Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoothing parameter selection in nonparametric regression using an improved kullback information criterion

Many different methods for constructing nonparametric estimates of a smooth regression function use a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper, a method based on an improved version of Kullback Information Criterion (KIC), termed KICc1 is derived and implemented for selecting the appropriate smoothing parameter for any type of linear smoother. Monte Carlo simulations demonstrate that KICc1 , unlike the Generalized Cross Validation (GCV) method, has less tendency to undersmooth and exhibits low variability specially for low SNR. Also, it is very competitive with the plug-in method and performs well when this last fails. Furthermore, KICc1 slightly outperforms its analogue AICc1 based on the known Akaike Information criterion (AIC).
Implicitly-Parallel Functional Dataflow for Productive Cloud Programming on Chameleon

One solution that makes parallel programming implicit rather than explicit is the dataflow model. Conceived ~35 years ago, it has only recently been made practical through systems such as Dryad and Swift [1]. We believe that we have successfully created a base for an implicitlyparallel functional dataflow programming model, as exemplified by Swift, a workflow language for executing scientific applications. This model has been characterized as a perfect fit for the many-task computing (MTC) paradigm. Some broad application classes that fit the MTC paradigm are workflows, MapReduce, high-throughput computing, and a subset of high-performance computing. MTC emphasizes using many computing resources over short periods of time to accomplish many smaller computational tasks (both dependent and independent), where the primary metrics are measured in seconds. MTC has proven successful in grid computing and supercomputing, but the distributed nature of today’s cloud resources pose many challenges in the efficient support of MTC workloads. This work aims to address the programmability gap between MTC and cloud computing, through an innovative parallel scripting language, Swift, which will enable MTC workloads to efficiently leverage cloud resources. This work will enable a broader class of MTC applications to leverage cloud systems.
An Analysis of Hedge Fund Strategies-Abstract

Part One: The Persistence in Hedge Fund Performance As stated in the first part of the introduction, the first objective of this Thesis is to understand hedge fund managers and to explain how they create alpha over time. This involve developing, testing and improving a performance analysis model in order to understand hedge fund performance on the one hand, while on the other developing and adapting a methodology to determine whether there is any persistence in hedge fund returns. We achieved this objective in three complementary studies (An Analysis of Hedge Fund Performance, Hedge Fund Performance and Persistence in Bull and Bear Markets and Sustainability in Hedge Fund Performance: New Insights). The basis of Part 1 is study 1 that aims at answering to one question: What factors might explain hedge fund returns? We base our multi-factor performance decomposition model on models that are used in the mutual fund literature for years, Fama and French (1993) and Carhart (1997) models. Even if hedge funds are different from mutual funds by the strategy they apply, the securities they use and the freedom they have in their management, they remain investment funds. As such, we saw a model coming from the mutual fund literature as a good basis to build a new performance decomposition model specific to hedge funds. 5 Fama and French’s (1993) model includes the following: a size factor that takes into account the difference in performance between small and large companies; a style factor that takes into account the difference in performance between growth and value players 6 Carhart (1997) extension added a momentum factor that takes into account the fact that certain managers favour previously well performing stocks in their portfolio.
The Group I Metabotropic Glutamate Receptor mGluR5 Is Required for Fear Memory Formation and Long-Term Potentiation in the Lateral Amygdala

The group I metabotropic glutamate receptor subtype mGluR5 has been shown to play a key role in the modulation of synaptic plasticity. The present experiments examined the function of mGluR5 in the circuitry underlying Pavlovian fear conditioning using neuroanatomical, electrophysiological, and behavioral techniques. First, we show using immunocytochemical and tract-tracing methods that mGluR5 is localized to dendritic shafts and spines in the lateral nucleus of the amygdala (LA) and is postsynaptic to auditory thalamic inputs. In electrophysiological experiments, we show that long-term potentiation at thalamic input synapses to the LA is impaired by bath application of a specific mGluR5 antagonist, 2-methyl-6-(phenyle-thynyl)-pyridine (MPEP), in vitro. Finally, we show that intra-amygdala administration of MPEP dose-dependently impairs the acquisition, but not expression or consolidation, of auditory and contextual fear conditioning. Collectively, the results of this study indicate that mGluR5 in the LA plays a crucial role in fear conditioning and in plasticity at synapses involved in fear conditioning.
Event-Related Potentials in Major Depressive Disorder : The Relationship between P 300 and Treatment Response 2

