Miracle-dz/UniFAR-FTUs huggingface dataset:
THE STAR FORMATION HISTORY OF BCGs TO z = 1.8 FROM THE SpARCS/SWIRE SURVEY: EVIDENCE FOR SIGNIFICANT IN SITU STAR FORMATION AT HIGH REDSHIFT

We present the results of an MIPS-24 μm study of the brightest cluster galaxies (BCGs) of 535 high-redshift galaxy clusters. The clusters are drawn from the Spitzer Adaptation of the Red-Sequence Cluster Survey, which effectively provides a sample selected on total stellar mass, over 0.2 < z < 1.8 within the Spitzer Wide-Area Infrared Extragalactic (SWIRE) Survey fields. Twenty percent, or 106 clusters, have spectroscopically confirmed redshifts, and the rest have redshifts estimated from the color of their red sequence. A comparison with the public SWIRE images detects 125 individual BCGs at 24 μm ≳ 100 μJy, or 23%. The luminosity-limited detection rate of BCGs in similar richness clusters (Ngal > 12) increases rapidly with redshift. Above z ∼ 1, an average of ∼20% of the sample have 24 μm inferred infrared luminosities of LIR > 1012 L⊙, while the fraction below z ∼ 1 exhibiting such luminosities is <1%. The Spitzer-IRAC colors indicate the bulk of the 24 μm detected population is predominantly powered by star formation, with only 7/125 galaxies lying within the color region inhabited by active galactic nuclei (AGNs). Simple arguments limit the star formation activity to several hundred million years and this may therefore be indicative of the timescale for AGN feedback to halt the star formation. Below redshift z ∼ 1, there is not enough star formation to significantly contribute to the overall stellar mass of the BCG population, and therefore BCG growth is likely dominated by dry mergers. Above z ∼ 1, however, the inferred star formation would double the stellar mass of the BCGs and is comparable to the mass assembly predicted by simulations through dry mergers. We cannot yet constrain the process driving the star formation for the overall sample, though a single object studied in detail is consistent with a gas-rich merger.
Can A ¢ ne Term Structure Models Help Us to Predict Exchange Rates ?

This paper proposes an arbitrage-free model of the joint behaviour of interest and exchange rates whose exchange rate forecasts outperform those produced by a random-walk model, a vector autoregression on the forward premiums and the rate of depreciation, or the standard forward premium regression. In addition, the model is able to reproduce the forward premium puzzle. JEL Classi cation: E43, F31, G12, G15
Incentives, Reputation and the Allocation of Authority

We address the question how much authority a principal should delegate to a manager with conflicting interests and uncertain ability in a context in which the manager has both compensation-based and reputational incentives. The optimal level of authority balances the value of the manager's decision-making expertise against the cost of ensuring that the manager uses his discretion productively. Reputational incentives reduce the necessary monetary incentives to discourage purely opportunistic behavior, but may cause the manager to pursue conservative courses of action to preserve his reputation. This undermines the benefits of delegating control, leading to decreased managerial authority and stronger monetary incentives. When the principal contracts with the manager repeatedly, she delegates additional authority early in the relationship in order to screen for managers of high ability. Decentralization of decision making may thus occur even if the static benefits from decentralization are negative.
Towards 1-click Tool Generation with PADS 1

An ad hoc data source is any semistructured data source for which useful data analysis and transformation tools have not yet been defined. XML, HTML and CSV are not ad hoc data sources as there are numerous programming libraries, query languages, manuals and other resources dedicated to helping analysts manipulate data in these formats. However, ad hoc data does arise often in many fields ranging from computational biology to finance to networking.
In-memory representations for mining big graphs

