Miracle-dz/UniFAR-FTUs huggingface dataset:
Stroke Comparison between Professional Tennis Players and Amateur Players using Advanced Computer Vision

: In this paper, we created a method to find how professional and amateur tennis serves differ from each other. We collected videos from online and from our own recordings and turned those videos into frames. From those frames, we manually selected ones appropriate for our study and ran those through a pose estimation system, which turned those frames into simple stick figures of the players including all the x and y coordinates of the player. By normalizing all data, we were able to calculate the Euclidean distance between two compared players’ joints and analyze their consistency in their serves. Our results from our t-tests showed that there was a significant difference between the amateur’s consistency and the pro’s consistency and body parts like both shoulders showed a significant difference.
Proteins : energy, heat and signal flow

ENERGY TRANSDUCTION IN MOLECULAR MOTORS Energy Balance and Dynamics of Kinesin Motors, Changbong Hyeon and Jose N. Onuchic Mechanochemical Coupling in Molecular Motors: Insights from Molecular Simulations of the Myosin Motor Domain, Haibo Yu, Yang Yang, Liang Ma, and Qiang Cui The Chemomechanical Coupling Mechanisms of Kinesin and Dynein, Yi Qin Gao and Qiang Shao Electron Transfer Reactions Coupled with Proton Translocation: Cytochrome Oxidase, Proton Pumps, and Biological Energy Transduction, Alexei Stuchebrukhov VIBRATIONAL ENERGY FLOW IN PROTEINS: MOLECULAR DYNAMICS-BASED METHODS Molecular Dynamics Simulation of Proteins: Two Models of Anharmonic Dynamics, Akinori Kidera, Kei Moritsugu, Yasuhiro Matsunaga, and Hiroshi Fujisaki Energy Flow Pathways in Photoreceptor Proteins, Takahisa Yamato Nonequilibrium Molecular Dynamics Simulation of Photoinduced Energy Flow in Peptides: Theory Meets Experiment, Phuong H. Nguyen, Peter Hamm, and Gerhard Stock Energy Flow Analysis in Proteins via Ensemble Molecular Dynamics Simulations: Time-Resolved Vibrational Analysis and Surficial Kirkwood-Buff Theory, Masataka Nagaoka, Isseki Yu, and Masayoshi Takayanagi VIBRATIONAL ENERGY FLOW IN PROTEINS AND NANOSTRUCTURES: NORMAL MODE-BASED METHODS Directed Energy Funneling in Proteins: From Structure to Function, Yong Zhang and John E. Straub A Minimalist Network Model for Studying Biomolecular Vibration, Mingyang Lu and Jianpeng Ma Heat Transport in Proteins, David M. Leitner Heat Transfer in Nanostructures, Dvira Segal CONFORMATIONAL TRANSITIONS AND REACTION PATH SEARCHES IN PROTEINS Tubes, Funnels, and Milestones, Ron Elber, Krzysztof Kuczera, and Gouri S. Jas Pathways and Rates for Structural Transformations of Peptides and Proteins, David J. Wales, Joanne M. Carr, Mey Khalili, Vanessa K. de Souza, Birgit Strodel, and Chris S. Whittleston Energy Flow and Allostery in an Ensemble, Vincent J. Hilser and Steven T. Whitten Molecular Dynamics Simulation Studies of Coupled Protein and Water Dynamics, Douglas J. Tobias, Neelanjana Sengupta, and Mounir Tarek Index
Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers

: With the breakthrough of pervasive advanced networking infrastructures and paradigms such as 5G and IoT, cybersecurity became an active and crucial ﬁeld in the last years. Furthermore, machine learning techniques are gaining more and more attention as prospective tools for mining of (possibly malicious) packet traces and automatic synthesis of network intrusion detection systems. In this work, we propose a modular ensemble of classiﬁers for spotting malicious attacks on Wi-Fi networks. Each classiﬁer in the ensemble is tailored to characterize a given attack class and is individually optimized by means of a genetic algorithm wrapper with the dual goal of hyper-parameters tuning and retaining only relevant features for a speciﬁc attack class. Our approach also considers a novel false alarm management procedure thanks to a proper reliability measure formulation. The proposed system has been tested on the well-known AWID dataset, showing performances comparable with other state of the art works both in terms of accuracy and knowledge discovery capabilities. Our system is also characterized by a modular design of the classiﬁcation model, allowing to include new possible attack classes in an efﬁcient way.
The Impact of the Transparency Consent Framework on Current Programmatic Advertising Practices

