Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of five methods to assess embolism resistance in trees

Vulnerability to drought-induced embolism is a key trait that shapes drought resistance and that could be increasingly used to design climate-smart forest management guidelines and to anticipate the outcome of climate change on populations dynamics and ecosystems functioning. A panel of methods is currently available to measure embolism resistance. This makes crucial a proper identification of which methods are the most accurate for determining this trait. However, the measurement of embolism resistance is sensitive to numerous artifacts that may lead to large errors for a given species. In addition, not all methods are easily accessible because of the cost of some large equipment and/or certain lab facilities. The emergence of the easy and low cost Pneumatic method allows to perform vulnerability curves at high throughput. However, only few studies have evaluated the reliability of this method compared to others. In this study, we proposed a comparison of five methods that allowed to assess embolism resistance in eleven tree species with contrasting xylem anatomy and vessels length (six short vessel angiosperms, two tracheid bearing conifers and three long-vessel angiosperms), covering a large part of the range of embolism resistance observed in trees. Consistent results were obtained among all the methods for short-vessel angiosperm species. In tracheid-bearing conifers, the Pneumatic method overestimated vulnerability to embolism. In long-vessel species, the Pneumatic method led to inconsistent results with accurate vulnerability to cavitation curves (VCs) for one species but led to r-shaped VCs with a underestimation of incipient embolism for the two other ones. The comparison of VC parameters with turgor loss point is proposed as an indicator of the validity of the VCs. The conditions of validity, the advantages and pitfalls of the five methods are discussed. Our results warned against the widespread usages of some methods before rigorous validation tests have been performed.
Adaptive sensor selection algorithms for wireless sensor networks

A wireless sensor network is a collection of tiny, autonomously powered devices commonly called sensor nodes endowed with sensing, communication, and processing capabilities. Once deployed over a region of interest, sensor nodes can collect ne-grained measurements of physical variables, like the temperature of a glacier or the concentration of a pollutant. To report their readings to one or more data sinks, sensor nodes communicate using their integrated radio-transceivers and build ad-hoc possibly multi-hop relay networks. Thanks to the potentially large number of nodes they are composed of and their ability to operate unattended for long periods of time, wireless sensor networks allow monitoring the environment at an unprecedented spatial and temporal scale. However, enabling a wireless sensor network to reliably report large quantities of data over long periods of time is still a challenging goal. In particular, since the operation of the radio is known to be the major factor of energy consumption on sensor nodes, limiting communication is crucial for increasing the lifetime of the network. On the other hand, meeting the requirements of wireless sensor network applications may require sensor nodes to collect and report large amounts of sensor readings. The e cient operation of a wireless sensor network thus requires careful scheduling of node participation in sensing and communication. Beyond the role that medium access control and routing protocols may play in this context, so-called sensor selection algorithms can provide for signi cant communication savings by identifying subsets of the deployed nodes that are su cient to comply with the application requirements. This thesis argues for endowing sensor selection algorithms with the ability to dynamically adapt to the observed data and to the local topology of the network. The presented work o ers novel sensor selection strategies that can continuously tune their parameters in a distributed fashion, thereby relying on no or only little a priori knowledge about the phenomena of interest. In particular, the thesis rst addresses the
Poverty in the United States

I ndiana has the third-lowest poverty rate in the nation, according to the U.S. Census Bureau (see Figure 1). On average, 8.3% of Indiana residents lived in poverty between 1997 and 1999. Nationally, poverty afflicts 12.6% of the population, or 34.4 million people. Imagine — 34.4 million equals the entire population of Indiana times six, and they all live in poverty. Yet current poverty rates are the lowest since the late 1970s, even when broken down by age or race and ethnicity. Unfortunately, lower poverty rates do not necessarily mean that fewer people are in poverty relative to the past. The rate is only a percentage of the total population, which has grown since the 1970s. For example, in 1980, 12.6% of the population would have equaled 28.5 million people compared to today's 34.4 million because the U.S. population has grown by 46.2 million since 1980. So while the proportion of the population in poverty may be the same, 5.9 million more people — approximately the population of Indiana — live in poverty today than in 1980 had the rate then been 12.6%. Looking closer to home, Indiana has the fewest number of people in poverty
2956 - BEHAVIOUR OF HIGH-STRENGTH CONCRETE AT HIGH LOADING RATES

