Miracle-dz/UniFAR-FTUs huggingface dataset:
SAT Is an Effective and Complete Method for Solving Stable Matching Problems with Couples

Stable matchings can be computed by deferred acceptance (DA) algorithms. However such algorithms become incomplete when complementarities exist among the agent preferences: they can fail to find a stable matching even when one exists. In this paper we examine stable matching problems arising from labour market with couples (SMP-C). The classical problem of matching residents into hospital programs is an example. Couples introduce complementarities under which DA algorithms become incomplete. In fact, SMP-C is NP-complete. Inspired by advances in SAT and integer programming (IP) solvers we investigate encoding SMP-C into SAT and IP and then using state-of-the-art SAT and IP solvers to solve it. We also implemented two previous DA algorithms. After comparing the performance of these different solution methods we find that encoding to SAT can be surprisingly effective, but that our encoding to IP does not scale as well. Using our SAT encoding we are able to determine that the DA algorithms fail on a non-trivial number of cases where a stable matching exists. The SAT and IP encodings also have the property that they can verify that no stable matching exists, something that the DA algorithms cannot do.
Temperature effects and long term fading of implanted and un-implanted gate oxide RADFETs

Key aspects of continuous operation of Radiation Sensitive Field Effect Transistors (RADFETs) as radiation monitors in space missions are addressed. The effect of possible temperature fluctuations and long-term fading on threshold-voltage measurement of Implanted and Unimplanted gate oxide RADFETs were studied. Evidence for temperature coefficient changes following irradiation and annealing cycles is presented. In addition, fading of unimplanted gate oxide RADFETs is shown to be significantly lower than that of Implanted ones.
Protective effects of fentanyl preconditioning on cardiomyocyte apoptosis induced by ischemia-reperfusion in rats

We aimed to study the effect of fentanyl (Fen) preconditioning on cardiomyocyte apoptosis induced by ischemia-reperfusion (I/R) in rats. A total of 120 Sprague Dawley male rats (age: 3 months) were randomly divided into: sham operation group (S group), I/R group, normal saline I/R group (NS group), and fentanyl low, middle, and high dose groups (Fen1: 2 μg/kg; Fen2: 4 μg/kg; Fen3: 6 μg/kg). Heart rate (HR), mean arterial pressure (MAP), left ventricular developed pressure (LVDP), ±dp/dtmax, malondialdehyde (MDA), superoxide dismutase (SOD) activity, creatine phosphokinase-MB (CK-MB), and cardiac troponin-I (cTnI) were measured. Myocardial ischemic (MI) area, total apoptotic myocardial cells, and protein and mRNA expressions of B-cell lymphoma 2 (Bcl-2) and Bax were detected. HR and MAP were higher, while LVDP and ±dp/dtmax were close to the base value in the Fen groups compared to those in the I/R group. Decreased MDA concentration and CK-MB value and increased SOD activity were found in the Fen groups compared to the I/R group, while cTnI concentration was significantly lower in the Fen1 and Fen2 groups (all P<0.05). Myocardial damage was less in the Fen groups compared to the I/R group and the MI areas and apoptotic indexes were significantly lower in the Fen1 and Fen2 groups (all P<0.05). Furthermore, significantly increased protein and mRNA expressions of Bcl-2, and decreased protein and mRNA expressions of Bax were found in the Fen groups compared to the I/R group (all P<0.05). Fentanyl preconditioning may suppress cardiomyocyte apoptosis induced by I/R in rats by regulating Bcl-2 and Bax.
Multi-decadal hydrologic change and variability in the Amazon River basin: understanding terrestrial water storage variations and drought characteristics

