Miracle-dz/UniFAR-FTUs huggingface dataset:
Allgemeine Abschätzung des kardiovaskulären Risikos

Validierte Scores zur Bestimmung des kardiovaskulären Risikos erlauben es, die Individuen zu identifizieren, die von präventiven Interventionen am meisten profitieren. Daneben verdeutlichen sie auf individueller Basis die Folgen ungesunden Lebensstils und die Risikoreduktion durch zielgerichtete Anpassung (z.B. LDL-C-Reduktion). Scores sind damit ein wichtiges Instrument zur Motivation von Patienten, ihr Risikoprofil zu verbessern.
Reasonably Mixing a Functional Language with Assembly ∗

We present FunTAL, the first multi-language system to formalize safe interoperability between a high-level functional language and low-level assembly code while supporting compositional reasoning about the mix. A central challenge in developing such a multi-language is bridging the gap between assembly, which is staged into jumps to continuations, and high-level code, where subterms return a result. We present a compositional stack-based typed assembly language that supports components, comprised of one or more basic blocks, that may be embedded in high-level contexts. We also present a logical relation for FunTAL that supports reasoning about equivalence of high-level components and their assembly replacements, mixed-language programs with callbacks between languages, and assembly components comprised of different numbers of basic blocks.
Niclosamide potentiates TMEM16A and induces vasoconstriction

The TMEM16A calcium-activated chloride channel is a promising therapeutic target for various diseases. Niclosamide, an anthelmintic medication, has been considered as a TMEM16A inhibitor for treating asthma and chronic obstructive pulmonary disease, but was recently found to possess broad-spectrum off-target effects. Here we show that, under physiological conditions, niclosamide acutely potentiates TMEM16A without having any inhibitory effect. Our computational and functional characterizations pinpoint a putative niclosamide binding site on the extracellular side of TMEM16A. Mutations in this site attenuate the potentiation. Moreover, niclosamide potentiates endogenous TMEM16A in vascular smooth muscle cells, triggers intracellular calcium increase, and constricts the murine mesenteric artery. Our findings advise caution when considering niclosamide as a TMEM16A inhibitor to treat diseases such as asthma, COPD, and hypertension. The identification of the putative niclosamide binding site provides insights into the mechanism of TMEM16A pharmacological modulation, shining light on developing specific TMEM16A modulators to treat human diseases.
Performance enhancement of ZnO nanowires/PbS quantum dot depleted bulk heterojunction solar cells with an ultrathin Al 2 O 3 interlayer

Depleted bulk heterojunction (DBH) PbS quantum dot solar cells (QDSCs), appearing with boosted short-circuit current density (, represent the great potential of solar radiation utilization, but suffer from the problem of increased interfacial charge recombination and reduced open-circuit voltage (. Herein, we report that an insertion of ultrathin Al2O3 layer (ca. 1.2 A thickness) at the interface of ZnO nanowires (NWs) and PbS quantum dots (QDs) could remarkably improve the performance of DBH-QDSCs fabricated from them, i.e., an increase of from 449 mV to 572 mV, from 21.90 mA/cm2 to 23.98 mA/cm2, and power conversion efficiency (PCE) from 4.29% to 6.11%. Such an improvement of device performance is ascribed to the significant reduction of the interfacial charge recombination rate, as evidenced by the light intensity dependence on and , the prolonged electron lifetime, the lowered trap density, and the enlarged recombination activation energy. The present research therefore provides an effective interfacial engineering means to improving the overall performance of DBH-QDSCs, which might also be effective to other types of optoelectronic devices with large interface area.
Appendix: Structured Reordering for Modeling Latent Alignments in Sequence Transduction

WCFG to PCFG Conversion The algorithm of converting a WCFG to its equivalent PCFG is shown in Algorithm 1. In a bottom-up manner, the algorithm first computes an inner weight β[Xk i ] for each segment, which is the total weight of all derivations with root Xk i . Then the algorithm normalizes the weight of production rules whose left-hand side is Xk i using the inner weight. The resulting normalized weight for a production rule, e.g., G[Xk i S −→ X iX j ], is the conditional probability of applying the rule Xk i S −→ X iX j given the presence of the segment Xk i . The PCFG is equivalent to the original WCFG in the sense that for each derivation D, we have
analgesic NSAID ibuprofen naproxen acetaminophen tylenol opioid morphine

