Miracle-dz/UniFAR-FTUs huggingface dataset:
Image compression via joint statistical characterization in the wavelet domain

We develop a probability model for natural images, based on empirical observation of their statistics in the wavelet transform domain. Pairs of wavelet coefficients, corresponding to basis functions at adjacent spatial locations, orientations, and scales, are found to be non-Gaussian in both their marginal and joint statistical properties. Specifically, their marginals are heavy-tailed, and although they are typically decorrelated, their magnitudes are highly correlated. We propose a Markov model that explains these dependencies using a linear predictor for magnitude coupled with both multiplicative and additive uncertainties, and show that it accounts for the statistics of a wide variety of images including photographic images, graphical images, and medical images. In order to directly demonstrate the power of the model, we construct an image coder called EPWIC (embedded predictive wavelet image coder), in which subband coefficients are encoded one bitplane at a time using a nonadaptive arithmetic encoder that utilizes conditional probabilities calculated from the model. Bitplanes are ordered using a greedy algorithm that considers the MSE reduction per encoded bit. The decoder uses the statistical model to predict coefficient values based on the bits it has received. Despite the simplicity of the model, the rate-distortion performance of the coder is roughly comparable to the best image coders in the literature.
Episodic memory as a prerequisite for online updates of model structure

Human learning in complex environments critically depends on the ability to perform model selection, that is to assess competing hypotheses about the structure of the environment. Importantly, information is accumulated continuously, which necessitates an online process for model selection. While model selection in human learning has been explored extensively, it is unclear how memory systems support learning in an online setting. We formulate a semantic learner and demonstrate that online learning on open model spaces results in a delicate choice between either tracking a possibly infinite number of competing models or retaining experiences in an intact form. Since none of these choices is feasible for a bounded-resource memory system, we propose an episodic learner that retains an optimised subset of experiences in addition to semantic memory. On a simple model system we demonstrate that this normative theory of episodic memory can effectively circumvent the challenge of online model selection.
Electron-phonon interaction in suspended highly doped silicon nanowires

We have realized highly doped suspended silicon nanowires with lateral dimensions down to 20 nm for studying electron transport and dissipation phenomena in these wires. Random dopant fluctuations lead to the formation of multiple tunnel junctions, showing Coulomb blockade phenomena at low drain–source bias. In the finite-bias regime we observe relaxation of hot electrons via phonons. Melting of the wires then occurs at high bias values at an extremely large current density of the order of 10 6 Ac m −2 .
Enzymatic preparation of biosurfactants from sugars or sugar alcohols and fatty acids in organic media under reduced pressure

Biosurfactants were prepared by enzymatic esterification of sugars and sugar alcohols in nonaqueous media. Sorbitol monooleate was produced in pure molten substrates, with reduced pressure to remove water. The results were compared to synthesis in organic solvent, with and without water removal. Synthesis in organic solvent with water removal, obtained by refluxing through a desiccant under reduced pressure, proved to be the most efficient method in terms of total yield and side‐products formation. This process was applied to the production of different surfactants, by changing the nature of the hydrophilic and hydrophobic moieties. Yields above 90% of monoesters were obtained after 24 h when the reaction was carried out in 2‐methyl‐2‐butanol with Novozym 435 (Type B lipase from Candida antarctica) with an excess of hydroxyl donor. © 1995 John Wiley & Sons, Inc.
Structural and Functional Elucidation of Peptide Ts11 Shows Evidence of a Novel Subfamily of Scorpion Venom Toxins