Abstract. We investigate the interannual and interdecadal hydrological changes in the Amazon River basin and its sub-basins during the 1980–2015 period using GRACE satellite data and a physically based, 2 km grid continental-scale hydrological model (LEAF-Hydro-Flood) that includes a prognostic groundwater scheme and accounts for the effects of land use–land cover (LULC) change. The analyses focus on the dominant mechanisms that modulate terrestrial water storage (TWS) variations and droughts. We find that (1) the model simulates the basin-averaged TWS variations remarkably well; however, disagreements are observed in spatial patterns of temporal trends, especially for the post-2008 period. (2) The 2010s is the driest period since 1980, characterized by a major shift in the decadal mean compared to the 2000s caused by increased drought frequency. (3) Long-term trends in TWS suggest that the Amazon overall is getting wetter (1.13 mm yr−1), but its southern and southeastern sub-basins are undergoing significant negative TWS changes, caused primarily by intensified LULC changes. (4) Increasing divergence between dry-season total water deficit and TWS release suggests a strengthening dry season, especially in the southern and southeastern sub-basins. (5) The sub-surface storage regulates the propagation of meteorological droughts into hydrological droughts by strongly modulating TWS release with respect to its storage preceding the drought condition. Our simulations provide crucial insight into the importance of sub-surface storage in alleviating surface water deficit across Amazon and open pathways for improving prediction and mitigation of extreme droughts under changing climate and increasing hydrologic alterations due to human activities (e.g., LULC change).
A computational framework to empower probabilistic protein design

Motivation: The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult. Results: In this paper, we develop a computational framework for probabilistic protein design following the structural paradigm. We formulate the distribution of sequences for a structure using the Boltzmann distribution over their free energies. The corresponding probabilistic graphical model is constructed, and we apply belief propagation (BP) to calculate marginal amino acid probabilities. We test this method on a large structural dataset and demonstrate the superiority of BP over previous methods. Nevertheless, since the results obtained by BP are far from optimal, we thoroughly assess the paradigm using high-quality experimental data. We demonstrate that, for small scale sub-problems, BP attains identical results to those produced by exact inference on the paradigmatic model. However, quantitative analysis shows that the distributions predicted significantly differ from the experimental data. These findings, along with the excellent performance we observed using BP on the smaller problems, suggest potential shortcomings of the paradigm. We conclude with a discussion of how it may be improved in the future. Contact: fromer@cs.huji. ac.il
Novel hexagonal polytypes of silver: growth, characterization and first-principles calculations

We report a study of the relative effects of dimensional and kinetic constraints on the stabilization of metastable, polytypic forms of metallic silver. We show that the hexagonal 4H polytype (hitherto observed only in size-constrained systems) can be produced in the form of bulk thin films by suitably slowing down the growth kinetics. Further, using extremely slow growth conditions, we have been successful in depositing a novel, two-dimensional, metastable polytype (2H) of silver, which is highly reactive (easily oxidized) and has a density 23% lower than normal silver. First-principles calculations based on density functional theory confirm that the 4H structure is relatively stable. However, local stability analysis via a determination of the phonon dispersion of the 2H structure reveals that it is only marginally stable with an energy surface that is rather flat or weakly varying with respect to many of the modes. This makes a large contribution to the configurational entropy and is probably the reason for the metastability of the observed 2H polytype with an unusually large lattice constant along the c-direction.
Clinical Protection from Falciparum Malaria Correlates with Neutrophil Respiratory Bursts Induced by Merozoites Opsonized with Human Serum Antibodies

Background Effective vaccines to combat malaria are urgently needed, but have proved elusive in the absence of validated correlates of natural immunity. Repeated blood stage infections induce antibodies considered to be the main arbiters of protection from pathology, but their essential functions have remained speculative. Methodology/Principal Findings This study evaluated antibody dependent respiratory burst (ADRB) activity in polymorphonuclear neutrophils (PMN) induced by Plasmodium falciparum merozoites and antibodies in the sera of two different African endemic populations, and investigated its association with naturally acquired clinical protection. Respiratory bursts by freshly isolated PMN were quantified by chemiluminescence readout in the presence of isoluminol, which preferentially detects extra-cellular reactive oxygen species (ROS). Using a standardized, high throughput protocol, 230 sera were analyzed from individuals of all age groups living in meso- (Ndiop) or holo-endemic (Dielmo) Senegalese villages, and enrolled in a cross-sectional prospective study with intensive follow-up. Statistical significance was determined using non-parametric tests and Poisson regression models. The most important finding was that PMN ADRB activity was correlated with acquired clinical protection from malaria in both high and low transmission areas (P = 0.006 and 0.036 respectively). Strikingly, individuals in Dielmo with dichotomized high ADRB indexes were seventeen fold less susceptible to malaria attacks (P = 0.006). Complementary results showed that ADRB activity was (i) dependent on intact merozoites and IgG opsonins, but not parasitized erythrocytes, or complement, (ii) correlated with merozoite specific cytophilic IgG1 and IgG3 antibody titers (P<0.001 for both), and (iii) stronger in antisera from a holo-endemic compared to a meso-endemic site (P = 0.002), and reduced in asymptomatic carriers (P<0.001). Conclusions/Significance This work presents the first clearly demonstrated functional antibody immune correlate of clinical protection from Plasmodium falciparum malaria, and begs the question regarding the importance of ADRB by PMN for immune protection against malaria in vivo.
Integrating Influence Mechanisms into Impact Analysis for Increased Precision