With General Data Protection Regulation (GDPR) introduced, many online advertising practices were affected as data-driven techniques were inhibited by missing user consents. Meanwhile, the IAB Europe introduced the Transparency and Consent Framework to adapt the GDPR requirements into the online advertising ecosystem and provide support in handling consent management for involved actors. In this paper, the impact of the new framework from a programmatic advertising campaign perspective is reflected from a practitioner point of view and implications of missing user consent in five typical techniques which are applied in programmatic campaigns (targeting, retargeting, frequency capping, frequency tracking and cross-device targeting) are addressed and also viewed from an e-commerce perspective. The discussion indicates potential losses in the effectiveness of the applied techniques as well as a potential shift in the market towards walledgarden DSPs such as Google or Facebook. It further provides awareness to raise the potential implications addressed and open future work in this regard.
Validating Results of 3D Finite Element Simulation for Mechanical Stress Evaluation using Machine Learning Techniques

When a new mechanical part is designed its configuration has to be tested for durability in different usage conditions (‘stress evaluation’). Before real test samples are produced, the model is checked analytically via 3D Finite Element Simulation. Even though the simulation produces good results, in certain conditions these could be unreliable. As a result, validation of simulation results is currently a task for experts. However, this task is time-consuming and significantly depends on experts’ competence. To reduce the manual checking effort and avoid possible mistakes, machine learning methods are proposed to perform automatic pre-sorting. The paper compares several approaches to solve the problem: (i) machine learning approach, relying on geometric feature engineering, (ii) 2D convolutional neural networks, and (iii) 3D convolutional neural networks. The results show that usage of neural networks can successfully classify the samples of the given
Computing Massive Trust Analytics for Twitter using Apache Spark with Account Self-assessment

: Although trust is predominantly a human trait, it has been carried over to the Web almost since its very inception. Given the rapid Web evolution to a true melting pot of human activity, trust plays a central role since there is a massive number of parties interested in interacting in a multitude of ways but have little or even no reason to trust a priori each other. This has led to schemes for evaluating Web trust in contexts such as e-commerce, social media, recommender systems, and e-banking. Of particular interest in social networks are classiﬁcation methods relying on network-dependent attributes pertaining to the past online behavior of an account. Since the deployment of such methods takes place at Internet scale, it makes perfect sense to rely on distributed processing platforms like Apache Spark. An added beneﬁt of distributed platforms is paving the way algorithmically and computationally for higher order Web trust metrics. Here a Web trust classiﬁer in MLlib, the machine learning library for Apache Spark, is presented. It relies on both the account activity but also on that of similar accounts. Three datasets obtained from topic sampling regarding trending Twitter topics serve as benchmarks. Based on the experimental results best practice recommendations are given.
Molecular gold clusters as precursors to heterogeneous catalysts

This work of this thesis covers a number of different aspects of chemistry. Initially, the relationship between the number of atoms at the core of molecular clusters of gold and the steric bulk of the stabilising phosphine ligands is explored. A range of gold clusters was prepared through the use of increasingly bulky phosphine ligands. Theses clusters were characterised using a number of techniques to gain an insight into their structures. Catalysis chemistry was investigated by using the clusters to synthesise heterogeneous catalysts. The effect of varying the loading and calcination temperature of the catalysts on their catalytic activity for the oxidation of benzyl alcohol was researched. A number of standard (XRD, TEM) and advanced characterisation (in situ XAS) methods were utilised to determine the effect on the catalyst structure by calcination temperature and gold loading. The relationship between the catalytic activity and the size of the cluster used to prepare the catalyst, as well as the type of catalyst support used, was also examined. Synchrotron radiation allowed for in situ experiments to be designed and utilized in order to learn about a new low temperature peroxide assisted ligand removal process as an alternative to calcination. This method proved to enable more control over the rate of phosphine loss and could possible be used to activate other ligand-stabilised clusters for catalysis.
FANCD2 and HES1 suppress inflammation‐induced PPARɣ to prevent haematopoietic stem cell exhaustion