To date, several families of peptide toxins specifically interacting with ion channels in scorpion venom have been described. One of these families comprise peptide toxins (called KTxs), known to modulate potassium channels. Thus far, 202 KTxs have been reported, belonging to several subfamilies of KTxs (called α, β, γ, κ, δ, and λ-KTxs). Here we report on a previously described orphan toxin from Tityus serrulatus venom, named Ts11. We carried out an in-depth structure-function analysis combining 3D structure elucidation of Ts11 and electrophysiological characterization of the toxin. The Ts11 structure is highlighted by an Inhibitor Cystine Knot (ICK) type scaffold, completely devoid of the classical secondary structure elements (α-helix and/or β-strand). This has, to the best of our knowledge, never been described before for scorpion toxins and therefore represents a novel, 6th type of structural fold for these scorpion peptides. On the basis of their preferred interaction with voltage-gated K channels, as compared to all the other targets tested, it can be postulated that Ts11 is the first member of a new subfamily, designated as ε-KTx.
Merlin: specification inference for explicit information flow problems

The last several years have seen a proliferation of static and runtime analysis tools for finding security violations that are caused by explicit information flow in programs. Much of this interest has been caused by the increase in the number of vulnerabilities such as cross-site scripting and SQL injection. In fact, these explicit information flow vulnerabilities commonly found in Web applications now outnumber vulnerabilities such as buffer overruns common in type-unsafe languages such as C and C++. Tools checking for these vulnerabilities require a specification to operate. In most cases the task of providing such a specification is delegated to the user. Moreover, the efficacy of these tools is only as good as the specification. Unfortunately, writing a comprehensive specification presents a major challenge: parts of the specification are easy to miss, leading to missed vulnerabilities; similarly, incorrect specifications may lead to false positives. This paper proposes Merlin, a new approach for automatically inferring explicit information flow specifications from program code. Such specifications greatly reduce manual labor, and enhance the quality of results, while using tools that check for security violations caused by explicit information flow. Beginning with a data propagation graph, which represents interprocedural flow of information in the program, Merlin aims to automatically infer an information flow specification. Merlin models information flow paths in the propagation graph using probabilistic constraints. A naive modeling requires an exponential number of constraints, one per path in the propagation graph. For scalability, we approximate these path constraints using constraints on chosen triples of nodes, resulting in a cubic number of constraints. We characterize this approximation as a probabilistic abstraction, using the theory of probabilistic refinement developed by McIver and Morgan. We solve the resulting system of probabilistic constraints using factor graphs, which are a well-known structure for performing probabilistic inference. We experimentally validate the Merlin approach by applying it to 10 large business-critical Web applications that have been analyzed with CAT.NET, a state-of-the-art static analysis tool for . NET. We find a total of 167 new confirmed specifications, which result in a total of 322 additional vulnerabilities across the 10 benchmarks. More accurate specifications also reduce the false positive rate: in our experiments, Merlin-inferred specifications result in 13 false positives being removed; this constitutes a 15% reduction in the CAT.NET false positive rate on these 10 programs. The final false positive rate for CAT.NET after applying Merlin in our experiments drops to under 1%.
EVALUATION OF POROUS HOTMIX ASPHALT PERFORMANCE UNDER ACCELERATED LOADING

The purpose of the study is to evaluate the performance of porous hot mix asphalt under accelerated loading. The parameters measured were stresses and deformation (rutting) occurred in the test section. To achieve the objective, a test section was constructed and tested with a loading device, simulating truck loading. Subbase and base layers were constructed using similar materials. The variable is on the surface layers, which consist of two conventional and two porous hotmix asphalt. The pavement material conforms to the Indonesian Directorate General of Highway for the conventional HMA and Australian standard for Porous HMA. The thickness of the test section was prepared to simulate the real condition of the pavement structure. The simulated load applied to the pavement test section was equivalent to the 9,600 kg, which was more than the standard 8,160 kg axle load, and applied for more than 1,500 passes. For every 100 passes, stresses and deformation were recorded. The result shows porous HMA relatively has a comparable performance when compared to the conventional one. Hence, the application of porous HMA needs to be pushed particularly in the area where rain intensity is high.
Multi‐class Classification with Dependent Gaussian Processes