Methods: Twenty-eight patients suffering from major depression who completed 12 weeks of follow-up appointments and 28 healthy people, whose age and gender were matched with patients, were included in the study. Event-related potentials (P300) were recorded for patients before and after treatment with sertraline (50-200 mg/day) for 12 weeks. Treatment response was defined as a 50% or greater decrease in a given patient’s total Hamilton Depression Rating Scale score. Pre-treatment and post-treatment P300 amplitude and latency values were compared for responders (n=18), non-responders (n=10) and healthy subjects.
Genomic Heterogeneity of Osteosarcoma - Shift from Single Candidates to Functional Modules

Osteosarcoma (OS), a bone tumor, exhibit a complex karyotype. On the genomic level a highly variable degree of alterations in nearly all chromosomal regions and between individual tumors is observable. This hampers the identification of common drivers in OS biology. To identify the common molecular mechanisms involved in the maintenance of OS, we follow the hypothesis that all the copy number-associated differences between the patients are intercepted on the level of the functional modules. The implementation is based on a network approach utilizing copy number associated genes in OS, paired expression data and protein interaction data. The resulting functional modules of tightly connected genes were interpreted regarding their biological functions in OS and their potential prognostic significance. We identified an osteosarcoma network assembling well-known and lesser-known candidates. The derived network shows a significant connectivity and modularity suggesting that the genes affected by the heterogeneous genetic alterations share the same biological context. The network modules participate in several critical aspects of cancer biology like DNA damage response, cell growth, and cell motility which is in line with the hypothesis of specifically deregulated but functional modules in cancer. Further, we could deduce genes with possible prognostic significance in OS for further investigation (e.g. EZR, CDKN2A, MAP3K5). Several of those module genes were located on chromosome 6q. The given systems biological approach provides evidence that heterogeneity on the genomic and expression level is ordered by the biological system on the level of the functional modules. Different genomic aberrations are pointing to the same cellular network vicinity to form vital, but already neoplastically altered, functional modules maintaining OS. This observation, exemplarily now shown for OS, has been under discussion already for a longer time, but often in a hypothetical manner, and can here be exemplified for OS.
Citation Patterns of a Controversial and High-Impact Paper: Worm et al. (2006) “Impacts of Biodiversity Loss on Ocean Ecosystem Services”

Citation patterns were examined for Worm et al. 2006 (Science 314∶787–790), a high-impact paper that focused on relationships between marine biodiversity and ecosystem services. This paper sparked much controversy through its projection, highlighted in the press release, that all marine fisheries would be collapsed by 2048. Analysis of 664 citing papers revealed that only a small percentage (11%) referred to the 2048 projection, while 39% referred to fisheries collapse in general, and 40% to biodiversity and ecosystem services. The 2048 projection was mentioned more often in papers published soon after the original paper, in low-impact journals, and in journals outside of fields that would be expected to focus on biodiversity. Citing papers also mentioned the 2048 projection more often if they had few authors (28% of single-author papers vs. 2% of papers with 10 or more authors). These factors suggest that the more knowledgeable the authors of citing papers were about the controversy over the 2048 projection, the less likely they were to refer to it. A noteworthy finding was that if the original authors were also involved in the citing papers, they rarely (1 of 55 papers, 2%) mentioned the 2048 projection. Thus the original authors have emphasized the broader concerns about biodiversity loss, rather than the 2048 projection, as the key result of their study.
No man’s brand : brands, institutions, fashion and the economy

Branding has become so intertwined with consumption that today’s consumers have often deeply personal relationships to brands and brand histories. Branding is an attempt to strategically ‘personify’ products and to encapsulate a balance between different economic values: quality, utility, symbolic and cultural worth. In this paper we argue that the relationship between the contemporary consumer and producer is mediated by and governed by a reflexive construction of brands. As such brands are best understood from an institutional perspective. The paper illustrates the institutional role of brand by using the example of the fashion industry. It is argued that in the fashion industry a focus on consumer-producer brandbuilding and brand loyalty now takes pole-position in determining industrial and innovation dynamics. We conclude by suggesting that economic geography has consistently undervalued brands as an area of study. By taking brands as the core product in industrial production, rather than as an interesting aside for sociologists, historians and cultural theorists, economic geography can better understand the institutions governing the economy. In particular better understanding the institution of brand helps us better appreciate the dynamics systems within which commodities and commodity chains are formed.
The Eilenberg-Watts theorem in homotopical algebra

