Miracle-dz/UniFAR-FTUs huggingface dataset:
Distance software: design and analysis of distance sampling surveys for estimating population size

Summary 1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built‐in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple‐covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state‐of‐the‐art software that implements these methods is described that makes the methods accessible to practising ecologists.
Inhibition of angiogenic differentiation of human umbilical vein endothelial cells by curcumin.

Angiogenesis is a crucial step in the growth and metastasis of cancers. Curcumin inhibits tumor initiation and growth. We analyzed the effect of curcumin on endothelial cell migration, attachment, and tube formation on Matrigel. Curcumin had no effect on endothelial cell migration or attachment to either plastic or Matrigel. Curcumin treatment resulted in a dose-dependent inhibition of tube formation when the cells were treated before plating or at the time of plating on Matrigel. Curcumin treatment also caused the preformed tubes to break down. Curcumin inhibited angiogenesis in a s.c. Matrigel plug model in mice. The role of metalloproteinases has been shown to be important in angiogenesis; therefore, zymography was performed to determine whether curcumin affected protease activity. Zymographs of curcumin-treated culture supernatants showed a decrease in the gelatinolytic activities of secreted 53- and 72-kDa metalloproteinases. Western and Northern analysis showed a dose-dependent decrease in the protein expression and transcript of 72 kDa, indicating that curcumin may be exerting its inhibitory effect at both the transcriptional and posttranscriptional level. These findings suggest that curcumin acts as an angiogenesis inhibitor by modulating protease activity during endothelial morphogenesis. Curcumin could be developed as an antiangiogenic drug.
Greed: Gut Feelings, Growth, and History

List of Figures and Captions. Acknowledgements. 1. Introduction. 2. What Do We mean By Greed? Part I: Denials and Apologies:. 3. Beastly Passions and Legitimate Desires. 4. Discipling Greed. 5. Scholars and Idiots. Part II: How Greed Grows:. 6. Feeling and Meaning. 7. Growth and History. Part III: Three Bio--Histories:. 8. The Gluttonous Peasant. 9. The Avaricious Pensioner. 10. The Venal Professional. 11. Corporate Greed. References. Tailpiece.
A Bayesian Network for Outbreak Detection and Prediction

Health care officials are increasingly concerned with knowing early whether an outbreak of a particular disease is unfolding. We often have daily counts of some variable that are indicative of the number of individuals in a given community becoming sick each day with a particular disease. By monitoring these daily counts we can possibly detect an outbreak in an early stage. A number of classical time-series methods have been applied to outbreak detection based on monitoring daily counts of some variables. These classical methods only give us an alert as to whether there may be an outbreak. They do not predict properties of the outbreak such as its size, duration, and how far we are into the outbreak. Knowing the probable values of these variables can help guide us to a cost-effective decision that maximizes expected utility. Bayesian networks have become one of the most prominent architectures for reasoning under uncertainty in artificial intelligence. We present an intelligent system, implemented using a Bayesian network, which not only detects an outbreak, but predicts its size and duration, and estimates how far we are into the outbreak. We show results of investigating the performance of the system using simulated outbreaks based on real outbreak data. These results indicate that the system shows promise of being able to predict properties of an outbreak.
Modification of lifestyle and nutrition interventions for management of early chronic kidney disease

Dietary modification Protein a. We suggest that patients with progressive chronic kidney disease (CKD) have individualised diet intervention involving an appropriately qualified dietitian (2C). b. We recommend adults with early CKD consume a normal protein diet, consisting of 0.75 1.0 g/kg/day, with adequate energy. This is in line with the Recommended Daily Intake (RDI) for the general population (1C). c. A low protein diet (0.6 g/kg/day) to slow down CKD progression is not recommended due to the risk of malnutrition (1C). d. We suggest people with excess protein intakes reduce their intakes to the RDI levels as a high protein diet may accelerate renal function decline in mild renal insufficiency (2C). Salt e. We recommend that early CKD patients restrict their dietary sodium intake to 100 mmol/day (or 2.3 g sodium or 6 g salt per day) or less, as it reduces blood pressure and albuminuria in patients with CKD (1C). f. We recommend that patients with CKD should not use salt substitutes that contain high amounts of potassium salts (1D). Phosphate g. We suggest that early CKD patients (stages 1-3) should not restrict dietary phosphate intake, as restriction of dietary phosphate does not influence renal or cardiovascular outcomes in these patients (2C). Potassium h. We suggest that early CKD patients with persistent hyperkalaemia restrict their dietary potassium intake with the assistance of an appropriately qualified dietitian (2D). Polyphenol-enriched diets i. We suggest that in early CKD patients with diabetic nephropathy, consumption of a carbohydrate-restricted, low-iron-available, polyphenol-enriched (CR-LIPE) diet may slow the progression of diabetic nephropathy (2C). Caloric restriction j. We recommend that overweight/obese patients with CKD should be prescribed caloric restriction under the management of an appropriately qualified dietitian. A reduction in weight can mean improvement of CKD (1C). k. We suggest that, in the absence of specific recommendations for CKD, overweight or obese patients are encouraged to lose body fatness to aim for a Body Mass Index (BMI) closer to 18.5 – 24.9 kg/m 2 and waist circumference ≤102 cm for men and ≤88 cm for women (2C).  This is in line with the Dietary Guidelines for Australian Adults recommended by the NHMRC and Australian better health initiatives. Fruit and vegetables l. We suggest adults with early CKD consume a balanced diet rich in fruits and vegetables, as these appear to reduce blood pressure and have renoprotective effects comparable to sodium bicarbonate (2C).
Capital as Organic Unity: The Role of Hegel’s Science of Logic in Marx’s Grundrisse