We present a novel multi-output Gaussian process model for multi-class classification. We build on the formulation of Gaussian processes via convolution of white Gaussian noise processes with a parameterized kernel and present a new class of multi-output covariance functions. The latter allow for greater flexibility in modelling relationships between outputs while being parsimonious with regard to the number of model parameters. We apply the model to multi-class Gaussian process classification using a sparse approximation based on the informative vector framework and investigate, both analytically as well as empirically, a scenario where our multi-class classifier performs better than combining independently trained binary classifiers.
Evaluation of a Cascade Hybrid Recommendation as a Combination of One-Class Classification and Collaborative Filtering

This paper decomposes the problem of recommendation into a two level cascade recommendation scheme which benefits from both content-based and collaborative filtering methodologies. The first level utilizes the content-based features of items in order to incorporate the individualized (subjective) user preferences within the recommendation process. This is achieved through the exploitation of the one-class classification paradigm which provides the means in order to filter out user specific undesirable items. The second level, on the other hand, serves the purpose of assigning particular rating degrees to the user-specific desirable items identified by the first level. The combination of two approaches in a cascade form, mimics the social process when someone has selected some items according to his preferences and asks for opinions about these by others, in order to achieve the best selection. Our experimentation provides significant evidence on the recommendation efficiency of the adapted hybrid approach which outperforms pure content-based and pure collaborative techniques.
Evaluating workforce developments to support children of mentally ill parents: implementing new interventions in the adult mental healthcare in Northern Norway

Background According to new Norwegian laws, mental healthcare for adults are obligated to assess all patients who are parents and to act on their children's needs. This article describes the study protocol of implementing the interventions Family Assessment and Child Talks for children of patients in the adult psychiatry of the University Hospital of Northern Norway. The project is designed to evaluate the process of changes in clinical practice due to the implementation of two interventions. The interventions to be implemented are a standardised Family Assessment Form and the intervention called Child Talks. The family assessment form is an intervention to identify children of mentally ill parents and their needs. The intervention Child Talks is a health-promoting and preventive intervention where the mental health workers talk with the family about the situation of the children and their needs. Methods/design There are two groups of participants in this study: (1) mental health workers in the clinic (N=220) and (2) patients who are parents (N=200) receiving treatment in the clinic. (1) In the evaluation of clinical practice, the authors use a pre-test, post-test and 1-year follow-up design. At pre-test, the authors evaluate status quo among mental health workers in the clinic regarding knowledge, attitudes, collaborative routines and clinical practice related to families with parental mental illness. After the pre-test is finished, the project move on to implement the interventions Family Assessment Form and Child Talks in the clinic. At post-test and 1-year follow-up, the authors evaluate the impact of implementing the Family Assessment Form in terms of how many children were identified and offered Child Talks in the clinic or referred to other services for additional support. (2) In the evaluation of parents/patients experience with the interventions, the authors use a pre-test post-test design. To identify children of mentally ill patients, the authors collect data on demographical variables for the patient and the child at pre-measures, as well as data on parental competence (PSOC) and parental concerns (PEDS) about their children. At post-measures, the authors evaluate the impact of the intervention in terms of user satisfaction, as well as changes between pre- and post-measures on parental competence (PSOC) and parental concerns (PEDS) about their children. Discussion The implication of implementing new interventions to safeguard children of mentally ill patients and the limitation of not measuring child development directly are discussed.
Loss of Dab2 Expression in Breast Cancer Cells Impairs Their Ability to Deplete TGF-β and Induce Tregs Development via TGF-β

Dab2 is a multifunctional adapter protein which is frequently under-expressed in a variety of cancers. It is implicated in many critical functions, including several signaling pathways, cell arrangement, differentiation of stem cells, and receptor endocytosis. Transforming growth factor-β (TGF-β) is a secreted multifunctional protein that controls several developmental processes and pathogenesis of many diseases. It has been documented that Dab2 played an important role in TGF-β receptors endocytosis. Here, we present evidence that re-expression of Dab2 in SK-BR-3 cell partially restored its ability to deplete TGF-β in surrounding medium by normalizing the trafficking of TGF-β receptors. We also demonstrate that the difference in TGF-β depletions produced by Dab2 expression was sufficient to impact on the conversion of naive CD4+ T cells to regulatory T cells (Tregs), and thus inhibited the proliferation of T cells. This work revealed a critical result that breast cancer cell was deficient in Dab2 expression and related receptor endocytosis-mediated TGF-β depletion, which may contribute to the accumulation of TGF-β in tumor microenvironment and the induction of immune tolerance.
Collaborative learning of preference rankings