Graphs are ubiquitous and are the best data structure for representing linked data because of their flexibility, scalability, and power to deal with complexity. Storing big graphs in graph databases leads to difficult computation and increased time complexity. The best alternative is to use inmemory representations such as compact data structures. They compress the graph sufficiently such that it can be stored in memory and can allow all the possible operations in compressed form itself. In this paper we discuss about five compression techniques: WebGraph, Re-pair, BFS, k2, and dk2. In addition, we compare them based on four parameters: compression ratio, supported functionalities, supported graph types, and dynamic support. The paper is concluded by bringing out the need to have a more advanced, dynamic, and versatile compression technique.
Validation of Visual Statistical Inference , with Application to Linear Models

Statistical graphics play a crucial role in exploratory data analysis, model checking and diagnosis. Until recently there were no formal visual methods in place for determining statistical significance of findings. This changed, when Buja et al. (2009) conceptually introduced two protocols for formal tests of visual findings. In this paper we take this a step further by comparing the lineup protocol (Buja et al., 2009) against classical statistical testing of the significance of regression model parameters. A human subjects experiment is conducted using simulated data to provide controlled conditions. Results suggest that the lineup protocol provides results equivalent to the uniformly most powerful (UMP) test and for some scenarios yields better power than the UMP test.
Leviticus 1-10

James Watts uses rhetorical analysis for this detailed exposition of Leviticus 1-10. In dialogue with a wide variety of contemporary scholarship on Leviticus, this commentary also engages the history of the book's interpretation and the history of Jewish and Christian ritual practices. Leviticus's rhetoric aimed to persuade ancient Israelites to make offerings to God. It legitimized the monopoly of Aaronide priests over Israel's offerings and over determining correct ritual practice. The priests in turn established the Torah containing Leviticus as the authoritative text of Israel's religion. Rhetorical analysis of Leviticus thus leads to new insights into the role of priests in raising the Pentateuch to the status of scripture and in shaping the biblical canon. It also calls attention to the role of ritual rhetoric in the polities of later Jewish and Christian groups, despite the fact that neither religion makes animal offerings as Leviticus 1-10 mandates.
Dynamic Information Flow Analysis and Dynamic Slicing ( Extended Version )

Information flow analysis is concerned with validating the security of information flows between objects in a program and between the program and the outside world. This paper presents a practical approach to dynamic information flow analysis that can be used in software testing or intrusion detection and that facilitates debugging of insecure flows. The proposed approach provides capabilities to validate and debug dynamic information flows against a given information flow policy. It can be applied offline with test cases or captured operational inputs, or, when fast response is not critical, it can be applied online with deployed software. This paper presents a precise forward-computing algorithm for dynamic slicing, which is applicable to debugging insecure information flows and other failures in both structured and unstructured programs. A prototype tool implementing the proposed approach has been developed for Java byte code programs.
VHDL-AMS model of an electrochemical cell to design VLSI bio-chips

This paper presents a new VHDL-AMS behavioral model of an electrochemical cell suitable for the design of VLSI bio-chips. Existing models are mainly equivalent circuit macro-models using electrical primitives, which hardly fit the actual behavior of biosensors. The presented VHDL-AMS model expresses a more realistic behavior as it can directly use electrochemical equations. It can also be made very flexible and reusable for different experimentation cases. Last, but not least, it can be easily used for the design of bio-chips.
Morphological Description of Cervical Cell Images for the Pathological Recognition

— The tracking allows to detect the tumor affections of cervical cancer, it is particularly complex and consuming time, because it consists in seeking some abnormal cells among a cluster of normal cells. In this paper, we present our proposed computer system for helping the doctors in tracking the cervical cancer. Knowing that the diagnosis of the malignancy is based in the set of atypical morphological details of all cells, herein, we present an unsupervised genetic algorithm for the separation of cell components since the diagnosis is doing by analysis of the core and the cytoplasm. We give also the various algorithms used for computing the morphological characteristics of cells (Ratio core/cytoplasm, cellular deformity, …) necessary for the recognition of illness.
DIGITAL CONTROL OF SINGLE-PHASE POWER FACTOR PREREGULATORS WITH INPUT VOLTAGE ESTIMATION