Preface. List of Abbreviations. 1. Introduction. 2. Money as a measure, monetary crises and the doctrine of being. 3. The essence of capital. 4. Capital as it proceeds from itself. 5. The Begriff of capital. 6. The objectivity of fixed capital. 7. Conclusion. Select Bibliography. Index.
Issues in Pharmacokinetics and Pharmacodynamics of Anti-Infective Agents: Kill Curves versus MIC

The success of antimicrobial therapy is determined by complex interactions between an administered drug, a host, and an infecting agent. In a clinical situation, the complexity of these interactions is usually reflected by a high variability in the dose-response relationship. Therefore, to minimize the dose-response variability, key characteristics of the drug, the infecting agent and the host have to be taken into account for selecting an appropriate antibiotic and an appropriate dose. Failure to do so may result in either therapeutic failure or emergence of resistant strains. To date, dose and drug selection is mostly based on a static in vitro parameter, the MIC and on the drug′s serum concentration as a pharmacokinetic parameter. In practice, however, a pharmacodynamic effect in vivo is rather the result of a dynamic exposure of the infective agent to the unbound antibiotic drug fraction at the relevant effect site. Thus, static conditions in an in vitro setting hardly reflect a dynamic situation in a target organ under in vivo conditions. Furthermore, serum concentrations do not reflect the unbound concentrations at the target site. In recent years substantial efforts were devoted to systematically elucidate the dynamic relationship between pharmacokinetic and pharmacodynamic variables. The main concept of this pharmacokinetic-pharmacodynamic approach is to use the concentration-effect relationship of the drug of interest in dosage adjustment and product development in a logical way and minimize trial-and-error approaches (29, 80). This approach can potentially result in substantial savings of time and expenses and may help to avoid unnecessary and, hence, unethical clinical studies (97). Thus, dosages and dosing intervals of antimicrobial agents should be designed with reference to dynamic pharmacokinetic and pharmacodynamic parameters. Accordingly, several efficacy indices or surrogate markers that take into account both pharmacokinetic and pharmacodynamic information have been defined and used by different authors to describe the antibacterial activity of various classes of antimicrobial agents (100, 106, 59). Currently there are two main trends for antibiotic pharmacokinetic-pharmacodynamic models; those based on the MIC and those based on a kill-curve approach, both of which will be described in detail in the following.
Deconvolution of poissonian images with the PURE-LET approach

We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parametrize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds (LET). This parametrization is then optimized by minimizing a robust estimate of the mean squared error, the “Poisson unbiased risk estimate (PURE)”. Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients. In contrast to existing approaches, the proposed algorithm merely amounts to solving a linear system of equations which has a fast and exact solution. Simulation experiments over various noise levels indicate that the proposed method outperforms current state-of-the-art techniques, in terms of both restoration quality and computational time.
CS 224 W Final Report

Recommender systems are information filtering systems that provide users with personalized suggestions for products or items. They have become an important research area since the appearance of the first papers on collaborative filtering. Much work has been done on developing new approaches to recommender systems over the last years because of the growing need of applications to help users deal with an increasing amount of information and provide them with personalized recommendations. Nowadays, recommender systems are usually based on the users past behaviors and the similarity between users and/or between items. But such traditional recommendation systems suffer from several problems, one of them being data sparsity. Some users may have very few recorded (or even no) interactions with the items, and thus it is hard to predict what they might like, which is called cold start. However, there is a noticeable social trend that people that are socially related share interests and tastes, and thus, the knowledge about a person’s social neighborhood structure could be used to infer its preferences to some degree. But conventional recommendation systems don’t leverage this network structure to make predictions, thus depriving themselves of a rich source of information. The idea of this project would be to take into consideration the relational structure of the network to improve the recommendation power, for both user network and item network.
Feliss: Flexible distributed computing framework with light-weight checkpointing