We propose a model for learning user preference rankings for the purpose of making product recommendations. The model allows us to learn from pairwise preference statements or from (incomplete) rankings over more than two items. We present two algorithms for performing inference in this model, both with excellent scaling in the number of users and items. The superior predictive performance of the new method is demonstrated on the well-known sushi preference data set. In addition, we show how the model can be used effectively in an active learning setting where we select only a small number of informative items for learning.
MYC Amplification as a Prognostic Marker of Early-Stage Lung Adenocarcinoma Identified by Whole Genome Copy Number Analysis

Purpose: Even in small-sized (≤2 cm in greatest dimension) and/or pathologic stage I lung adenocarcinoma (ADC), a considerable proportion of the patients will relapse within 5 years and show poor prognosis. The purpose of this study was to identify genetic alterations that define prognosis of patients with early-stage lung ADC. Experimental Design: Regions of copy number alterations in 65 small-sized lung ADCs and 40 ADC cell lines were determined by using GeneChip Human Mapping 10-K and 250-K single-nucleotide polymorphism (SNP) arrays, respectively. A copy number assay based on real-time genomic PCR (RT-G-PCR) was done for 60 small-sized lung ADCs and 162 stage I lung ADCs. Results: Several regions on chromosomes 5p, 7p, 8q, and 14q were frequently (>10%) amplified in both small-sized ADCs and lung ADC cell lines. In particular, the MYC gene was mapped in the minimum common region at chromosome 8q24.21, and therefore was indicated to be a target of gene amplification in lung ADCs. MYC amplification correlated with poor prognosis (P = 0.031) of patients with small-sized ADCs. MYC amplification detected by SNP array analysis was well reproduced by RT-G-PCR analysis. Therefore, to investigate the utility of MYC amplification as a prognostic marker for early-stage lung ADCs, 162 stage I lung ADCs were subjected to the analysis. MYC amplification was associated with relapse-free survival in these patients (P = 0.013 by multivariate Cox proportional hazard model analysis). Conclusions: These results strongly indicate that MYC amplification is a prognostic marker of patients with early-stage lung ADCs. Clin Cancer Res; 17(6); 1481–9. ©2010 AACR.
Association of Hypertension and Hypokalemia with Cushing's Syndrome Caused by Ectopic ACTH Secretion

