) in the spectral range of 1–1.7 µm. FP glasses doped with PbSe and PbS QDs are infrared fluoro-phores, which are both efficient and possess short lifetimes. These materials may find unique applications for fluorescent imaging tagging in the near-IR spectral range or as fluorescent materials in the fiber communication range of 1.3–1.5 microns.
Structural Change and Productivity Growth in Europe - Past, Present and Future

This paper studies the effect of structural change on the historical path of aggregate labor productivity growth for a large sample of European countries, and it builds a quantitative multi-sector growth model to analyze the potential impact that structural change may have on future productivity growth. We document that the observed reallocation of economic activity since the 1970s towards the service sector has exerted a strongly negative effect on aggregate productivity growth in most European countries. Moreover, we perform a quantitative analysis to show that the expected path of structural change might continue to have a sizable dent on future productivity growth in Europe. By contrast, the impact in the U.S. is expected to rapidly diminish. We show that this differential result can be explained by the large expansion, in Europe, of certain service sub-sectors characterized by stagnant productivity.
Design and Development of Immediate Release and Sustained Release Bi-Layered Matrix Tablets of Oral-Hypoglycemic Drugs

The aim of the work is to develop Bi-layered matrix tablets containing a Biguanide (API I) in SR layer and a Thiazolidinedione (API II) in IR layer. The formula is developed by taking In-vitro release of both the drugs in to consideration. HPMC K 4M, HPMC K 15M and HPMC K 100M were used as rate retarding polymers and Croscarmellose sodium as superdisintegrant for SR layer and IR layer respectively, in the trials for preparing an optimum formulation of the Bi-Layer tablets. Pre-formulation studies and Compatibility studies were found to be satisfactory. Tablet blend all formulations was evaluated for flow properties, which were found to be optimum. Tablet prepared were evaluated for several parameters like Thickness, Hardness, Weight variation, Friability, Drug content and In-vitro release of the drugs. Formulation F-7 was found to be an optimum formula with the release of the API I and API II within specifications. The release rate was found to be following First-order kinetics and mechanism is by combination of diffusion and erosion. Stability studies at conditions 30°C/ 65%RH and 40°C/75%RH are showing that the dosage form is intact after storage for 3 months at both the conditions. It can be concluded that Bi-layered tablets of these drugs are advantageous in having uniform drug content of both the drugs and the sustained release of the API I reduces the dosing frequency thereby improving patient compliance.
Stock Market Response to Firms’ Unethical Conduct

In the last decades, labor rights violations and tangible environmental challenges have caused companies to come under increasing society’s pressure to achieve environmental and social goals. Using a novel dataset on worldwide industrial disasters and companies (allegedly) involved in them, I examine whether companies suffer systematic stock market losses after malpractices’ disclosure through industrial disasters. I estimate an average drop in price returns of 1.47 percentage points on the day after the disaster and of 3.21 percentage points over the week after. I then discuss the possible mechanisms behind the negative stock market response to industrial disasters through a series of exercises that exploit heterogeneity in disasters’ and companies’ characteristics. Notwithstanding other possible channels, correlations show that stock market losses are consistent with reputation losses after industrial disasters.
Theoretical study of the regioselectivity of the reaction between tetrachloromethane and triethyl phosphite using DFT B3LYP/6-31G (d)

In this study we used DFT B3LYP/6-31G(d) to determine certain thermodynamic properties, transition states, energies of the highest occupied molecular orbits (HOMO) and lowest unoccupied molecular orbits (LUMO), the difference between the two energies (eLUMO–eHOMO), global indices and chemical potentials of the reaction between tetrachloromethane and triethyl phosphite P(OEt)3. Our results show that triethyl phosphite behaves as a nucleophile, while tetrachloromethane behaves as an electrophile. The nucleophilic attack takes place preferentially at the chlorine atom of the tetrachloromethane rather than at the carbon atom. The reaction is exothermic and regioselective, and the reaction mechanism is controlled by charge transfer.
Costs and Benefits of Fair Regression