Current distributed computing frameworks, such as MapReduce and Spark, allow programmers to use only limited operations defined by the framework. Because of this restriction, algorithms that do not fit with the framework cannot be efficiently expressed. This restriction arose from the need of fault-tolerance. That is, these frameworks recover lost data by re-computing them from available data when a fault occurs. To ensure this mechanism works correctly, only operations provided by the system can be used. On the other hand, there is another fault-tolerance method called checkpointing. Since it achieves fault-tolerance by saving memory contents, there is no such limitation to operations. However, the cost of saving a memory image is high. To overcome this trade-off, we propose a light-weight checkpointing method called continuation-based checkpointing, which enables low overhead fault-tolerance without any restriction. It saves only the information that is necessary for restarting, which significantly reduces the cost of checkpointing. We implemented a distributed computing framework called Feliss by using our continuation-based checkpointing method, which includes an improved MapReduce without the above restriction and a message passing interface (MPI) subset. We evaluated Feliss with various applications and showed that order-of-magnitude speedup can be attained with applications that cannot be expressed efficiently with current frameworks.
S-Flink: Schedule for QoS in Flink Using SDN

SDN provides support for QoS through mechanisms such as queuing, meters, flow control, bandwidth reservation and TE. MPTCP aware SDN also provides improved throughput. SDN is now being widely adopted in cloud data centers and so does some of the SPS. Many SPS applications have QoS constraints like bandwidth and throughput. Meeting QoS for SPS, where the peak input rate varies dynamically, is a challenge. The schedulers of current SPS do not consider QoS requirements while scheduling the tasks or while placing the replicas for HA and fault tolerance. The current SPS don't move non-QoS jobs in favor of jobs having QoS requirements. In this paper, we propose that the scheduler in a SPS, 'also' use network information from SDN controller to place tasks/replicas by 'also' considering QoS requirements. SDN can also be used to handle peak demands and to move tasks in favor of QoS jobs.
Dynaldroid: A system for automated dynamic analysis of Android applications

Android is an extensively used mobile platform and with evolution it has also witnessed an increased influx of malicious applications in its market place. Availability of multiple sources for downloading applications has aided the malware developers by increasing the consumer base for their applications. This has caused users falling victim to malicious applications. A major hindrance in blocking the entry of malicious applications into the Android market place is scarcity of effective mechanisms to detect malicious applications. Static analysis of an Android application gives a good idea as to what the application is capable of, but it's effectiveness decreases if the application is obfuscated. Dynamic analysis of an Android application shows the true behavior of the application during execution. Thus dynamic analysis is more useful in uncovering the threats that the user has from an application. The contribution of this work includes automation of Android application execution on an emulator to facilitate dynamic analysis of the application. Based on information collected during dynamic analysis of malware samples, we derived a set of patterns which indicate the occurrence of malicious activity. These patterns allowed us to develop `Dynanlroid', a dynamic analysis tool for detecting a set of malicious actions performed by Android applications. The network activity carried out by the application is also analyzed by the Dynaldroid.
Using Trees to Depict a Forest

When a database query has a large number of results, the user can only be shown one page of results at a time. One popular approach is to rank results such that the "best" results appear first. However, standard database query results comprise a set of tuples, with no associated ranking. It is typical to allow users the ability to sort results on selected attributes, but no actual ranking is defined. An alternative approach to the first page is not to try to show the best results, but instead to help users learn what is available in the whole result set and direct them to finding what they need. In this paper, we demonstrate through a user study that a page comprising one representative from each of k clusters (generated through a k-medoid clustering) is superior to multiple alternative candidate methods for generating representatives of a data set. Users often refine query specifications based on returned results. Traditional clustering may lead to completely new representatives after a refinement step. Furthermore, clustering can be computationally expensive. We propose a tree-based method for efficiently generating the representatives, and smoothly adapting them with query refinement. Experiments show that our algorithms outperform the state-of-the-art in both result quality and efficiency.
The auditory foreign-language effect of moral decision making in highly proficient bilinguals

