Miracle-dz/UniFAR-FTUs huggingface dataset:
Sound credit scores and financial decisions despite cognitive aging

Significance At a time when the world’s 65-and-older population will double by 2035, policy changes have transferred many complex financial and healthcare decisions to individuals. Age-related declines in cognitive ability raise the specter that older adults facing major financial decisions may find them increasingly challenging. We explore whether knowledge and expertise accumulated from past decisions can offset age-related cognitive declines. Using a unique dataset that combines measures of cognitive ability, knowledge, and credit scores—a measure of creditworthiness that reflects sustained ability for sound financial decision-making—we find that cognitive decline does not spell doom. Instead, domain-specific knowledge and expertise provide an alternative route to sound financial decisions. These results suggest guidelines for designing effective interventions and decision aids across the life span. Age-related deterioration in cognitive ability may compromise the ability of older adults to make major financial decisions. We explore whether knowledge and expertise accumulated from past decisions can offset cognitive decline to maintain decision quality over the life span. Using a unique dataset that combines measures of cognitive ability (fluid intelligence) and of general and domain-specific knowledge (crystallized intelligence), credit report data, and other measures of decision quality, we show that domain-specific knowledge and expertise provide an alternative route for sound financial decisions. That is, cognitive aging does not spell doom for financial decision-making in domains where the decision maker has developed expertise. These results have important implications for public policy and for the design of effective interventions and decision aids.
La performance des managers de comptes clés dans un contexte industriel : Conceptualisation et modèlisation

Cette recherche a pour objet de proposer un modele explicatif de la performance des managers de comptes cles dans un contexte industriel. Elle s'interesse aux leviers d'action (comportements et mecanismes) que ces commerciaux particuliers utilisent pour executer leurs activites importantes et etre performants en termes de creation de valeur percue, d'integration percue et de satisfaction client. Dans une premiere partie, nous presentons le contexte theorique de notre recherche. Apres avoir analyse la fonction manager des comptes cles et les recherches sur la performance des vendeurs, trois notions fondamentales sont retenues sur la base d'une adaptation du cadre conceptuel de Walker, Chruchill et Ford (1979) : activites importantes, leviers d'action et performance des resultats. Une revue elargie de la litterature est ensuite realisee sur ces differents domaines. Dans la deuxieme partie, les apports de la litterature sont confrontes aux resultats d'une enquete exploratoire, realisee aupres de six fournisseurs de la grande industrie. Ceci nous permet de conceptualiser les leviers d'action et la performance des managers de comptes cles, puis l'elaborer un cadre conceptuel et un corpus d'hypotheses. La troisieme partie presente les caracteristiques et les resultats d'une enquete par questionnaire realisee aupres d'un echantillon d'acheteurs. Elle permet de juger de la qualite de nos mesures et de tester un modele explicatif par le biais de la methode des equations structurelles (PLS). La contribution de cette recherche est triple. D'un point de vue theorique, elle propose une nouvelle explication de la performance des managers de comptes cles. Au niveau methodologique, elle contribue au developpement d'echelles de mesure propres aux managers de comptes cles. Enfin, dans une optique plus manageriale, elle offre aux praticiens un ensemble de recommandations utiles a la comprehension et a l'amelioration de la performance des managers de comptes cles.
A ray tracing method for predicting path loss and delay spread in microcellular environments

The ability to predict path loss and delay spread is crucial for determining coverage and for planning interference reduction strategies in wireless radio system design. A promising theoretical method to accurately predict these channel characteristics in microcells is presented. The method uses modified geometrical optics to evaluate average path loss and delay spread. Quantitative building data, such as location, height, and electrical properties, are used to determine the individual multipath component amplitudes and delays. Preliminary verification of the technique against measured data has been conducted. The results illustrate that accurate path loss prediction is possible, with predicted values being within 5 dB of the measured values. As a result of this study, a computer program is being developed to automate the prediction process. The technical issues required for automated propagation prediction are presented. The ray optics model, computer ray tracing techniques, and building data requirements are also described. Comparisons between simulations and measurements are provided. <<ETX>>
Continual coordination of spacecraft through shared activities