Real-world applications of machine learning tools in high-stakes domains are often regulated to be fair, in the sense that the predicted target should satisfy some quantitative notion of parity with respect to a protected attribute. However, the exact tradeoff between fairness and accuracy with a real-valued target is not entirely clear. In this paper, we characterize the inherent tradeoff between statistical parity and accuracy in the regression setting by providing a lower bound on the error of any fair regressor. Our lower bound is sharp, algorithm-independent, and admits a simple interpretation: when the moments of the target differ between groups, any fair algorithm has to make an error on at least one of the groups. We further extend this result to give a lower bound on the joint error of any (approximately) fair algorithm, using the Wasserstein distance to measure the quality of the approximation. With our novel lower bound, we also show that the price paid by a fair regressor that does not take the protected attribute as input is less than that of a fair regressor with explicit access to the protected attribute. On the upside, we establish the first connection between individual fairness, accuracy parity, and the Wasserstein distance by showing that if a regressor is individually fair, it also approximately verifies the accuracy parity, where the gap is given by the Wasserstein distance between the two groups. Inspired by our theoretical results, we develop a practical algorithm for fair regression through the lens of representation learning, and conduct experiments on a real-world dataset to corroborate our findings.
Ein flugzeuggetragenes kryogenes Infrarotspektrometer zur Fernerkundung klimarelevanter Spurengase im Tropopausenbereich

A reliable prognosis of climate change in the earth’s atmosphere as well as the distinction of the relevant natural and anthropogenic influences is a prevailing scientific question and necessitates a detailed understanding of energetic, dynamic and chemical processes in the atmosphere, particularly in the tropopause region. The upper troposphere and lower stratosphere seems to proof highly sensitive to changes in atmospheric parameters such as temperature and trace gas distributions. Dynamic transport and exchange of chemical constituents between troposphere and stratosphere may cause significant changes in the atmosphere’s chemical and radiative budget. In spite of their great importance the involved processes are far from beeing well understood. To assess the major uncertainties concerning the distribution, chemistry and radiative forcing of water vapor, aerosols, ozone, the hydroxyl radical or the formation mechanisms of the various types of cirrus clouds, processes ranging from synoptical scale to microscale have to be investigated requiring different measurement techniques. While hitherto global satellite measurements are limited either by their horizontal or vertical resolution, local in-situ instruments are limited by their little spatial coverage. Therefore since 2003 the Research Center Julich and the University of Wuppertal have cooperated in the development of a new limb sounding infrared instrument aboard the high altitude research aircraft M55-Geophysika aiming at measurements in the tropopause region, combining high vertical resolution with reasonable regional coverage. The first mission of the new CRISTA-NF (CRyogenic Infrared Spectrometers and Telescope for the Atmosphere - New Frontiers) instrument took place during the extensive European SCOUT-O3 (Stratosphere Climate Ozone links with emphasis on the UTLS) measurement campaign in Darwin, Australia, in November/December 2005. Here, an overview of the instrument’s measurement technique is given, followed by descriptions of the instrument calibration, the process of data analysis and retrieval. Using these methods a set of preliminary data from the SCOUT campaign is analyzed concerning the distribution of clouds as well as the mixing ratios of water vapor and the chlorofluorocarbon CFCl 3 . Simulations by the Chemical Lagrangian Model of the Stratosphere (CLaMS) are used for comparisons with the observed situation concerning processes of transport and mixing.
Assessment of CFD capability for prediction of the Coandă effect

The tendency of a jet to stay attached to a flat or convex surface is called the Coandă effect and has many potential technical applications. The aim of this thesis is to assess how well Computational Fluid Dynamics can capture it. A Reynolds-Averaged Navier-Stokes approach with a 2-dimensional domain was first used to simulate an offset jet on a flat plane. Whether it was for k − ω SST or k − turbulence model, a good prediction of the flow was found. Since it is known that streamline curvature can have an important impact on the numerical results, a jet blown tangentially to a cylinder was then considered. Using the same approach as for the flat plane, with k − ω SST turbulence model, some of the flow features such as the separation location or velocity profiles near the jet exit were accurately predicted. However, the jet development was overall poorly captured. A Curvature Correction was then introduced in the turbulence model and if it did slightly improve the jet development, the negative impact on other quantities makes its benefits questionable. Due to the known presence of longitudinal and spanwise vortices in the flow, a Reynolds-Averaged Navier-Stokes approach with a 3-dimensional domain was attempted but was only able to reproduce the 2-dimensional results. Although if the longitudinal vortices are artificially generated at the inlet, their development is supported by the simulation. Finally, since the shortcomings of the numerical results obtained might be due to limitations of the Reynolds-Averaged Navier-Stokes approach, a Large Eddy Simulation was attempted. Unfortunately, due to time and computational restrictions a fully developed flow could not be obtained, the methodology and preliminary results are however presented.
Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess

This paper introduces deep synoptic Monte Carlo planning (DSMCP) for large imperfect information games. The algorithm constructs a belief state with an unweighted particle filter and plans via playouts that start at samples drawn from the belief state. The algorithm accounts for uncertainty by performing inference on"synopses,"a novel stochastic abstraction of information states. DSMCP is the basis of the program Penumbra, which won the official 2020 reconnaissance blind chess competition versus 33 other programs. This paper also evaluates algorithm variants that incorporate caution, paranoia, and a novel bandit algorithm. Furthermore, it audits the synopsis features used in Penumbra with per-bit saliency statistics.
BeamSurfer: Minimalist Beam Management of Mobile mm-Wave Devices

Management of narrow directional beams is critical for mm-wave communication systems. Translational or rotational motion of the user can cause misalignment of transmit and receive beams with the base station losing track of the mobile. Reacquiring the user can take about one second in 5G New Radio systems and significantly impair performance of applications, besides being energy intensive. It is therefore important to manage beams to continually maintain high received signal strength and prevent outages. It is also important to be able to recover from sudden but transient blockage caused by a hand or face interposed in the Line-of-Sight (LoS) path. This work presents a beam management protocol called BeamSurfer that is targeted to the use case of users walking indoors near a base station. It is designed to be minimalistic, employing only in-band information, and not requiring knowledge such as location or orientation of the mobile device or any additional sensors. Evaluations under pedestrian mobility show that 96% of the time it employs beams that are within 3 dB of what an omniscient Oracle would have chosen as the pair of optimal transmit-receive LoS beams. It also recovers from transient LoS blockage by continuing to maintain control packet communication over a pre-selected reflected path that preserves time-synchronization with the base station, which allows it to rapidly recover to the LoS link as soon as it is unblocked.
A Lie algebra of Grassmannian Dirac operators and vector variables

The Lie algebra generated by m p-dimensional Grassmannian Dirac operators and m pdimensional vector variables is identified as the orthogonal Lie algebra so(2m + 1). In this paper, we study the space P of polynomials in these vector variables, corresponding to an irreducible so(2m+ 1) representation. In particular, a basis of P is constructed, using various Young tableaux techniques. Throughout the paper, we also indicate the relation to the theory of parafermions.
Detection of blue whale vocalisations using a temporal-domain convolutional neural network

We present a framework for detecting blue whale vocalisations from acoustic submarine recordings. The proposed methodology comprises three stages: i) a preprocessing step where the audio recordings are conditioned through normalisation, filtering, and denoising; ii) a label-propagation mechanism to ensure the consistency of the annotations of the whale vocalisations, and iii) a convolutional neural network that receives audio samples. Based on 34 real-world submarine recordings (28 for training and 6 for testing) we obtained promising performance indicators including an Accuracy of 85.4% and a Recall of 93.5%. Furthermore, even for the cases where our detector did not match the ground-truth labels, a visual inspection validates the ability of our approach to detect possible parts of whale calls unlabelled as such due to not being complete calls.
Sicilian Translator: A Recipe for Low-Resource NMT

With 17,000 pairs of Sicilian-English translated sentences, Arba Sicula developed the first neural machine translator for the Sicilian language. Using small subword vocabularies, we trained small Transformer models with high dropout parameters and achieved BLEU scores in the upper 20s. Then we supplemented our dataset with backtranslation and multilingual translation and pushed our scores into the mid 30s. We also attribute our success to incorporating theoretical information in our dataset. Prior to training, we biased the subword vocabulary towards the desinences one finds in a textbook. And we included textbook exercises in our dataset.
Combining physics and deep learning to learn continuous-time dynamics models