The Fanconi anaemia protein FANCD2 suppresses PPARƔ to maintain haematopoietic stem cell’s (HSC) function; however, the underlying mechanism is not known. Here we show that FANCD2 acts in concert with the Notch target HES1 to suppress inflammation‐induced PPARƔ in HSC maintenance. Loss of HES1 exacerbates FANCD2‐KO HSC defects. However, deletion of HES1 does not cause more severe inflammation‐mediated HSC defects in FANCD2‐KO mice, indicating that both FANCD2 and HES1 are required for limiting detrimental effects of inflammation on HSCs. Further analysis shows that both FANCD2 and HES1 are required for transcriptional repression of inflammation‐activated PPARg promoter. Inflammation orchestrates an overlapping transcriptional programme in HSPCs deficient for FANCD2 and HES1, featuring upregulation of genes in fatty acid oxidation (FAO) and oxidative phosphorylation. Loss of FANCD2 or HES1 augments both basal and inflammation‐primed FAO. Targeted inhibition of PPARƔ or the mitochondrial carnitine palmitoyltransferase‐1 (CPT1) reduces FAO and ameliorates HSC defects in inflammation‐primed HSPCs deleted for FANCD2 or HES1 or both. Finally, depletion of PPARg or CPT1 restores quiescence in these mutant HSCs under inflammatory stress. Our results suggest that this novel FANCD2/HES1/PPARƔ axis may constitute a key component of immunometabolic regulation, connecting inflammation, cellular metabolism and HSC function.
Enhanced IgG1‐mediated antibody response towards thymus‐dependent immunization in CXCR1‐deficient mice

Chemokine receptors and their corresponding ligands are key players of immunity by regulation of immune cell differentiation and migration. CXCR1 is a high‐affinity receptor for CXCL8. Differential expression of CXCR1 is associated with a variety of human pathologies including cancer and inflammatory diseases. While various studies have highlighted the importance of CXCR1‐mediated CXCL8‐sensing for neutrophil trafficking and function, its role in B‐cell responses remains unsolved. Therefore, our aim was to investigate innate and adaptive antibody responses in CXCR1‐deficient mice.
Context-specific abnormalities of the central executive network in first-episode psychosis: relationship with cognition

Abstract Background Cognitive impairments, which contribute to the profound functional deficits observed in psychotic disorders, have found to be associated with abnormalities in trial-level cognitive control. However, neural tasks operate within the context of sustained cognitive states, which can be assessed with ‘background connectivity’ following the removal of task effects. To date, little is known about the integrity of brain processes supporting the maintenance of a cognitive state in individuals with psychotic disorders. Thus, here we examine background connectivity during executive processing in a cohort of participants with first-episode psychosis (FEP). Methods The following fMRI study examined background connectivity of the dorsolateral prefrontal cortex (DLPFC), during working memory engagement in a group of 43 patients with FEP, relative to 35 healthy controls (HC). Findings were also examined in relation to measures of executive function. Results The FEP group relative to HC showed significantly lower background DLPFC connectivity with bilateral superior parietal lobule (SPL) and left inferior parietal lobule. Background connectivity between DLPFC and SPL was also positively associated with overall cognition across all subjects and in our FEP group. In comparison, resting-state frontoparietal connectivity did not differ between groups and was not significantly associated with overall cognition, suggesting that psychosis-related alterations in executive networks only emerged during states of goal-oriented behavior. Conclusions These results provide novel evidence indicating while frontoparietal connectivity at rest appears intact in psychosis, when engaged during a cognitive state, it is impaired possibly undermining cognitive control capacities in FEP.
Catalysis and Adsorption by Zeolites: Proceedings of Zeocat 90, Leipzig, August 20-23, 1990