The object of this paper is to prove that the standard categories in which homotopy theory is done, such as topological spaces, simplicial sets, chain complexes of abelian groups, and any of the various good models for spectra, are all homotopically self-contained. The left half of this statement essentially means that any functor that looks like it could be a tensor product (or product, or smash product) with a fixed object is in fact such a tensor product, up to homotopy. The right half says any functor that looks like it could be Hom into a fixed object is so, up to homotopy. More precisely, suppose we have a closed symmetric monoidal category (resp. Quillen model category) M. Then the functor T_{B} that takes A to A tensor B is an M-functor and a left adjoint. The same is true if B is an E-E'-bimodule, where E and E' are monoids in M, and T_{B} takes an E-module A to A tensored over E with B. Define a closed symmetric monoidal category (resp. model category) to be left self-contained (resp. homotopically left self-contained) if every functor F from E-modules to E'-modules that is an M-functor and a left adjoint (resp. and a left Quillen functor) is naturally isomorphic (resp. naturally weakly equivalent) to T_{B} for some B. The classical Eilenberg-Watts theorem in algebra then just says that the category of abelian groups is left self-contained, so we are generalizing that theorem.
A Search for Radio Emission at the Bottom of the Main Sequence and Beyond

We have used the VLA to conduct a deep search for 3.6 cm radio emission from nearby very low mass stars and brown dwarfs. The Güdel-Benz relation is used to predict radio luminosities for some very low mass stars and candidate brown dwarfs with measured X-ray fluxes. The predicted radio fluxes are quite small, whereas the measured radio flux from the brown dwarf candidate GY 31 in the ρ Oph cloud is relatively strong. In light of our new observations, this object remains an anomaly. We present upper limits for our measured radio fluxes at 3.6 cm for our targets.
Europe's New Border Taxes

Instead of abolishing internal border controls in 1993, the European Union (EU) replaced them with VAT and statistical requirements that appear to be just as onerous. For Dutch businesses, the compliance costs of the new requirements are, on average, 5 per cent of the value of their intra-EU trade. The figure is probably higher for other EU Member States. Obviously, the costs constitute a (differentiated) border tax that impedes intra-EU trade. The article analyses the determinants of the compliance costs, as well as their effect on intra-EU trade intensity. The article submits that the differential compliance costs violate the non-discrimination provisions of the EC Treaty. Suggestions are made to reduce them.
Iterative interference cancellation and decoding for convolutional coded VBLAST systems

In this paper, we present an iterative interference cancellation (IIC) and decoding scheme that significantly improves the performance of coded VBLAST systems. We show that due to the residue interference caused by incorrect decisions from the previous iteration, the distribution of the total noise in the IIC process is non-Gaussian. Euclidean distance therefore cannot be used as the bit metric in soft decision Viterbi decoding. To preserve the Gaussian distribution of the total noise, we propose to use soft nulling for VBLAST detection. We also propose a scheme employing soft decision Viterbi decoding with IIC (SDD-IIC) and demonstrate that it significantly improves both the coding gain and diversity gain of the VBLAST system.
A Distributional Model of Semantic Context Effects in Lexical Processing

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a computational model of contextual facilitation based on word co-occurrence vectors, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. The aim of our study is to find out whether special-purpose mechanisms are necessary in order to capture the pattern of the experimental results.
Combining sFlow and Tracker traffic analysis: A novel estimation approach for network-wide BitTorrent distribution

BitTorrent(BT) has emerged as one of the most popular protocols for content sharing in recent years. Most BT applications are network-oblivious which brings great challenges to traffic engineering. As basic input information, network-wide BT traffic distribution is of vital importance for service providers (SP) or carriers to enforce appropriate control policies. Direct online measurement of network-wide BT traffic faces scalability issue, for it requires deploying application identification deep packet inspection (DPI) device on every subject link. In this paper, we propose a novel approach to estimate network-wide BT traffic distribution through combining sFlow and Tracker traffic analysis. Owing to a noticeable increase of BT traffic using random port numbers, sFlow alone is no longer sufficient for BT recognition. We tackle this problem by combining sFlow with information collected from BT tracker's traffic. With assistance of this information, BT samples can be accurately distinguished from other sFlow samples. The efficacy of our approach is evaluated through experiments on backbone links of campus network. Evaluation results show that our approach can reach a high accuracy in network-wide BT traffic estimation.
The Antiangiogenic Compound Aeroplysinin-1 Induces Apoptosis in Endothelial Cells by Activating the Mitochondrial Pathway