This paper proposes a fully digital control of boost Power Factor Preregulators (PFPs) with input voltage estimation. The proposed solution is based on a conventional multi-loop structure for PFP with an internal current control and an outer voltage control possibly with fast dynamic response. Instead, input voltage sensing is avoided by using a conventional PI (proportional-integral) current regulator for the estimation of the rectified input voltage. The use of the estimated rectified input voltage for the generation of current reference introduces an additional control loop which, however, increases the stability margin of the current regulator. The digital control has been implemented using the TMX320F2812 DSP, which has been proven to be an effective hardware for rapid prototyping. Experimental results on a single-phase 400W boost PFP show the effectiveness of the proposed approach.
Limited spatial response to direct predation risk by African herbivores following predator reintroduction

Abstract Predators affect ecosystems not only through direct mortality of prey, but also through risk effects on prey behavior, which can exert strong influences on ecosystem function and prey fitness. However, how functionally different prey species respond to predation risk and how prey strategies vary across ecosystems and in response to predator reintroduction are poorly understood. We investigated the spatial distributions of six African herbivores varying in foraging strategy and body size in response to environmental factors and direct predation risk by recently reintroduced lions in the thicket biome of the Addo Elephant National Park, South Africa, using camera trap surveys, GPS telemetry, kill site locations and Light Detection and Ranging. Spatial distributions of all species, apart from buffalo, were driven primarily by environmental factors, with limited responses to direct predation risk. Responses to predation risk were instead indirect, with species distributions driven by environmental factors, and diel patterns being particularly pronounced. Grazers were more responsive to the measured variables than browsers, with more observations in open areas. Terrain ruggedness was a stronger predictor of browser distributions than was vegetation density. Buffalo was the only species to respond to predator encounter risk, avoiding areas with higher lion utilization. Buffalo therefore behaved in similar ways to when lions were absent from the study area. Our results suggest that direct predation risk effects are relatively weak when predator densities are low and the time since reintroduction is short and emphasize the need for robust, long‐term monitoring of predator reintroductions to place such events in the broader context of predation risk effects.
Contests with the Probability of a Draw: A Stochastic Foundation

This article proposes a stochastic foundation for the contest success function (CSF for short) with a richer structure on the set of possible outcomes of the contest. Specifically, the analysis allows for the possibility of a draw, so that no contestant can claim a victory over all other players. Under plausible conditions, this article not only discovers new functional forms of CSFs, but also shows the newly derived CSFs have very different properties in equilibrium to those of conventional CSFs. For example, in contrast to the CSFs discussed in the contest literature, which always generate a unique pure strategy Nash equilibrium, the newly discovered CSFs admit the possibility of multiple equilibria.
CPMTS: Catching packet modifiers with trust support in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. Packet modification is a common attack in wireless sensor networks. In literature, many schemes have been proposed to mitigate such an attack but very few detect the malicious nodes effectively. In the proposed approach, each node chooses the parent node for forwarding the packet towards sink. Each node adds its identity and trust on parent as a routing path marker and encrypts only the bytes added by node in packet before forwarding to parent. Sink can determine the modifiers based on trust value and node identities marked in packet. Child node observes the parent and decides the trust on parent based on successful and unsuccessful transactions. Data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node at the beginning of a round based on its own observation on parent. Simulated the algorithm in NS-3 and performance analysis is discussed. With the combination of trust factor and fixed path routing to detect malicious activity, analytical results show that proposed method detect modifiers efficiently and early, and also with low percentage of false detection.
Neurobeachin Is Essential for Neuromuscular Synaptic Transmission

