Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Erythropoietin Receptor Activator (C.E.R.A.) Treatment of Renal Anemia in Patients with Chronic Kidney Disease: A Two-Year Observational Study

Objective: To evaluate once-monthly continuous erythropoiesis receptor activator (C.E.R.A.) in patients with chronic kidney disease (CKD) for two years under standard conditions. Methods: In a non-interventional study, C.E. R.A. was administered according to local practice in patients with dialysis dependent or non-dialysis dependent CKD. Results: 206 patients were evaluable to month 24. In the dialysis dependent and non-dialysis dependent patients who had received ESA therapy prior to study entry, Hb remained stable from baseline to the end of the study: mean (SD) change was -0.3 (1.5) g/dL (n=148) and 0.3 (1.6) g/dL (n=33), respectively. The mean (SD) dose of C.E. R.A. was 114 (78) μg in dialysis dependent patients and 97 (71) μg in non-dialysis dependent patients at baseline, remaining virtually unchanged during the study (109 (76) μg and 99 (68) μg). During the two-year study, dialysis dependent and non-dialysis dependent patients received a mean of 6.1 and 4.3 dose changes, respectively. Discontinuation due to adverse events was rare (2.9%). Conclusions: Once-monthly C.E. R.A. is effective and convenient in dialysis dependent and non-dialysis dependent patients with renal anemia under routine conditions for at least two years, and requires few dose changes. C.E. R.A. was well-tolerated with a good safety profile over the two-year study period.
IMPACT OF NATURAL AND ARTIFICIAL CHEMICAL INPUTS ON THE MARINE ECOSYSTEM OF BATROUN REGION (NORTH LEBANON)

This paper is an attempt at studying the interaction of a seasonal river and the discharges of a major chemical plant in the Batroun region in North Lebanon. Surface water analysis was done monthly for a period of 14 months from May 2001 till June 2002, along 13 stations (10 along the shore and 3 on a horizontal transect from the factory). The ranges of the hydrological and hydro-biological parameters were as follow: 16.3 to 32°C for temperature, 26.34 to 39.53‰ for salinity, 6.33 to 8.30 for pH, 81.85 to 110.7% for dissolved oxygen saturation, 0.02 to 25. 36µgat. L -1 for nitrate, 0.01 to 54. 65µgat. L -1 for phosphate and 0.01 to 2.52mg. m -3 for chlorophyll-a respectively. Results show that nitrate values follow a decreasing gradient from the mouth of the river in winter and spring. Phosphate values decrease as one moves away from the discharge points of the plant. The buffering capacity of seawater did not prevent the increase in seawater acidity to abnormally high levels and the acid discharge from the factory to its immediate proximity has created a local ecologically deliterious factor that was marked out at primary production level represented by chlorophyll-a concentrations. The high levels of nutrients essential for phytoplankton development did not result in high chlorophyll-a concentrations. The reduction in chlorophyll-a concentrations was the consequence of the dystrophic condition caused by the unbalance in nutrients distribution and the destructive effect of acid discharges at primary production level. The hydrodynamic and geomorphologic position of any marine zone, the flow rate and concentrations of the different continental discharges will interfere in the level of its contamination.
Quantum $K$-theory of projective spaces and confluence of $q$-difference equations

In this paper, we study $K$-theoretic Gromov--Witten invariants of projective spaces. Using the theory of the confluence of $q$-difference equations, introduced by J. Sauloy, we propose a new perspective to relate $K$-theoretical Gromov--Witten invariants to their cohomological analogues. In the case of projective spaces, we show that adapting the confluence process to Givental's small $K$-theoretical $J$-function recovers the small cohomological $J$-function.
Benchmarking Simulated Robotic Manipulation Through a Real World Dataset

We present a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset. Users are given various simulated manipulation tasks with assigned protocols having the objective of replicating the real world results of a recorded dataset. The benchmark comprises of a range of metrics used to characterise the successes of submitted environments whilst providing insight into their deficiencies. We apply our benchmark to two simulation environments, i.e., PyBullet and V-Rep, and publish the results. All materials required to benchmark an environment, including protocols and the dataset, can be found at the benchmarks’ website<xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label><p>[Online]. Available: <uri>https://research.csiro. au/robotics/manipulation-benchmark/</uri>. </p></fn>.
Structural and photometric properties of barred galaxies from the Auriga cosmological simulations