ABSTRACT Previous research has shown that people make systematically different decisions when faced with a moral dilemma in a native than in a foreign language [e.g. Costa, A. , A. Foucart, S. Hayakawa, M. Aparici, J. Apesteguia, J. Heafner, and B. Keysar. 2014. “Your Morals Depend on Language. ” PLoS One 9 (4): e94842]. The aim of the current study is to test the limits of this so-called Foreign-Language Effect by examining (1) whether it holds for highly proficient bilinguals of a closely related language pair (i.e. Dutch-English), and (2) whether it can be replicated in an auditory setting. In Experiment 1, 60 Dutch-English bilinguals read moral dilemmas in Dutch or in English, whereas in Experiment 2, a different sample of 60 Dutch-English bilinguals listened to the same dilemmas. After reading or listening, participants’ task was to indicate whether the proposed action was appropriate or not. The results showed that the Foreign-Language Effect was absent in Experiment 1, but present in Experiment 2. These findings aid in understanding the robustness of the Foreign-Language Effect, revealing that in some contexts it may be overcome and/or inhibited, whereas in others it may be enhanced.
A Rigorous Investigation of “Evidence” and “Occam Factors” in Bayesian Reasoning

This paper first reviews the reasoning behind the Bayesian "evidence" procedure for setting parameters in the probability distributions involved in inductive inference. This paper then proves that the evidence procedure is incorrect. More precisely, this paper proves that the assumptions going into the evidence procedure do not, as claimed, "let the data determine the distributions". Instead, those assumptions simply amount to an implicit replacement of the original distributions, containing free parameters, with new distributions, none of whose parameters are free. For example, as used by MacKay [1991] in the context of neural nets, the evidence procedure is a means for using the training set to determine the free parameter ex in the distribution P(Iwil) oc exp(ro:: 1 Wi2), where the N Wi are the N weights in the network. As this paper proves, in actuality the assumptions going into MacKay's use of the evidence procedure do not result in a distribution P(lwil) oc exp(ro::1 w? ) for some ex, but rather result in a parameter-less distribution, P(lwil) oc (L:1 w? r CN! 2 + 1). This paper goes on to prove that ifone makes the assumption of an "entropic prior" with unknown parameter value, in addition to the assumptions used in the evidence procedure, then the prior is completely fixed, but in a form which can not be entropic. (This calls into question the self-consistency of the numerous arguments purporting to derive an entropic prior "from first principles".) Finally, this paper goes on to investigate the Bayesian first-principles "proof' of Occam's razor involving Occam factors. This paper proves that that "proof' is flawed.
Critical Legal Studies: A Liberal Critique

*1133 The debate between the Critical Legal Studies movement and its critics has often seemed little more than a collection of arbitrary dismissals from both sides. For this reason, few books can be more welcome than the philosopher Andrew Altman's recent study of the Critical Legal Studies movement. Altman discusses and critiques CLS writings from the standpoint of a scholar deeply committed to liberal political theory. But there is nothing dismissive about Altman's work; if Altman's is a criticism of CLS, it is a very sympathetic criticism. Altman is a liberal who nevertheless recognizes the potential failings of liberal legal theory. He agrees with Morton Horwitz that the Rule of Law is not an "unqualified human good," despite its obvious importance in preserving human liberty.1 He accepts the claim that social rules and practices affect the justice of the legal system as much as do legal rules, and argues that without a culture of tolerance and respect for freedom, legal guarantees of human rights will not be sufficient to prevent oppression.2 Thus, Altman has much in common with the CLS thinkers he purports to criticize. Nevertheless, he has an abiding faith in liberalism as a political theory that can adapt to changes in human affairs and reform itself in order better to protect human rights and human values.3 In his view, the soundest versions of liberalism have nothing to fear from CLS critiques,
Mutual Funds: Risk and Performance Analysis for Decision Making

Preface. 1. Understanding Mutual Funds. 2. Mutual Fund Service Advantages. 3. Mutual Fund Service Advantages: Professional Management. 4. Mutual Fund Service Disadvantages. 5. Mutual Fund Service Disadvantages: Expenses. 6. Long-Term Investing in Mutual Funds. 7. Portfolio Diversification Risk and Equity Style Allocation. 8. Mutual Fund Performance Measures and Buy/Sell Decision Criteria. 9. Mutual Fund Performance and Portfolio Investment Style Allocations. 10. Actively Managed versus Passively Managed Funds: The Search for Managers. 11. Mutual Funds: The Next Generation. 12. Economic and Market Indicators: Knowledge, Not Faith. Index.