Invited Lectures . Catalysis on ZSM-5 zeolites modified by phosphorus (G. Ohlmann et al.). New directions in zeolite catalysis (J. Weitkamp). Zeolites as catalysts for alkane oxidations (R.F. Parton et al.). Sorption and separation of binary mixtures of CH 4 , N 2 and CO 2 in zeolites (L.V.C. Rees). Use of ZSM zeolites in the liquid phase separation of alcohols (R. Schollner, W.D. Einicke). Basic principles and recent results of 1 H magic-angle-spinning and pulsed field gradient nuclear magnetic resonance studies on zeolites (H. Pfeifer et al.). Spectral study of Lewis acidity of zeolites and of its role in catalysis (V.B. Kazansky). Comparative measurements on acidity of zeolites (H.G. Karge). Acidity and basicity in zeolites (D. Barthomeuf). Matrix vs zeolite contributions to the acidity of fluid cracking catalysts (R. v. Ballmoos, C.-M.T. Hayward). ZEOSORB HS-30 - a template-free synthesized pentasil-type zeolite (K.-H. Bergk et al.). Selective conversion on syngas to hydrocarbons by zeolites (H. Tominaga et al.). 129 Xe NMR of adsorbed xenon for the determination of void spaces (Q. Chen et al.). Diffusion of hydrocarbons in A and X zeolites and silicalite (D.M. Ruthven et al.). Atlas of zeolite structure types: Past - Present - Future (W.M. Meier). Zeolites as membranes: The role of the gas-crystal interface (R.M. Barrer). The roles of metal and organic cations in zeolite synthesis (D.E.W. Vaughan). Temperature dependence of nucleation of zeolites in alkaline aluminosilicate gels in hydrothermal crystallization conditions (S.P. Zhdanov et al.). Submitted Papers . Catalysis (14 Papers). Sorption and Diffusion (5 Papers). Synthesis and Structure (16 Papers). Acidity (6 Papers). Author Index. Subject Index.
Incident Type 2 Diabetes Duration and Cancer Risk: A Prospective Study in Two US Cohorts.

BACKGROUND The influence of type 2 diabetes mellitus (T2D) duration on cancer incidence remains poorly understood. METHODS We prospectively followed for cancer incidence 113 429 women in the Nurses' Health Study (1978-2014) and 45 604 men in the Health Professionals Follow-up Study (1988-2014) who were free of diabetes and cancer at baseline. Cancer incidences were ascertained by review of medical records. RESULTS In the multivariable-adjusted model incident, T2D was associated with higher risk of cancers in the colorectum, lung, pancreas, esophagus, liver, thyroid, breast, and endometrium. The pooled hazard ratios (HRs) ranged from 1.21 (95% confidence interval [CI] = 1.06 to 1.38) for colorectal cancer to 3.39 (95% CI = 2.24 to 5.12) for liver cancer. For both composite cancer outcomes and individual cancers, the elevated risks did not further increase after 8 years of T2D duration. The hazard ratio for total cancer was 1.28 (95% CI = 1.17 to 1.40) for T2D duration of 4.1-6.0 years, 1.37 (95% CI = 1.25 to 1.50) for 6.1-8.0 years, 1.21 (95% CI = 1.09 to 1.35) for 8.1-10.0 years, and 1.04 (95% CI = 0.95 to 1.14) after 15.0 years. In a cross-sectional analysis, a higher level of plasma C-peptide was found among participants with prevalent T2D of up to 8 years than those without T2D, whereas a higher level of HbA1c was found for those with prevalent T2D of up to 15 years. CONCLUSIONS Incident T2D was associated with higher cancer risk, which peaked at approximately 8 years after diabetes diagnosis. Similar duration-dependent pattern was observed for plasma C-peptide. Our findings support a role of hyperinsulinemia in cancer development.
Susceptibility to interference between Pavlovian and instrumental control is associated with early hazardous alcohol use

Pavlovian‐to‐instrumental transfer (PIT) tasks examine the influence of Pavlovian stimuli on ongoing instrumental behaviour. Previous studies reported associations between a strong PIT effect, high‐risk drinking and alcohol use disorder. This study investigated whether susceptibility to interference between Pavlovian and instrumental control is linked to risky alcohol use in a community sample of 18‐year‐old male adults. Participants (N = 191) were instructed to ‘collect good shells’ and ‘leave bad shells’ during the presentation of appetitive (monetary reward), aversive (monetary loss) or neutral Pavlovian stimuli. We compared instrumental error rates (ER) and functional magnetic resonance imaging (fMRI) brain responses between the congruent and incongruent conditions, as well as among high‐risk and low‐risk drinking groups. On average, individuals showed a substantial PIT effect, that is, increased ER when Pavlovian cues and instrumental stimuli were in conflict compared with congruent trials. Neural PIT correlates were found in the ventral striatum and the dorsomedial and lateral prefrontal cortices (lPFC). Importantly, high‐risk drinking was associated with a stronger behavioural PIT effect, a decreased lPFC response and an increased neural response in the ventral striatum on the trend level. Moreover, high‐risk drinkers showed weaker connectivity from the ventral striatum to the lPFC during incongruent trials. Our study links interference during PIT to drinking behaviour in healthy, young adults. High‐risk drinkers showed higher susceptibility to Pavlovian cues, especially when they conflicted with instrumental behaviour, indicating lower interference control abilities. Increased activity in the ventral striatum (bottom‐up), decreased lPFC response (top‐down), and their altered interplay may contribute to poor interference control in the high‐risk drinkers.
Etude de noyaux riches en neutrons dans la région du coeur doublement magique de ¹³²Sn