We report a random disruption in the mouse genome that resulted in lethal paralysis in homozygous newborns. The disruption blocked expression of neurobeachin, a protein containing a BEACH (beige and Chediak-Higashi) domain implicated in synaptic vesicle trafficking and an AKAP (A-kinase anchor protein) domain linked to localization of cAMP-dependent protein kinase activity. nbea-null mice demonstrated a complete block of evoked synaptic transmission at neuromuscular junctions, whereas nerve conduction, synaptic structure, and spontaneous synaptic vesicle release were completely normal. These findings support an essential role for neurobeachin in evoked neurotransmitter release at neuromuscular junctions and suggest that it plays an important role in synaptic transmission.
Monetary Policy, Real Exchange Rate, and the Current Account in a Small Open Economy

Using a sticky price model for small open economy this paper compares the consequences of two alternative monetary policies over a set of variables. The baseline case assumes a policy rule for the Central Bank that has inflation forecast and output as a target. The alternative policy is a rule that also includes a target for the real exchange rate. The inclusion of a target for the real exchange rate in the policy reaction function of the Central Bank could be important if the monetary authority is concerned about fluctuations in the current account. The results show that the alternative policy can help to reduce the volatility of the external accounts and in some case it can also help to reduce the volatility of the output gap. However, this policy introduces more volatility in inflation and it also makes the convergence of this variable to its steady state level slower.
Economic classification and regression problems and neural networks.

Artificial neural networks provide powerful models for solving many economic classifications, as well as regression problems. For example, they were successfully used for the discrimination between healthy economic agents and those prone to bankruptcy, for the inflation-deflation forecasting, for the currency exchange rates prediction, or for the prediction of share prices. At present, the neural models are part of the majority of standard statistical software packages. This paper discusses the basic principles, which the neural network models are based on, and sum up the important principles that must be respected in order that their utilization in practice is efficient.
Biomedical titanium alloys with Young’s moduli close to that of cortical bone

Biomedical titanium alloys with Young’s moduli close to that of cortical bone, i.e., low Young’s modulus titanium alloys, are receiving extensive attentions because of their potential in preventing stress shielding, which usually leads to bone resorption and poor bone remodeling, when implants made of their alloys are used. They are generally β-type titanium alloys composed of non-toxic and allergy-free elements such as Ti–29Nb–13Ta–4.6Zr referred to as TNTZ, which is highly expected to be used as a biomaterial for implants replacing failed hard tissue. Furthermore, to satisfy the demands from both patients and surgeons, i.e., a low Young’s modulus of the whole implant and a high Young’s modulus of the deformed part of implant, titanium alloys with changeable Young’s modulus, which are also β-type titanium alloys, for instance Ti–12Cr, have been developed. In this review article, by focusing on TNTZ and Ti–12Cr, the biological and mechanical properties of the titanium alloys with low Young’s modulus and changeable Young’s modulus are described. In addition, the titanium alloys with shape memory and superelastic properties were briefly addressed. Surface modifications for tailoring the biological and anti-wear/corrosion performances of the alloys have also been briefly introduced.
FEATURE EXTRACTION TECHNIQUE FOR HANDWRITTEN INDIAN NUMBERS CLASSIFICATION

In this paper, we propose a recognition technique f or handwritten Indian numbers. Due to the variation s in shapes and sizes of handwritten Indian numbers, ima ge representation, smoothing, skeletonization and localization processes are applied to further impro ve the classification performance. We employ five efficient classifiers RNN, DROP 1, DROP 2, DROP 3 a nd DROP 4 in our technique. New feature extraction method that divides the image of the han dwritten Indian number into 16 sub blocks and 24 partition lines is proposed. For this feature extra ction, a vector set is built with 41 attributes re presenting 16 sub blocks, 24 partition lines and one for the p ossibility of getting closed loop. Simulation resul t show that the above classifiers with the new proposed fe atur extraction improve the recognition accuracy w ith acceptable subset size. It is noted that DROP 3 met hod has the highest classification accuracy with go od reduced memory size compared to other methods.
Solving the Inverse Cauchy Problem of the Laplace Equation Using the Method of Fundamental Solutions and the Exponentially Convergent Scalar Homotopy Algorithm (Ecsha)