Poor data quality has become a pervasive issue due to the increasing complexity and size of modern datasets. Constraint based data cleaning techniques rely on integrity constraints as a benchmark to identify and correct errors. Data values that do not satisfy the given set of constraints are flagged as dirty, and data updates are made to re-align the data and the constraints. However, many errors often require user input to resolve due to domain expertise defining specific terminology and relationships. For example, in pharmaceuticals, ’Advil’ is-a brand name for ’ibuprofen’ that can be captured in a pharmaceutical ontology. While functional dependencies (FDs) have traditionally been used in existing data cleaning solutions to model syntactic equivalence, they are not able to model broader relationships (e.g., is-a) defined by an ontology. In this paper, we take a first step towards extending the set of data quality constraints used in data cleaning by defining and discovering Ontology Functional Dependencies (OFDs). We lay out theoretical and practical foundations for OFDs, including a set of sound and complete axioms, and a linear inference procedure. We then develop effective algorithms for discovering OFDs, and a set of optimizations that efficiently prune the search space. Our experimental evaluation using real data show the scalability and accuracy of our algorithms.
SORET, HALL CURRENT, ROTATION, CHEMICAL REACTION AND THERMAL RADIATION EFFECTS ON UNSTEADY MHD HEAT AND MASS TRANSFER NATURAL CONVECTION FLOW PAST AN ACCELERATED VERTICAL PLATE

The heat and mass transfer characteristics of the unsteady hydromagnetic natural convection flow with Hall current and Soret effect of an incompressible, viscous, electrically conducting, heat absorbing and optically thin radiating fluid flow past a suddenly started vertical infinite plate through fluid saturated porous medium in a rotating environment are taken into account in this paper. Derivations of exact analytical solutions are aimed under different physical properties. The velocity, concentration and temperature profiles, Sherwood number and Nusselt number are easily examined and discussed via the closed forms obtained. Soret effect and permeability parameter tends to accelerate primary and secondary fluid velocities whereas hall current, radiation and heat absorption have reverse effect on it. Radiation and heat absorption have tendency to enhance rate of heat transfer at the plate. The results obtained here may be further used to verify the validity of obtained numerical solutions for more complicated transient free convection fluid flow problems.
Joint processing of audio-visual information for the recognition of emotional expressions in human-computer interaction

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities to control the computer such as voice, gesture, and force-feedback are emerging. Among these, voice and vision are two natural modalities in human-to-human communication. Automatic speech recognition (ASR) technology has matured enough to allow users to dictate to a word processor or operate the computer using voice commands. Computer vision techniques have enabled the computer to see. Interacting with computers in these modalities is much more natural for people, and the progression is towards the kind of interaction between humans. Despite these advances, one necessary ingredient for natural interaction is still missing—emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in some applications such as computer-aided learning or user-friendly online help. This thesis addresses the problem of detecting human emotional expressions by computer from the voice and facial motions of the user. The computer is equipped with a microphone to listen to the user's voice, and a video camera to look at the user. Prosodic features in the audio and facial motions exhibited on the face can help the computer make some inferences about the user's emotional state, assuming the users are willing to show their emotions. Another problem it addresses is the coupling between voice and the facial expression. Sometimes the user moves the lips to produce the speech, and sometimes the user only exhibits facial expression without speaking any words. Therefore, it is important to handle these two modalities accordingly. In particular, a pure “facial expression detector” will not function properly when the person is speaking, and a pure “vocal emotion recognizer” is useless when the user is not speaking. In this thesis, a complementary relationship between audio and video is proposed. Although these two modalities do not couple strongly in time, they seem to complement each other. In some cases, similar facial expressions may have different vocal characteristics, and vocal emotions having similar properties may have distinct facial behaviors.
Evolution of Optical Binary Fraction in Sparse Stellar Systems

This work studies the evolution of the fraction of optical binary stars (OBF; not including components such as neutron stars and black holes), which is caused by stellar evolution, and the contributions of various binaries to OBF via the stellar population synthesis technique. It is shown that OBF decreases from 1 to about 0.81 for stellar populations with the Salpeter initial mass function (IMF), and to about 0.85 for the case of the Kroupa IMF, on a timescale of 15 Gyr. This result depends on metallicity, slightly. The contributions of binaries varying with mass ratio, orbital period, separation, spectral types of primary and secondary, contact degree, and pair type to OBF are calculated for stellar populations with different ages and metallicities. The contribution of different kinds of binaries to OBF depends on age and metallicity. The results can be used for estimating the global OBF of star clusters or galaxies from the fraction of a kind of binary. It is also helpful for estimating the primordial and future binary fractions of sparse stellar systems from the present observations. Our results are suitable for studying field stars, open clusters, and the outer part of globular clusters, because the OBF of such objects is affected by dynamical processes, relatively slightly, but they can also be used for giving some limits for other populations.
A study of crossover operator in real coded genetic algorithm