Aeroplysinin-1 is a brominated metabolite extracted from the marine sponge Aplysina aerophoba that has been previously characterized by our group as a potent antiangiogenic compound in vitro and in vivo. In this work, we provide evidence of a selective induction of apoptosis by aeroplysinin-1 in endothelial cells. Studies on the nuclear morphology of treated cells revealed that aeroplysinin-1 induces chromatin condensation and nuclear fragmentation, and it increases the percentage of cells with sub-diploid DNA content in endothelial, but not in HCT-116, human colon carcinoma and HT-1080 human fibrosarcoma cells. Treatment of endothelial cells with aeroplysinin-1 induces activation of caspases-2, -3, -8 and -9, as well as the cleavage of apoptotic substrates, such as poly (ADP-ribose) polymerase and lamin-A in a caspase-dependent mechanism. Our data indicate a relevant role of the mitochondria in the apoptogenic activity of this compound. The observation that aeroplysinin-1 prevents the phosphorylation of Bad relates to the mitochondria-mediated induction of apoptosis by this compound.
A Registration Approach to Coastline Satellite Image

A rough-to-fine image registration algorithm is brought forward for the calculation of satellite attitude information. Rough transformation parameters are calculated through feature point sets algorithm. In fine registration process, edge map of partial real-time image is firstly detected and edge tentative connection technique is applied, then edge labeling is performed; the objective coastline is matched through dynamic programming. After this, chain code-based corner detector and cubic B-spline interpolation are used to acquire the accurate position of corners on coastline; arc length restriction is applied in the corner matching algorithm and corner matching confidence is calculated. ; accurate transformation parameters are obtained adopting the least squares method with arc-length as weight. Simulated experimental results indicate that the algorithm is of high accuracy and timesaving
Diabetic Prognosis through Data Mining Methods and Techniques

Data mining now-a-days plays an important role in prediction of diseases in health care industry. Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. Medical data mining has emerged impeccable with potential for exploring hidden patterns from the data sets of medical domain. These patterns can be utilized for fast and better clinical decision making for preventive and suggestive medicine. However raw medical data are available widely distributed, heterogeneous in nature and voluminous for ordinary processing. Data mining and Statistics can collectively work better towards discovering hidden patterns and structures in data. In this paper, two major Data Mining techniques v.i. z. , FP-Growth and Apriori have been used for application to diabetes dataset and association rules are being generated by both of these algorithms.
A Performance Assessment Metric for Information Security Financial Instruments

Business interruptions caused by cyber-attacks pose a serious threat to revenue and share price of the organisation. Furthermore, recent cyber-attacks on various organisations prove that the technical controls, security policies, and regulatory compliance are not sufficient to mitigate the cyber risks. In such a scenario, the residual cyber risk can be mitigated with cyber-insurance policies and with information security derivatives (financial instruments). Information security derivatives are a new class of financial instruments designed to provide an alternate risk mitigation mechanism to reduce the potential adverse impact of an information security event. However, there is a lack of research on the metrics to measure the performance of information security derivatives in mitigating the underlying risk. This article examines the basic requirements to assess the performance of information security derivatives. Furthermore, the article presents three metrics, namely hedge ratio, hedge effectiveness, and hedge efficiency to formulate and evaluate a cyber risk mitigation strategy devised with information security derivatives. Also, the application of these metrics is demonstrated in an imaginary scenario. The accurate measure of performance of information security derivatives is of practical importance for effective risk management strategy.
Lessons Learned from Modelling the NHL Playoff Qualification Problem

The modelling of complex problems tends to be most effective when modelling is calibrated using a concrete solver and modifications to the model are made as a result. In some cases, the final model is significantly different from the simple model that best fits the constraints of the problem. While there are several projects that are attempting to create black box solvers with generic modelling languages, for the moment the modelling processes is intimately tied to the solving and search procedure. This paper looks at the changes to the simple model and the search procedures that were made to create an efficient solution to the NHL Playoff Qualification problem. The approaches for improving the model could be extended to other applications as the techniques are not specific to this problem.
Achievable angles between two compressed sparse vectors under RIP-induced norm/distance constraints

The angle between two compressed sparse vectors subject to the norm/distance constraints imposed by the restricted isometry property (RIP) of the sensing matrix plays an important role in the studies of many compressive sensing (CS) problems. Assuming that (i) u and v are two sparse vectors with (u, v) = θ and (ii) the sensing matrix Φ satisfies RIP, this paper is aimed at analytically characterizing the achievable angles between Φu and Φv. Motivated by geometric interpretations of RIP and with the aid of the well-known law of cosines, we propose a plane geometry based formulation for the study of the considered problem. It is shown that all the RIP-induced norm/distance constraints on Φu and Φv can be jointly depicted via a simple geometric diagram in the two-dimensional plane. This allows for a joint analysis of all the involved algebraic constraints from a geometric perspective. By conducting plane geometry analyses based on the constructed diagram, closed-form formulae for the maximal and minimal achievable angles are derived. Computer simulations confirm that the proposed solution is tighter than an existing algebraic-based estimate derived using the polarization identity.