In this work we analyse the structural and photometric properties of 21 barred simulated galaxies from the Auriga Project. These consist of Milky Way-mass magnetohydrodynamical simulations in a Λ cold dark matter (ΛCDM) cosmological context. In order to compare with observations, we generate synthetic SDSS-like broad-band images from the numerical data at z = 0 with different inclinations (from face-on to edge-on). Ellipse fits are used to determine the bar lengths, and 2D bulge/disc/bar decompositions with galfit are also performed, modelling the bar component with the modified Ferrer profile. We find a wide range of bar sizes and luminosities in the sample, and their structural parameters are in good agreement with the observations. All bulges present low Sérsic indexes, and are classified as pseudobulges. In regard to the discs, the same breaks in the surface brightness profiles observed in real galaxies are found, and the radii at which these take place are in agreement with the observations. Also, from edge-on unsharp-masked images at z = 0, boxy or peanut-shaped (B/P) structures are clearly identified in the inner part of four bars, and also two more bars are found in buckling phase. The sizes of the B/P match fairly well with those obtained from observations. We thus conclude that the observed photometric and structural properties of galaxies with bars, which are the main drivers of secular evolution, can be developed in present state-of-the-art ΛCDM cosmological simulations.
Title Excitation of Lateral Habenula Neurons as a Neural Mechanism Underlying Ethanol-Induced Conditioned Taste Aversion Author names and affiliation , including postal codes

Ethanol, like other drugs of abuse, has both rewarding and aversive properties. Previous work suggests that sensitivity to ethanol’s aversive effects negatively modulates voluntary alcohol intake and thus may be important in vulnerability to developing alcohol use disorders. We previously found that rats with lesions of the lateral habenula (LHb), which is implicated in aversion-mediated learning, show accelerated escalation of voluntary ethanol
Penalized robust estimators in logistic regression with applications to sparse models.

Sparse covariates are frequent in classification and regression problems and in these settings the task of variable selection is usually of interest. As it is well known, sparse statistical models correspond to situations where there are only a small number of non--zero parameters and for that reason, they are much easier to interpret than dense ones. In this paper, we focus on the logistic regression model and our aim is to address robust and penalized estimation for the regression parameter. We introduce a family of penalized weighted $M-$type estimators for the logistic regression parameter that are stable against atypical data. We explore different penalizations functions and we introduce the so--called Sign penalization. This new penalty has the advantage that it depends only on one penalty parameter, avoiding arbitrary tuning constants. We discuss the variable selection capability of the given proposals as well as their asymptotic behaviour. Through a numerical study, we compare the finite sample performance of the proposal corresponding to different penalized estimators either robust or classical, under different scenarios. A robust cross--validation criterion is also presented. The analysis of two real data sets enables to investigate the stability of the penalized estimators to the presence of outliers.
Validation of a deep learning mammography model in a population with low screening rates

A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are often only trained on data from advantaged populations that have the infrastructure and resources required for large-scale data collection. In this paper, we aim to empirically investigate the potential impact of such biases on breast cancer detection in mammograms. We specifically explore how a deep learning algorithm trained on screening mammograms from the US and UK generalizes to mammograms collected at a hospital in China, where screening is not widely implemented. For the evaluation, we use a top-scoring model developed for the Digital Mammography DREAM Challenge. Despite the change in institution and population composition, we find that the model generalizes well, exhibiting similar performance to that achieved in the DREAM Challenge, even when controlling for tumor size. We also illustrate a simple but effective method for filtering predictions based on model variance, which can be particularly useful for deployment in new settings. While there are many components in developing a clinically effective system, these results represent a promising step towards increasing access to life-saving screening mammography in populations where screening rates are currently low.
Combinatorial list-decoding of Reed-Solomon codes beyond the Johnson radius