Abstract Cracks in plain concrete run along the boundary layer of the concrete-aggregate grain. The aggregate grains are pulled out from the surrounding cement paste under friction. This friction, which shows a partial viscous character, leads to an additional force transfer in the composite material at high rates of loading. In contrast, in high-strength concrete under low rates of loading, the cracks run right through the aggregate grains, due to of the hardened cement paste and the improved bond between the cement paste and the aggregate. This leads to relatively smooth crack-surfaces and also to a small toughness of the high strength concrete. Additionally for a high strain rate, the viscose friction of the grains should not be present. Based on the described effects on cracking between the different types of concrete in this static fracture-mechanics, differences are also expected under high rates of loading. Investigations at the Technical University of Dresden observed these differences in the fracture behavior under high rates of loading. For this purpose a specially designed device was developed, which can transfer a short traction force of 70 kN in a few milliseconds to concrete specimens. Tests carried out show that high-strength concrete also experiences an increase in the tensile strength, but with a substantial smaller factor due to the missing viscose behavior. Research on the crack surface has shown a higher fragmentation of this area compared to surfaces of the quasi-static tests, so that the real crack surface increases. Due to this fact, the ultimate bearing force increases too, because it is dependant on the ultimate tension stress and the real surface.
Why machine learning (ML) has failed physical activity research and how we can improve

Measuring physical activity is a critical issue for our understanding of the health benefits of human movement. Machine learning (ML), using accelerometer data, has become a common way to measure physical activity. ML has failed physical activity measurement research in four important ways. First, as a field, physical activity researchers have not adopted and used principles from computer science. Benchmark datasets are common in computer science and allow the direct comparison of different ML approaches. Access to and development of benchmark datasets are critical components in advancing ML for physical activity. Second, the priority of methods development focused on ML has created blind spots in physical activity measurement. Methods, other than cut-point approaches, may be sufficient or superior to ML but these are not prioritised in our research. Third, while ML methods are common in published papers, their integration with software is rare. Physical activity researchers must continue developing and integrating ML methods into software to be fully adopted by applied researchers in the discipline. Finally, training continues to limit the uptake of ML in applied physical activity research. We must improve the development, integration and use of software that allows for ML methods’ broad training and application in the field.
Just Diverse Among Themselves: How Does Negative Performance Feedback Affect Boards’ Expertise vs. Ascriptive Diversity?

We investigate how negative performance feedback affects board diversity, which is instrumental in shaping a firm’s strategic change. When a firm underperforms compared with its aspiration, its board is motivated to promptly address the underperformance. The board needs to not only help search for strategic alternatives but also quickly build consensus around its strategic reorientation. These two motivations lead the board to value two dimensions of diversity among its members differently. On the one hand, to understand the problem of underperformance and find a solution, the board is motivated to seek new expertise, avoiding redundancy in the pool of expertise already represented in the boardroom. This results in a higher level of diversity in director expertise. On the other hand, the urgent need to build consensus prompts the board to value trust and solidarity and to avoid potential conflict among directors. Because people perceive others with similar ascriptive backgrounds as trustworthy, changes in the board of an underperforming firm are likely to yield a lower level of diversity in its members’ ascriptive backgrounds. These changes in board are affected by the committee chairs of the board whose power and influence are significant in the boardroom. Analyses of the boards of 733 U.S. listed manufacturing firms show that when a firm underperforms compared with its aspirations, it increases the board expertise diversity, but decreases the board ascriptive diversity. When chairs on the board are gender or racial minorities, the negative association between underperformance and the board ascriptive diversity is weakened.
ENTROPY WAVES, THE ZIG-ZAG GRAPH PRODUCT, AND NEW CONSTANT-DEGREE