In this paper ， we propose a new crossover operator for real coded genetic algorithm (real-coded GA). Real-coded GA is expected to be powerful optimization technique for the nonlinear functions. A designing of crossover operator is important for a GA with good performance. At this point ， for realcoded GA ， many crossover operators are proposed. As a major crossover ， the BLX-αproposed by Es-helman et a 1. ， the unimodal normal distribution crossover (UNDX) proposed by Ono et a l. However ， the conventional crωsover operators have merits and demerits of the accuracy and the processing speed. Then ， in this paper ， we propωe the center distribution crossover (CDX) 回 anew crossover operator which improves the above prublems. The performance of the propωed method is evaluated with the standard test functions. The performance of the proposed method are compared with the conventional methods.
eXtra Botany

51 Abscission is a developmental process with important implications for agricultural practices. 52 Ethylene has long been considered as a key regulator of the abscission process. The existence of 53 an ethylene-independent abscission pathway, controlled by the complex of INFLORESCENCE 54 DEFICIENT IN ABSCISSION (IDA) peptide and the HAESA (HAE) and HAESA-like2 (HSL2) 55 kinases, has been proposed, based mainly on observations that organ abscission in ethylene56 insensitive mutants was delayed but not inhibited. A recent review on plant organ abscission 57 signaling (Patharkar and Walker, 2018), highlighted the IDA-HAE-HSL2 components as the 58 regulators of organ abscission, while the role of auxin and ethylene in this process was hardly 59 addressed. After a careful analysis of the relevant abscission literature, we propose that the IDA60 HAE-HSL2 pathway is essential for the final stages of organ abscission, while ethylene plays a 61 major role in its initiation and progression. We discuss the view that the IDA-HAE-HSL2 pathway 62 is ethylene-independent, and present recent evidence showing that ethylene activates the IDA63 HAE-HSL2 complex. We conclude that the ability of an organ to abscise is tightly linked to cell 64 turgidity in the abscission zone, and suggest that lack of cell turgidity might contribute to the failure 65 of floral organ abscission in the ida mutants. 66
Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.
A Systematic Study of Data Augmentation for Protected AES Implementations

Side-channel attacks against cryptographic implementations are mitigated by the application of masking and hiding countermeasures. Hiding countermeasures attempt to reduce the Signal-to-Noise Ratio of measurements by adding noise or desynchronization effects during the execution of the cryptographic operations. To bypass these protections, attackers adopt signal processing techniques such as pattern alignment, filtering, averaging, or resampling. Convolutional neural networks have shown the ability to reduce the effect of countermeasures without the need for trace preprocessing, especially alignment, due to their shift invariant property. Data augmentation techniques are also considered to improve the regularization capacity of the network, which improves generalization and, consequently, reduces the attack complexity. In this work, we deploy systematic experiments to investigate the benefits of data augmentation techniques against masked AES implementations when they are also protected with hiding countermeasures. Our results show that, for each countermeasure and dataset, a specific neural network architecture requires a particular data augmentation configuration to achieve significantly improved attack performance. Our results clearly show that data augmentation should be a standard process when targeting datasets with hiding countermeasures in deep learning-based side-channel attacks.
Solving Partial Differential Equations by Taylor Meshless Method

Le but de cette these est de developper une methode numerique simple, robuste, efficace et precise pour resoudre des problemes d'ingenierie de grande taille a partir de la methode Taylor Meshless (TMM) et fournir de nouvelles idees principales de TMM est d'utiliser comme fonctions de forme des polynomes d'ordre eleve qui sont des solutions approchees de l'EDP. Ainsi la discretisation ne concerne que la frontiere. Les coefficients de ces fonctions de forme sont obtenus en discretisant les conditions aux limites par des procedures de collocation associees a la methode des moindres carres. TMM est alors une veritable methode sans maillage sans processus d'integration, les conditions aux limites etant obtenues par collocation. Les principales contributions de cette these sont les suivantes: 1) Base sur TMM, un algorithme general et efficace a ete developpe pour resoudre des EDP elliptiques tridimensionnelles; 2) Trois techniques de couplage pour des resolutions par morceaux ont ete discutees dans des cas de problemes a grande echelle: la methode de collocation par les moindres carres et deux methodes de couplage basees sur les multiplicateurs de Lagrange; 3) Une methode numerique generale pour resoudre les EDP non-lineaires a ete proposee en combinant la methode de Newton, la TMM et la technique de differentiation automatique. 4) Pour resoudre des problemes avec un bord non regulier, des solutions singulieres satisfaisant l'equation de controle sont introduites comme des fonctions de forme complementaires, ce qui fournit une base theorique pour la resolution de problemes singuliers
A Spatial Approach to Predict Performance of Conversational Search Systems