List-decoding of Reed-Solomon (RS) codes beyond the so called Johnson radius has been one of the main open questions in coding theory and theoretical computer science since the work of Guruswami and Sudan. It is now known by the work of Rudra and Wootters, using techniques from high dimensional probability, that over large enough alphabets there exist RS codes that are indeed list-decodable beyond this radius. In this paper we take a more combinatorial approach that allows us to determine the precise relation (up to the exact constant) between the decoding radius and the list size. We prove a generalized Singleton bound for a given list size, and conjecture that the bound is tight for most RS codes over large enough finite fields. We also show that the conjecture holds true for list sizes 2 and 3, and as a by product show that most RS codes with a rate of at least 1/9 are list-decodable beyond the Johnson radius. Lastly, we give the first explicit construction of such RS codes. The main tools used in the proof are a new type of linear dependency between codewords of a code that are contained in a small Hamming ball, and the notion of cycle space from Graph Theory. Both of them have not been used before in the context of list-decoding.
Investigation of Mechanical Properties of Horn Powder-Filled Epoxy Composites

Recent environmental concerns focus on the development of bio-composites, due to their low cost, and eco-friendly and bio-degradability characteristics. The aim of this work is to investigate the properties of particle-filled composites fabricated using bio-waste horn powder (HP) and epoxy resin. Composites were fabricated according to a Taguchi L9 (3×3) orthogonal array. The HP particles and matrix were mixed in an appropriate ratio, compression-moulded and cured at room temperature to produce the specimens. Properties, such as ultimate tensile strength, tensile modulus, elongation percentage at break, flexural strength, flexural modulus, impact strength and microstructure of the specimens, were investigated. Scanning electron microscopy (SEM) micrographs of tensile tested specimens show good compatibility between HP particles and resin, when the HP volume is at 10% and increases in agglomeration with increase in the HP volume percentage. Optimization of factors was done using grey relational analysis (GRA) and analysis of variance (ANOVA). The optimum factor levels were found to be the highest NaOH concentration (con.), A3 (i.e., 0.3 N), the smallest HP size, B1 (i.e., 125 μm) and the lowest HP volume percentage, C1 (i.e., 10%). The contribution of influencing factors in decreasing order were found to be, HP volume percentage with 90.87%, HP size with 6.43% and NaOH concentration with 0.78%. From the R squared and R squared adjusted values of the properties, it is determined that most of the values are above 95%, confirming a 95% confidence level. Comparison between the optimum specimen and untreated HP specimens reveals that the optimum specimen has better properties. Hence, HP/epoxy composites can find application in various sectors.
Post-FEC BER Benchmarking for Bit-Interleaved Coded Modulation With Probabilistic Shaping

Accurate performance benchmarking after forward error correction (FEC) decoding is essential for system design in optical fiber communications. Generalized mutual information (GMI) has been shown to be successful at benchmarking the bit-error rate (BER) after FEC decoding (post-FEC BER) for systems with soft-decision (SD) FEC without probabilistic shaping (PS). However, GMI is not relevant to benchmark post-FEC BER for systems with SD-FEC and PS. For such systems, normalized GMI (NGMI), asymmetric information (ASI), and achievable FEC rate have been proposed instead. They are good at benchmarking post-FEC BER or to give an FEC limit in bit-interleaved coded modulation (BICM) with PS, but their relation has not been clearly explained so far. In this article, we define generalized L-values under mismatched decoding, which are connected to the GMI and ASI. We then show that NGMI, ASI, and achievable FEC rate are theoretically equal under matched decoding but not under mismatched decoding. We also examine BER before FEC decoding (pre-FEC BER) and ASI over Gaussian and nonlinear fiber-optic channels with approximately matched decoding. ASI always shows better correlation with post-FEC BER than pre-FEC BER for BICM with PS. On the other hand, post-FEC BER can differ at a given ASI when we change the bit mapping, which describes how each bit in a codeword is assigned to a bit tributary.
Extended papers from NPAR 2010 Contour-driven Sumie rendering of real photos