Abstract: Cushing's syndrome is associated with hypertension in approximately 80% of cases. Hypertension contributes to the marked increased mortality risk of past or current Cushing's syndrome, largely because of increased cardiovascular risk. Observation of the pathophysiological effect of chronically elevated ACTH and cortisol values in patients with ectopic ACTH secretion complements the available data from acute studies of the effects of ACTH and glucocorticoid infusions in normal volunteers. In a retrospective case review, we identified 58 patients with Cushing's syndrome caused by ectopic ACTH secretion, who were treated at the National Institutes of Health between 1983‐1997. The diagnosis of an ectopic ACTH cause was confirmed by inferior petrosal sinus sampling and/or pathologic examination of tumor. The commonest causes were bronchial carcinoid (40%) and thymic carcinoid (10%), but 18 of 58 (31%) patients had an unknown source of ectopic ACTH. Hypertension (systolic blood pressure >140 mmHg and/or diastolic blood pressure >90 mmHg in adults) was noted in 45 of 58 (78%) ectopic Cushing's patients, a prevalence similar to that noted in other endogenous Cushing's syndrome etiologies. Hypertension was severe, deemed to require 3 or more drugs by the treating physicians, in 26 of 58 (45%) patients. Hypokalemia was much more prevalent than in patients with other causes of Cushing's syndrome, affecting 33 of 58 (57%) patients. The range of plasma ACTH (17‐1557 pg/mL, normal <60) and 24‐hour urine cortisol (UC) excretion (192‐1600 mcg/24 hr, normal <90) allowed analysis of the influence of these hormones on blood pressure and plasma potassium. There was a significant relationship between 24‐hour UC excretion and the presence of hypokalemia (P= 0.003). Eight of nine patients with a UC >6000 mcg/24 hr had hypokalemia. There was no relation between ACTH level and hypokalemia. In addition, we did not find blood pressure severity to be related to UC excretion or ACTH levels. Urine and plasma cortisol and cortisol metabolite measurements suggest that cortisol may act as a mineralocorticoid when in excess, perhaps by saturating the 11β‐hydroxysteroid‐dehydrogenase (11β‐HSD2 enzyme) that inactivates cortisol at the renal tubule. The current data suggest that high cortisol levels may be the principal cause of hypokalemic alkalosis in Cushing's syndrome, rather than inhibition of the 11βHSD2 enzyme by ACTH or the effects of adrenal steroid biosynthetic intermediaries with mineralococorticoid activity.
Effect of Widespread Agricultural Chemical Use on Butterfly Diversity across Turkish Provinces

Although agricultural intensification is thought to pose a significant threat to species, little is known about its role in driving biodiversity loss at regional scales. I assessed the effects of a major component of agricultural intensification, agricultural chemical use, and land‐cover and climatic variables on butterfly diversity across 81 provinces in Turkey, where agriculture is practiced extensively but with varying degrees of intensity. I determined butterfly species presence in each province from data on known butterfly distributions and calculated agricultural chemical use as the proportion of agricultural households that use chemical fertilizers and pesticides. I used constrained correspondence analyses and regression‐based multimodel inference to determine the effect of environmental variables on species composition and richness, respectively. The variation in butterfly species composition across the provinces was largely explained (78%) by the combination of agricultural chemical use, particularly pesticides, and climatic and land‐cover variables. Although overall butterfly richness was primarily explained by climatic and land‐cover variables, such as the area of natural vegetation cover, threatened butterfly richness and the relative number of threatened butterfly species decreased substantially as the proportion of agricultural households using pesticides increased. These findings suggest that widespread use of agricultural chemicals, or other components of agricultural intensification that may be collinear with pesticide use, pose an imminent threat to the biodiversity of Turkey. Accordingly, policies that mitigate agricultural intensification and promote low‐input farming practices are crucial for protecting threatened species from extinction in rapidly industrializing nations such as Turkey.
Precipitation and plasticity couplings in a 7xxx aluminium alloy: application to thermomechanical treatments for distortion correction of aerospace component

7xxx aluminium alloys are high strength alloys mainly used for aerospace applications due to their high specific mechanical properties. Manufacturing these age-hardenable alloys involves complex thermomechanical treatments that develop the precipitation microstructure responsible for the mechanical properties. Undesirable long-range internal stresses are also produced during these treatments, and may induce severe distortions in the final products. Different thermomechanical processes: shot peening, laser forming and age-forming (respectively a purely mechanical, a purely thermal and a thermomechanical treatment) are used to reduce internal stresses and/or part distortion in aerospace aluminium 7xxx machined components. However, they modify the material's microstructure and consequently the mechanical properties. We have examined the couplings between these thermomechanical treatments, the microstructure and the mechanical properties, throughout experimental characterization and physically based modelling. The effect of precipitation on the strain hardening properties is investigated, to establish a physically-based strain hardening model able to be used in a shot peening finite element model. Then, the effect of laser forming on the precipitation state and on the material strength is examined. The last part is dedicated to the study of the effect of age forming – a process combining simultaneous plastic deformation and heat treatment - on precipitation.