Software change impact analysis is the process of determining the potential effects, or impacts, of a change to a program. Strategies for impact analysis vary in their approach toward the opposing goals of high precision and low analysis time. Fine-grained techniques, such as slicing, can be used to gain very precise knowledge of a change's impact, but may be prohibitively expensive. Coarse-grained techniques such as method-level impact analyses sacrifice precision for faster analysis. In this paper, we present static and dynamic method-level impact analysis algorithms that utilize value propagation information from the source code to increase precision and keep analysis times low. We experimentally compare the results of our analyses with common static and dynamic impact analysis techniques. Our results show that the precision of the common method-level analyses can be improved with very little added overhead
Emotional perception for updating agents' beliefs

The relative influence of perception and situation in emotional judgments has been extensively debated in psychology. A main issue in this debate concerns how these sources of information are integrated. This work proposes a method able to make probabilistic predictions of appraisals of other agents, using mental models of those agents. From these appraisal predictions, predictions about another agent's expressions are made, integrated with observations of the other agent's ambiguous emotional expressions using Bayesian techniques, resulting in updates to the agent's mental models. Our method is inspired by psychological work on human interpretation of emotional expressions. We demonstrate how these appraisals of others' emotions and observations of their expressions can be an integral part of an agent capable of Theory of Mind reasoning.
Effect of Teachers' Stereotyping on Students' Stereotyping of Mathematics as a Male Domain

Abstract This multilevel analysis used data from a representative sample from Grades 6, 7, and 8 in public schools in Switzerland. The data included information on (a) 6,602 students (3,307 girls, 3,295 boys) nested within 338 classes and (b) 321 mathematics teachers of these classes. The teachers and the students tended to stereotype mathematics as a male domain, and the teachers' stereotypes significantly affected the students' stereotypes after the author controlled for achievement, interest, and self-confidence in mathematics and for school grade and schooling track.
Sustainability of lemon (Citrus aurantifolia Swingle) farms in the province of Santa Elena, Ecuador

Surveys were applied to a sample of 83 lemon producers from this province, with structured questions about economic, ecological and socio-cultural dimensions. The sustainability was determined with techniques of multicriteria analysis to calculate the Economic Indicator (IK), Ecological Indicator (IE) and Socio-Cultural Indicator (ISC), to estimate the General Sustainability Indicator (IS Gen) of each farm. The 25.3% of the farms had an IK> 2; 60.24% an IE> 2 and 55.4% an ISC> 2. The 74.7% of the farms had an IS Gen <2, which indicates that the farms are not sustainable.
Pattern recognition in spatial data: a new method of seismic explorations for oil and gas in crystalline basement rocks

The problem of prospecting oil and gas reserves in the crystalline basement of the Earth mantle by way of a combined interpretation of seismic data registered on the daylight surface and direct information from a sparse net of exploratory wells is considered as pattern recognition problem in which the role of objects whose class membership is to be recovered is played by points of the three-dimensional underground medium. Local properties of reflected seismic signals serve as features of the membership of the respective rock mass zones in the class of collectors, i.e. spatial areas capable of accumulating fluids, whereas direct data obtained from exploratory wells serve as trainer's information. A new spatial approach to supervised pattern recognition is proposed which makes use of the fact that objects to be recognized are arranged in an array in space. Along with the additional assumption that immediately adjacent points offer a tendency to belong to the same class, this fact allows for drawing reliable decisions from relatively unreliable features.
Can nomograms be superior to other prediction tools?