We propose a sketch-based system for rendering oriental brush strokes on complex shapes. While previous research has focused on methods for converting user-specified trajectories into oriental ink painting (Sumi-e) strokes, we propose an approach that takes as input the contours of complex shapes, estimates automatically the sizes of the brush footprints, their orientations, and the brush trajectory. Then, it renders them into oriental ink paintings. The approach performs in three steps; first complex shapes are decomposed into elementary shapes that can be rendered with a single brush stroke. Second, the brush trajectory estimation is formulated as the minimization of an energy function that measures the quality of the trajectory. Finally, the estimated trajectories are rendered into brush strokes by mapping on them footprint textures scanned from real images. We demonstrate that the proposed contour-driven approach is particularly suitable for converting real images into Sumi-e paintings with minimum interaction. To this end we develop a system where images are either interactively or automatically segmented into elementary shapes and converted into Sumi-e paintings. For the automatic segmentation, we observe that the strokes in Sumi-e paintings are nearly parallel to the edge tangent flow (ETF). We propose a segmentation procedure that is based on clustering the curves obtained by integrating the ETF of real images. Experiments on complex shapes show that the proposed contour-based approach produces a large variety of complex strokes compared to trajectory-based approaches. & 2010 Elsevier Ltd. All rights reserved.
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success

Random forests remain among the most popular off-the-shelf supervised machine learning tools with a well-established track record of predictive accuracy in both regression and classification settings. Despite their empirical success as well as a bevy of recent work investigating their statistical properties, a full and satisfying explanation for their success has yet to be put forth. Here we aim to take a step forward in this direction by demonstrating that the additional randomness injected into individual trees serves as a form of implicit regularization, making random forests an ideal model in low signal-to-noise ratio (SNR) settings. Specifically, from a model-complexity perspective, we show that the mtry parameter in random forests serves much the same purpose as the shrinkage penalty in explicitly regularized regression procedures like lasso and ridge regression. To highlight this point, we design a randomized linear-model-based forward selection procedure intended as an analogue to tree-based random forests and demonstrate its surprisingly strong empirical performance. Numerous demonstrations on both real and synthetic data are provided.
Constraining Short Gamma-Ray Burst Jet Properties with Gravitational Waves and Gamma-Rays

Gamma-ray burst (GRB) prompt emission is highly beamed, and understanding the jet geometry and beaming configuration can provide information on the poorly understood central engine and circumburst environment. Prior to the advent of gravitational-wave astronomy, astronomers relied on observations of jet breaks in the multiwavelength afterglow to determine the GRB opening angle, since the observer’s viewing angle relative to the system cannot be determined from the electromagnetic data alone. Gravitational-wave observations, however, provide an independent measurement of the viewing angle. We describe a Bayesian method for determining the geometry of short GRBs (sGRBs) using coincident electromagnetic and gravitational-wave observations. We demonstrate how an ensemble of multimessenger detections can be used to measure the distributions of the jet energy, opening angle, Lorentz factor, and angular profile of sGRBs; we find that for a population of 100 such observations, we can constrain the mean of the opening angle distribution to within 10° regardless of the angular emission profile. Conversely, the constraint on the energy distribution depends on the shape of the profile, which can be distinguished.
‘Zippin’ up My Boots, Goin’ Back to My Roots': Radical Left Parties in Southern Europe

Radical left parties actively encourage the participation of their members in internal decision-making and insist on promoting organised links to trade unions and social movements. As a party family, they deviate from what is considered to be the trend in which Western political parties have turned their backs on their social roots. Drawing on the experience of South European radical left parties from the fall of the Berlin Wall until the recent financial crisis, we argue that ideology, electoral incentives, party competition and external events explain the radical left's pronounced emphasis on linkage, while organisational trajectory explains variation within the party family in terms of the linkage strategies pursued.
Developmental and Reproductive Effects of Bisphenol A (Bpa) in Drosophila Melanogaster