In this paper, the inverse Cauchy problem of the Laplace equation is considered. Using the method of fundamental solutions, a system of linear algebraic equations can be obtained by satisfying the Cauchy boundary conditions on the overprescribe boundary points. The resulting linear algebraic equation is ill-posed and is treated by the exponentially convergent scalar homotopy algorithm (ECSHA). Four examples are adopted to show the validity of the proposed numerical scheme and it is concluded that the current approach can successfully resolve the ill-posedness of the inverse Cauchy problem even when the noise exists.
Mechanisms for Increased Glycolysis in the Hypertrophied Rat Heart

Glycolysis increases in hypertrophied hearts but the mechanisms are unknown. We studied the regulation of glycolysis in hearts with pressure-overload LV hypertrophy (LVH), a model that showed marked increases in the rates of glycolysis (by 2-fold) and insulin-independent glucose uptake (by 3-fold). Although the Vmax of the key glycolytic enzymes was unchanged in this model, concentrations of free ADP, free AMP, inorganic phosphate (Pi), and fructose-2,6-bisphosphate (F-2,6-P2), all activators of the rate-limiting enzyme phosphofructokinase (PFK), were increased (up to 10-fold). Concentrations of the inhibitors of PFK, ATP, citrate, and H+ were unaltered in LVH. Thus, our findings show that increased glucose entry and activation of the rate-limiting enzyme PFK both contribute to increased flux through the glycolytic pathway in hypertrophied hearts. Moreover, our results also suggest that these changes can be explained by increased intracellular free [ADP] and [AMP], due to decreased energy reserve in LVH, activating the AMP-activated protein kinase cascade. This, in turn, results in enhanced synthesis of F-2,6-P2 and increased sarcolemma localization of glucose transporters, leading to coordinated increases in glucose transport and activation of PFK.
A methodological leg to stand on: lessons learned using grounded theory to study software development

We are engaged in a qualitative research project to understand how people manage the process of software development. This study uses grounded theory as its method of inquiry and we have learned much about what is and what is not a grounded theory. We, like many researchers have claimed to follow grounded theory methods and even to have produced a grounded theory. In reality, we often only borrow a few grounded theory practices to categorize our data. This paper presents lessons learned about using grounded theory so that both researchers and reviewers can critically evaluate investigators' claims to be producing grounded theory.
Spatio-temporal aggregation for visual analysis of movements

Data about movements of various objects are collected in growing amounts by means of current tracking technologies. Traditional approaches to visualization and interactive exploration of movement data cannot cope with data of such sizes. In this research paper we investigate the ways of using aggregation for visual analysis of movement data. We define aggregation methods suitable for movement data and find visualization and interaction techniques to represent results of aggregations and enable comprehensive exploration of the data. We consider two possible views of movement, traffic-oriented and trajectory-oriented. Each view requires different methods of analysis and of data aggregation. We illustrate our argument with example data resulting from tracking multiple cars in Milan and example analysis tasks from the domain of city traffic management.
A Preliminary Investigation on Computer Vision for Telemedicine Systems Using OpenCV

OpenCV is typically, an open source vision library suitable for computer vision programs. In this paper, we present some of our preliminary investigation experiences of developing Computer Vision programs using OpenCV for robotic telemedicine cluster system, within the practice based ICTD subject within the undergraduate Software Engineering Program at the Faculty of Engineering, University of Technology Sydney (UTS). Firstly, it discusses our shared experiences in designing and implementing Computer Vision subsystem and discusses successes, as well as common problems both experienced and anticipated in adaptation of OpenCV framework and then justifies its purpose building a robotic system for telemedicine. Finally, it attempts to bridge the gap between the theoretical knowledge of design and programming with the practical side of software reuse and modularization when designing and implementing a robotic system for medical applications.