The main contribution of this work is a new type of graph product, which we call the {\it zig-zag product}. Taking a product of a large graph with a small graph, the resulting graph inherits (roughly) its size from the large one, its degree from the small one, and its expansion properties from both! Iteration yields simple explicit constructions of constant-degree expanders of arbitrary size, starting from one constant-size expander. Crucial to our intuition (and simple analysis) of the properties of this graph product is the view of expanders as functions which act as ``entropy wave" propagators -- they transform probability distributions in which entropy is concentrated in one area to distributions where that concentration is dissipated. In these terms, the graph products affords the constructive interference of two such waves. Subsequent work [ALW01], [MW01] relates the zig-zag product of graphs to the standard semidirect product of groups, leading to new results and constructions on expanding Cayley graphs.
A P300-Based Speller Design Using a MINMAX Riemannian Geometry Scheme and Convolutional Neural Network

A P300-based speller BCI with large target symbols was designed to improve the detection accuracy and information transfer rate (ITR) as well as to overcome adjacency, crowding, and fatigue problems. The speller paradigm consists of a $8\times 7$ speller matrix which uses only 8 flashing characters for selection. Another critical issue in the design of the BCI speller is the feature extraction and classification algorithms. The symmetric positive definite (SPD) matrix of data has emerged as a compact and informative representation of brain signals conveying spatial information. A geometric way to analyze SPD matrices is through the Riemannian geometry (RG) which has been shown to overcome the limitations of Euclidean geometry and provides robustness with respect to outlier and possess good generalization abilities. However, for large data sets, when the number of channels is large, we encounter the curse of dimensionality problem in computing the covariance matrix and constructing the Riemannian graph. To overcome this problem and be able to construct a low-dimensional SPD, we propose a hybrid method based on a convolutional neural network and RG. A mapping that relies on the RG, is proposed to extract the maximum discriminative features. The experimental results from 6 subjects showed that the proposed BCI speller achieved promising accuracy (ITR) of 95±3.6% (26.73±1.8 bits/min) in offline analyses and 94.3±3.8% (54.6±3.8 bits/min) in online experiments. Additionally, the proposed feature extraction method was applied to the BCI competition datasets and compared with most of the state-of-the-art algorithms. The results demonstrated that the accuracy (ITR) of the proposed method was 100% (8.9 bits/min) and 97% (8.5 bits/min) on dataset II from BCI-competition II and dataset II from BCI-competition III, respectively.
THE LIMITING DISTRIBUTION OF FUNCTIONS OF SAMPLE MEANS AND APPLICATION TO TESTING HYPOTHESES

In 1935 J. L. Doob published a paper [21 in which he derived the limiting distribution of a function of four sample means from one homogeneous sample. This work is susceptible to an easy generalization and supplies a powerful weapon with which to find the limiting distribution of a vast number of statistics. But since publication its importance seems to have been overlooked. A generalization of Doob's theorem to any number of sample means was given by the author [71. In the first part of this paper two theorems are proved which embody a further generalization of Doob's result to the case of several samples of different sizes, and numerous examples are given to illustrate their wide applicability. These examples are confined to the limiting distributions of given statistics, but in the second part a much more important constructive application is made. Two hypotheses of a general character, concerning one sample and several samples respectively, are formulated, and a systematic method of constructing a test function for each hypothesis included in the two general ones is given. The construction is done in such a manner that, as a consequence of the results obtained in the first part, (i) the test function has for its limiting distribution the x2 distribution with a known degree of freedom when the hypothesis tested is true, and (ii) the power of the test tends in general to unity as its limit. Special hypotheses and their large sample tests are treated as examples in the second part of the paper.
Association Between Psychological Flexibility and Eating Disorder Symptoms Among College Students