Deep learning has been widely used within learning algorithms for robotics. One disadvantage of deep networks is that these networks are black-box representations. Therefore, the learned approximations ignore the existing knowledge of physics or robotics. Especially for learning dynamics models, these black-box models are not desirable as the underlying principles are well understood and the standard deep networks can learn dynamics that violate these principles. To learn dynamics models with deep networks that guarantee physically plausible dynamics, we introduce physics-inspired deep networks that combine first principles from physics with deep learning. We incorporate Lagrangian mechanics within the model learning such that all approximated models adhere to the laws of physics and conserve energy. Deep Lagrangian Networks (DeLaN) parametrize the system energy using two networks. The parameters are obtained by minimizing the squared residual of the Euler–Lagrange differential equation. Therefore, the resulting model does not require specific knowledge of the individual system, is interpretable, and can be used as a forward, inverse, and energy model. Previously these properties were only obtained when using system identification techniques that require knowledge of the kinematic structure. We apply DeLaN to learning dynamics models and apply these models to control simulated and physical rigid body systems. The results show that the proposed approach obtains dynamics models that can be applied to physical systems for real-time control. Compared to standard deep networks, the physics-inspired models learn better models and capture the underlying structure of the dynamics.
Benchmarking Memory-Centric Computing Systems: Analysis of Real Processing-In-Memory Hardware

Many modern workloads such as neural network inference and graph processing are fundamentally memory-bound. For such workloads, data movement between memory and CPU cores imposes a significant overhead in terms of both latency and energy. A major reason is that this communication happens through a narrow bus with high latency and limited bandwidth, and the low data reuse in memory-bound workloads is insufficient to amortize the cost of memory access. Fundamentally addressing this data movement bottleneck requires a paradigm where the memory system assumes an active role in computing by integrating processing capabilities. This paradigm is known as processing-in-memory (PIM). Recent research explores different forms of PIM architectures, motivated by the emergence of new technologies that integrate memory with a logic layer, where processing elements can be easily placed. Past works evaluate these architectures in simulation or, at best, with simplified hardware prototypes. In contrast, the UPMEM company has designed and manufactured the first publicly-available real-world PIM architecture. The UPMEM PIM architecture combines traditional DRAM memory arrays with general-purpose in-order cores, called DRAM Processing Units (DPUs), integrated in the same chip. This paper presents key takeaways from the first comprehensive analysis [1] of the first publicly-available real-world PIM architecture. First, we introduce our experimental characterization of the UPMEM PIM architecture using microbenchmarks, and present PrIM (Processing-In-Memory benchmarks), a benchmark suite of 16 workloads from different application domains (e.g., dense/sparse linear algebra, databases, data analytics, graph processing, neural networks, bioinformatics, image processing), which we identify as memory-bound. Second, we provide four key takeaways about the UPMEM PIM architecture, which stem from our study of the performance and scaling characteristics of PrIM benchmarks on the UPMEM PIM architecture, and their performance and energy consumption comparison to their state-of-the-art CPU and GPU counterparts. More insights about suitability of different workloads to the PIM system, programming recommendations for software designers, and suggestions and hints for hardware and architecture designers of future PIM systems are available in [1].
Phase diagram and Higgs phases of three-dimensional lattice SU(N_{c}) gauge theories with multiparameter scalar potentials.

We consider three-dimensional lattice SU(N_{c}) gauge theories with degenerate multicomponent (N_{f}>1) complex scalar fields that transform under the fundamental representation of the gauge SU(N_{c}) group and of the global U(N_{f}) invariance group, interacting with the most general quartic potential compatible with the global (flavor) and gauge (color) symmetries. We investigate the phase diagrams, identifying the low-temperature Higgs phases and their global and gauge symmetries, and the critical behaviors along the different transition lines. In particular, we address the role of the quartic scalar potential, which determines the Higgs phases and the corresponding symmetry-breaking patterns. Our study is based on the analysis of the minimum-energy configurations and on numerical Monte Carlo simulations. Moreover, we investigate whether some of the transitions observed in the lattice model can be related to the behavior of the renormalization-group flow of the continuum field theory with the same symmetries and field content around its stable charged fixed points. For N_{c}=2, numerical results are consistent with the existence of charged critical behaviors for N_{f}>N_{f}^{★}, with 20