Bu calismanin amaci, endokrin bozucu kimyasallardan (EBK) biri olan BPA’nin Drosophila melanogaster’in gelisimi ve uremesi uzerine etkilerini arastirmaktir. D. melanogaster larvalarina 0.1 mg/L, 1 mg/L ve 10 mg/L BPA uygulanmistir. Larvadan pupaya, pupadan ergine gecis yuzdeleri ve sureleri ve ortalama yavru dol sayilari belirlenmistir. Larvadan pupaya, pupadan ergine gecis yuzdelerinde anlamli bir degisim gozlenmemistir (p > 0.05). Ancak BPA uygulamalari ile hem ortalama pupalasma hem de ortalama erginlesme sureleri gecikmistir (p < 0.05). 0.1 mg/L and 1 mg/L BPA uygulama gruplarinda, ortalama yavru dol sayilari kontrol gruplarina gore anlamli derecede azalmistir (p < 0.05).
Data Diversification: An Elegant Strategy For Neural Machine Translation

We introduce Data Diversification: a simple strategy to boost neural machine translation (NMT) performance. It diversifies the training data by using the predictions of multiple forward and backward models and then merging them with the original dataset on which the final NMT model is trained. Our method is applicable to all NMT models. It does not require extra monolingual data like back-translation, nor does it add more computations and parameters like ensembles of models. In the experiments, our method achieves state-of-the-art BLEU score of 30.7 & 43.7 in the WMT'14 English-German & English-French tasks. It also substantially improves on 8 other translation tasks: 4 IWSLT tasks (English-German and English-French) and 4 low-resource translation tasks (English-Nepali and English-Sinhala). We demonstrate that our method is more effective than knowledge distillation and dual learning, it exhibits strong correlation with ensembles of models, and it trades perplexity off for better BLEU score. We have released our source code at https://github.com/nxphi47/data_diversification
Biocontrol of rice sheath blight with formulated Pseudomonas putida

The development of an effective formulation for the biological control agent of sheath blight (Rhizoctonia solani) of rice, P. putida PpV14i, to increase shelf-life was investigated. Of the various combinations tested, methyl cellulose:talc in the ratio 1:4 was found to maintain the bacterium in a viable state for up to 10 months, affording 60% suppression of sheath blight of rice (cv. IR 50) in field experiments conducted at Pallikaranai, Tamil Nadu, India, which is on par with the fungicide validamycin. In addition, the formulation had no adverse effects on seed germination. The results were consistent when tested by the detached leaf assay in vitro.
A Study on Crystallization of Oxalic Acid in Batch Cooling Crystallizer

A study on batch cooling crystallizer, to assess the effects of various cooling modes on the crystal size distribution, is attempted for oxalic acid. The linear cooling mode is found to give narrow crystal size distribution and higher crystal size as compared to controlled cooling modes. The effect of various operating quantities such as the seed ratio, type of agitator, agitation rates, are carried out with linear cooling mode. The increase in seed ratio is found to reduce the mean crystal size. For turbine type agitator, increase in the agitation rate beyond 140 rpm is found to widen the crystal size distribution and lower the mean crystal size, where as for anchor type agitator, agitation rate beyond 100 rpm is found to decrease mean crystal size.
Circular specifications and “predicting” with information from the future: Errors in the empirical SAOM–TERGM comparison of Leifeld & Cranmer

Abstract We review the empirical comparison of Stochastic Actor-oriented Models (SAOMs) and Temporal Exponential Random Graph Models (TERGMs) by Leifeld & Cranmer in this journal [Network Science 7(1):20–51, 2019]. When specifying their TERGM, they use exogenous nodal attributes calculated from the outcome networks’ observed degrees instead of endogenous ERGM equivalents of structural effects as used in the SAOM. This turns the modeled endogeneity into circularity and obtained results are tautological. In consequence, their out-of-sample predictions using TERGMs are based on out-of-sample information and thereby predict the future using observations from the future. Thus, their analysis rests on erroneous model specifications that invalidate the article’s conclusions. Finally, beyond these specific points, we argue that their evaluation metric—tie-level predictive accuracy—is unsuited for the task of comparing model performance.