Objective: Research suggests an association between emotion regulation difficulties and eating disorders. Less is known about the association between psychological flexibility (a related construct) and eating disorders, particularly among college students. Therefore, this study aimed to estimate the association between psychological flexibility and eating disorder symptoms among a national sample of U.S. college students. Method: Participants in the 2018–2019 Healthy Minds Study (N = 25,086) completed the Acceptance and Action Questionnaire-II (AAQ-II) and the SCOFF questionnaire. I conducted unadjusted and adjusted analyses to determine the association between AAQ score and a positive eating disorder screen (SCOFF). Results: Analyses revealed significant associations between psychological flexibility and a positive eating disorder screen. Participants who screened positive for an eating disorder had higher mean scores on the AAQ (p < .001), indicating greater psychological inflexibility compared to those who screened negative. Logistic regression analysis revealed that greater psychological inflexibility was significantly associated with higher odds of a positive eating disorder screen (adjusted OR 1.05, 95% confidence interval [1.04, 1.05]), while adjusting for covariates. Conclusions: Social workers and eating disorder prevention efforts can incorporate specific interventions to help college students develop greater psychological flexibility, which may reduce eating disorder symptoms.
Synthesis and Characterization of ZnO doped by Al via sol-gel method

Preparation of ZnO doped with Al using ethylene glycol as a media at room temperature, nano crystals was obtained stable up to 700 o C highly crystalals with 17A o and 12A o in length TEM(transmitted microscope) ,the prolongation of absorption band is between (367-800)nm with energy gap =4.448 EV.The Characterization with Uv(Ultra violet ),TEM(Transmission Electron Microscope), SEM(Scanning Electrons Microscope) and XRD(X-Ray Diffractions) were showed a good uniform crystals. ةصلاخلا ٌٍوكرو خفشغنا حساشح خجسذث ظسوك لوكٌلاك ٍٍهثا واذخزساخطساوث ووًٍُنلابث ىعذًنا كَزنا ذٍسكوا شٍضحر ثحجنا ازه ًف ىر يوَبَ تكشي ىنا مصر ٍٍخسر حساشح دبجسد ًف ذثبث 700 . يوئي ةوكسشكًناو سكا خعشا خطساوث ضٍخشزنا للاخ ٍيو زًٌ تكشًنا ٌا ٍٍجر زفبُنا ًَوشزكنلاا يوزحٌو خٍنبع خٌسوهث كه ٍي ٍٍعوَ ىهع دار داسوهجنا لاوطا 71 و ووشزسهكا 71 لا خنبطزسا كهزياو ووشزسهكَا ي ٍي ذزًر ًئوض صبصز 088 160 شزيوَبَ . ِساذقي ًقبط عبفرسا ورو . 0 . ذنوف
The Role of C-Reactive Protein and Fibrinogen in the Development of Intracerebral Hemorrhage: A Mendelian Randomization Study in European Population

Background: The causal association of C-reactive protein (CRP) and fibrinogen on intracerebral hemorrhage (ICH) remains uncertain. We investigated the causal associations of CRP and fibrinogen with ICH using two-sample Mendelian randomization. Method: We used single-nucleotide polymorphisms associated with CRP and fibrinogen as instrumental variables. The summary data on ICH were obtained from the International Stroke Genetics Consortium (1,545 cases and 1,481 controls). Two-sample Mendelian randomization estimates were performed to assess with inverse-variance weighted and sensitive analyses methods including the weighted median, the penalized weighted median, pleiotropy residual sum and outlier (MR-PRESSO) approaches. MR-Egger regression was used to explore the pleiotropy. Results: The MR analyses indicated that genetically predicted CRP concentration was not associated with ICH, with an odds ratio (OR) of 1.263 (95% CI = 0.935–1.704, p = 0.127). Besides, genetically predicted fibrinogen concentration was not associated with an increased risk of ICH, with an OR of 0.879 (95% CI = 0.060–18.281; p = 0.933). No evidence of pleiotropic bias was detected by MR-Egger. The findings were overall robust in sensitivity analyses. Conclusions: Our findings did not support that CRP and fibrinogen are causally associated with the risk of ICH.
Natural history of TFR2‐related hereditary hemochromatosis in a 47‐yr‐old Italian patient