Accurate estimates of the likelihood of treatment success, complications and long‐term morbidity are essential for counselling and informed decision‐making in patients with urological malignancies. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous patient distribution. Nomograms, risk groupings, artificial neural networks (ANNs), probability tables, and classification and regression tree (CART) analyses represent the available decision aids that can be used within these tasks. We critically reviewed available decision aids (nomograms, risk groupings, ANNs, probability tables and CART analyses) and compared their ability to predict the outcome of interest. Of the available decision aids, nomograms provide individualized evidence‐based and highly accurate risk estimates that facilitate management‐related decisions. We suggest the use of nomograms for the purpose of evidence‐based, individualized decision‐making.
Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g.,"skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.
Illumination cones for recognition under variable lighting: faces

Due to illumination variability, the same object can appear dramatically different even when viewed in fixed pose. To handle this variability, an object recognition system must employ a representation that is either invariant to, or models this variability. This paper presents an appearance-based method for modeling the variability due to illumination in the images of objects. The method differs from past appearance-based methods, however, in that a small set of training images is used to generate a representation-the illumination cone-which models the complete set of images of an object with Lambertian reflectance map under an arbitrary combination of point light sources at infinity. This method is both an implementation and extension (an extension in that it models cast shadows) of the illumination cone representation proposed in Belhumeur and Kriegman (1996). The method is tested on a database of 660 images of 10 faces, and the results exceed those of popular existing methods.
Update on Pregnancy in Chronic Kidney Disease

The occurrence of pregnancy in patients with chronic kidney disease (CKD) has been considered a dangerous event both for the mother and for the fetus. However, increasing evidence shows that the stage of CKD is the leading factor that can predict possible acceleration in the declining of renal function and complications of pregnancy. This review summarizes recent data on pregnancy in patients with CKD, dialysis and kidney transplantation.
Discussion of “Data‐driven confounder selection via Markov and Bayesian networks” by Häggström

We are grateful for the opportunity to discuss this article. Selecting the appropriate set of covariates to adjust for when estimating causal effects in observational studies is an important methodological problem. A number of criteria have been proposed and the article makes an important contribution by comparing and contrasting these methods via theoretical results, simulation and a data analysis. In what follows, we discuss an alternative way to represent counterfactual causal models with graphs, different approaches for selecting sets to adjust for, and make some comments on the specific data example. We do not discuss the important problem of inference after model selection in our commentary.
Renin-Angiotensin System Blockers Protect Pancreatic Islets against Diet-Induced Obesity and Insulin Resistance in Mice

Background The associations between obesity, hypertension and diabetes are well established, and the renin-angiotensin system (RAS) may provide a link among them. The effect of RAS inhibition on type 2 diabetes is still unclear; however, RAS seems to play an important role in the regulation of the pancreas and glucose intolerance of mice fed high-fat (HF) diet. Methods C57BL/6 mice fed a HF diet (8 weeks) were treated with aliskiren (50 mg/kg/day), enalapril (30 mg/kg/day) or losartan (10 mg/kg/day) for 6 weeks, and the protective effects were extensively compared among groups by morphometry, stereological tools, immunostaining, Western blotting and hormonal analysis. Results All RAS inhibitors significantly attenuated the increased blood pressure in mice fed a HF diet. Treatment with enalapril, but not aliskiren or losartan, significantly attenuated body mass (BM) gain, glucose intolerance and insulin resistance, improved the alpha and beta cell mass and prevented the reduction of plasma adiponectin. Furthermore, enalapril treatment improved the protein expression of the pancreatic islet Pdx1, GLUT2, ACE2 and Mas receptors. Losartan treatment showed the greatest AT2R expression. Conclusion Our findings indicate that ACE inhibition with enalapril attenuated several of the deleterious effects of the HF diet. In summary, enalapril appears to be responsible for the normalization of islet morphology and function, of alpha and beta cell mass and of Pdx1 and GLUT2 expression. These protective effects of enalapril were attributed, primarily, to the reduction in body mass gain and food intake and the enhancement of the ACE2/Ang (1-7) /Mas receptor axis and adiponectin levels.