Le present manuscrit de these se propose d'etudier l'evolution de l'interaction nucleaire dans les noyaux riches en neutrons de la region du coeur doublement magique de 132Sn via les isotopes de 136Sb, 136Sn et de 138Sn. Le premier chapitre de ce manuscrit presente succinctement certains des isotopes de Sb et de Sn connus a ce jour au dela des fermetures de couches Z=50 et N=82, ainsi que certaines des problematiques rencontrees pour la description des noyaux de cette region par le modele en couches nucleaire. Le lecteur y trouvera en outre une breve presentation des outils indispensables a la comprehension de tels calculs. Les etats excites du 136Sb furent produits a l'Institut Laue-Langevin de Grenoble via le mecanisme de fission induite par neutrons thermiques sur une cible de 241Pu. Les fragments de fission ainsi produits furent separes par le spectrometre de masse Lohengrin, et une mesure des gammas et des electrons de conversions des fragments de 136Sb permirent la confirmation du precedent spectre experimental obtenu ainsi que la mesure d'une duree de vie de 31(7) ns pour l'etat de spin 4-. Ces elements sont presentes et discutes en premiere partie du manuscrit. Les isotopes de 136,138Sn, objets de la seconde partie, furent quant a eux produits par fission d'un faisceau de 238U sur une cible de 9Be au RIBF du RIKEN, a Tokyo. Les separateurs de masses BigRIPS et ZDS y permirent une separation et une identification des fragments de fission ainsi produits avant leur implantation dans le detecteur en silicium hautement segmente WAS3ABi, entoure par l'ensemble de detecteurs en germaniums EURICA. Une premiere mesure des energies des transitions 6+ -> 4+, 4+ -> 2+ et 2+ -> 0+ de chacun de ces deux isotopes fut alors realisee, ainsi que la mesure d'une duree de vie de 46(7) et 219(58) ns pour les etats de spins 6+ du 136Sn et du 138Sn respectivement.
Environmental Control of Isoprene Emission: from Leaf to Canopy Scale

Isoprene is the most abundant volatile organic compound (VOC) emitted from vegetation, mainly trees. Because it plays an important role in tropospheric chemistry leading to formation of pollutants and enhancing the lifetime of the greenhouse gas methane, concern about the response of isoprene emissions to the rise in atmospheric CO2 concentration and global climate change has been increasing over the last few years. The consequences of predicted climate change will have complex repercussions on global isoprene emission. The increasing atmospheric CO2 per se will have direct effects on terrestrial vegetation since CO2 is the substrate of photosynthesis. Because photosynthesis is limited by CO2 at current ambient concentrations, an increase in CO2 is expected to increase leaf biomass (i.e. isoprene emitting surface). Predicted warmer climate, extended drought periods, the possible shift in plant species in favour of isoprene emitters and the increase in length of growing season, may cause an increase in global isoprene emissions with profound perturbations of air quality and the global carbon cycle. The aim of this thesis was to investigate the effect of environmental variables such as light, temperature, drought and leaf-to-air vapour pressure deficit (VPD), and the shortand long-term effect of atmospheric [CO2] on isoprene emission from temperate and tropical tree species. Both leaf and whole ecosystem level fluxes were studied. At the leaf scale, a short-term experiment with leaves of potted two-year old trees of Quercus virginiana was carried out, exposing plants to two dryingrewatering cycles. Leaf isoprene emission fell, but the process was considerably less sensitive to water stress than photosynthesis and stomatal conductance. In drought conditions, the large reduction in photosynthesis caused the percentage of fixed carbon lost as isoprene to increase as plants became more stressed, reaching peaks of 50% when photosynthesis was almost zero. Isoprene emissions also showed a strong negative linear relationship with pre-dawn leaf water potential (Ψleaf). In another experiment carried out at the large enclosed facility of Biosphere 2 (B2L, Arizona, USA), studying isoprene emission from leaves of three-year-old plants of Populus deltoides grown at three CO2 atmospheric concentrations (430, 800 and 1200 μmol