To the Editor: Hereditary haemochromatosis (HH) refers to a heterogeneous group of disorders characterized by increased iron absorption and augmented release from the reticuloendothelial system (1). In North Europe, more than 80% HH cases harbor mutations in the HFE gene (HH type I or classic HH), while a growing number of patients have been found carrying mutations in rarer genes, including HJV (HH type IIA), HAMP (HH type IIB), TFR2 (HH type III) and SLC40A1 (HH type IV) (2). Among them, mutations in TFR2 are reported in 21 apparently unrelated cases only, 14 of whom of Italian origin (3–6). Based on the very limited available data, HH type III is considered a milder disorder than HH type II (or juvenile HH), although it is usually more severe than HH type I (7). Here, we report a 47-yr-old Italian patient born to non-consanguineous parents. The proband, who is now 51 yr of age, began to complain of fatigue, erectile dysfunction, somnolence and frequent headaches at 31 yr of age. Two years later, insulin-dependent diabetes mellitus and hypogonadotropic hypogonadism were diagnosed. Shortly after, blood tests revealed high levels of liver enzymes. At 35 yr of age, the patient started to suffer from generalized arthralgia, which initially involved left shoulder, hands, feet and right knee and subsequently progressed, mainly affecting metacarpo-phalengeal joints and causing severe pain, deformation of hands and functional impairment in multiple sites. At age 37, radiographic evaluation showed characteristic degenerative changes (Fig. 1). At the age of 47 yr, the patient was evaluated by an endocrinologist, who found transferrin saturation (TS) 97% and ferritin 2982 ng ⁄mL, suggestive of HH. After that, the patient was referred to our dedicated outpatient service. Abdominal ultrasound revealed hepatomegaly. As the patient denied liver biopsy, he was investigated by magnetic resonance imaging, that revealed severe hepatic iron overload with a hepatic iron content >300 lmol ⁄g, according to Gandon et al. (8). Main biochemical data of the patient are summarized in Table 1. The patient was treated by phlebotomy for 1 yr with success (the estimated quantity of iron removed was about 18 g). Liver enzymes levels normalized and hepatomegaly recovered. Despite the achievement of subnormal serum ferritin levels, arthropathy continued to progress and left hip prosthesis replacement was requested at 50 yr of age. After obtaining informed consent, genomic DNA was extracted from peripheral blood leukocytes. In the patient, analysis of 17 HH-associated mutations was first performed as previously described (9) and identified the common heterozygous stop mutation p.E60X. Then, TFR2 (Genbank no. NC_000007.12) was directly A
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling

On-policy reinforcement learning (RL) algorithms perform policy updates using i.i. d. trajectories collected by the current policy. However, after observing only a finite number of trajectories, on-policy sampling may produce data that fails to match the expected on-policy data distribution. This sampling error leads to noisy updates and data inefficient on-policy learning. Recent work in the policy evaluation setting has shown that non-i.i. d. , off-policy sampling can produce data with lower sampling error than on-policy sampling can produce. Motivated by this observation, we introduce an adaptive, off-policy sampling method to improve the data efficiency of on-policy policy gradient algorithms. Our method, Proximal Robust On-Policy Sampling (PROPS), reduces sampling error by collecting data with a behavior policy that increases the probability of sampling actions that are under-sampled with respect to the current policy. Rather than discarding data from old policies -- as is commonly done in on-policy algorithms -- PROPS uses data collection to adjust the distribution of previously collected data to be approximately on-policy. We empirically evaluate PROPS on both continuous-action MuJoCo benchmark tasks as well as discrete-action tasks and demonstrate that (1) PROPS decreases sampling error throughout training and (2) improves the data efficiency of on-policy policy gradient algorithms. Our work improves the RL community's understanding of a nuance in the on-policy vs off-policy dichotomy: on-policy learning requires on-policy data, not on-policy sampling.