Recent advancements in Information Retrieval and Natural Language Processing have led to significant developments in the way users interact with search engines, with traditional one-shot textual queries being replaced by multi-turn conversations. As a highly interactive search scenario, Conversational Search (CS) can significantly benefit from Query Performance Prediction (QPP) techniques. However, the application of QPP in the CS domain is a relatively new field and requires proper framing. This study proposes a set of spatial-based QPP models, designed to work effectively in the conversational search domain, where dense neural retrieval models are the most common approach and query cutoffs are small. The proposed QPP approaches are shown to improve the predictive performance over the state-of-the-art in different scenarios and collections, highlighting the utility of QPP in the CS domain.
Dissatisfaction Induced by Pairwise Swaps

Fairness is increasingly recognized as an important property of information access systems. Pairwise fairness is a measure of equity in ranking whose normative grounding has not been clearly studied nor discussed in the literature. In this work, we target this gap by providing a clear interpretation for this family of measures, by demonstrating and remedying its key limitations, and by analysing its relationship to other measures of fair ranking.
Key Environmental Lexicon Extraction Using Generative Transformer (Short Paper)

This paper presents a study of the core environmental lexicon at the intersection of the fields of terminology and natural language processing. The goal was to find a way of automatizing the expansion of the preselected keyword list, and, in particular, to evaluate the ability of generative transformers to extract keywords unseen during the training phase. As a starting point, we collected keywords pertinent to the environmental discourse. Additionally, we compiled a corpus of texts on current and emerging environmental issues. These materials were used to train deep generative models of two types: a T5 transformer and a pointer-generator network pretrained for concept extraction as a baseline. We show that T5 significantly outperforms the baseline in detecting unseen keywords. We further provide qualitative analysis of the outcome of the resulting model applied to weakly annotated texts and confirm that the model helps to discover more keywords pertinent to the environmental topic.
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear

To use deep reinforcement learning in the wild, we might hope for an agent that can avoid catastrophic mistakes. Unfortunately, even in simple environments, the popular deep Q-network (DQN) algorithm is doomed by a Sisyphean curse. Owing to the use of function approximation, these agents eventually forget experiences as they become exceedingly unlikely under a new policy. Consequently, for as long as they continue to train, DQNs may periodically relive catastrophic mistakes. In this paper, we demonstrate unacceptable performance of DQNs on two toy problems. We then introduce intrinsic fear , a new method that mitigates these problems by avoiding states deemed dangerous. Our approach incorporates a second model trained via supervised learning to predict the probability of catastrophe within a short number of steps. This score then acts to penalize the Q-learning objective, shaping the reward function away from catastrophic states.
AN INCREMENTAL QUASI-NEWTON METHOD WITH LOCAL SUPERLINEAR CONVERGENCE RATE ∗

The problem of minimizing an objective that can be written as the sum of a set of n smooth and strongly convex functions is challenging because the cost of evaluating the function and its derivatives is proportional to the number of elements in the sum. The incremental quasi-Newton (IQN) method proposed here belongs to the family of stochastic and incremental methods that have a cost per iteration independent of n. IQN iterations are a stochastic version of BFGS iterations that use memory to reduce the variance of stochastic approximations. The method is shown to exhibit local superlinear convergence. The convergence properties of IQN bridge a gap between deterministic and stochastic quasi-Newton methods. Deterministic quasi-Newton methods exploit the possibility of approximating the Newton step using objective gradient differences. They are appealing because they have a smaller computational cost per iteration relative to Newton’s method and achieve a superlinear convergence rate under customary regularity assumptions. Stochastic quasi-Newton methods utilize stochastic gradient differences in lieu of actual gradient differences. This makes their computational cost per iteration independent of the number of objective functions n. However, existing stochastic quasi-Newton methods have sublinear or linear convergence at best. IQN is the first stochastic quasiNewton method proven to converge superlinearly in a local neighborhood of the optimal solution. IQN differs from state-of-the-art incremental quasi-Newton methods in three aspects: (i) The use of aggregated information of variables, gradients, and quasi-Newton Hessian approximation matrices to reduce the noise of gradient and Hessian approximations. (ii) The approximation of each individual function by its Taylor’s expansion, in which the linear and quadratic terms are evaluated with respect to the same iterate. (iii) The use of a cyclic scheme to update the functions in lieu of a random selection routine. We use these fundamental properties of IQN to establish its local superlinear convergence rate. The presented numerical experiments match our theoretical results and justify the advantage of IQN relative to other incremental methods.