Interacting agents that interleave planning and execution must reach consensus on their commitments to each other. In domains where agents have varying degrees of interaction and different constraints on communication and computation, agents will require different coordination protocols in order to efficiently reach consensus. ShAC (Shared Activity Coordination) is a framework for designing coordination protocols with an algorithm for continually coordinating agents using these protocols during execution. We show how to construct a wide range of protocols using this framework and describe how ShAC coordinates two rovers and an orbiter in a simulated Mars scenario.
An Empirical Study on the Relationship Between Entrepreneur's Social Network and Entrepreneurial Performance: The Case of the Chinese IT Industry

The paper used data from the Chinese IT industry to test the effects of a mediator over the relationship between entrepreneurial social network and entrepreneurial performance. Results showed that entrepreneurs’ social network size was the predictor in entrepreneurial self-efficacy, which in turn, results in better entrepreneurial performance. Results also showed that the relationship between entrepreneurial social network and entrepreneurial self-efficacy was moderated by entrepreneurial stage and experience. Implications of the findings for researchers and practitioners are presented.
Bulk I/O Storage Management for Big Data Applications

We propose and design a new Block I/O schedulingscheme called Bulk I/O Dispatch (BID) suited for disk intensiveMapReduce applications. Large data access by such applicationsresult in a large number of block I/O requests which have thepotential to be sequentialized. However, due to contention byother applications and the way current I/O schedulers operate, the opportunities of a large sequential I/Os are missed. SequentialI/Os, which are faster than random I/Os, can lead to saving theCPU wait times and thus better application performance. The proposed scheduler is designed to work with all blockdevices which have superior sequential performance than random. Through simulation based experiments with MapReducebenchmarks we show that the proposed block I/O schedulerresults in about 27% to 52% lesser time for I/O than the currentlyavailable schedulers.
Can Probabilities Be Mimicked by Rules ?

We examine the expressive power of probabilistic context free grammars (PCFGs), with a special focus on the use of probabilities as a filtering mechanism. Probabilities in PCFGs induce an ordering relation among the set of trees yielding a given input sentence. PCFG parsers return the trees bearing the maximum probability for a given sentence, discarding all other possible trees. Obviously, this mechanism can be viewed as a way of defining a new class of tree languages. We formalize the tree language thus defined, study its expressive power, and show that the latter is beyond context freeness.
Cardiac Output Measurements in Septic Patients: Comparing the Accuracy of USCOM to PiCCO

USCOM is an ultrasound-based method which has been accepted for noninvasive hemodynamic monitoring in various clinical conditions (USCOM, Ultrasonic cardiac output monitoring). The present study aimed at comparing the accuracy of the USCOM device with that of the thermodilution technique in patients with septicemia. We conducted a prospective observational study in a medical but noncardiological ICU of a university hospital. Septic adult patients (median age 55 years, median SAPS-II-Score 43 points) on mechanical ventilation and catecholamine support were monitored with USCOM and PiCCO (n = 70). Seventy paired left-sided CO measurements (transaortic access = COUS-A) were obtained. The mean COUS-A were 6.55 l/min (±2.19) versus COPiCCO 6.5 l/min (±2.18). The correlation coefficient was r = 0.89. Comparison by Bland-Altman analysis revealed a bias of −0.36 l/min (±0.99 l/min) leading to a mean percentage error of 29%. USCOM is a feasible and rapid method to evaluate CO in septic patients. USCOM does reliably represent CO values as compared to the reference technique based on thermodilution (PiCCO). It seems to be appropriate in situations where CO measurements are most pertinent to patient management.
Time- and Cost-Efficient Identification of T-DNA Insertion Sites through Targeted Genomic Sequencing

Forward genetic screens enable the unbiased identification of genes involved in biological processes. In Arabidopsis, several mutant collections are publicly available, which greatly facilitates such practice. Most of these collections were generated by agrotransformation of a T-DNA at random sites in the plant genome. However, precise mapping of T-DNA insertion sites in mutants isolated from such screens is a laborious and time-consuming task. Here we report a simple, low-cost and time efficient approach to precisely map T-DNA insertions simultaneously in many different mutants. By combining sequence capture, next-generation sequencing and 2D-PCR pooling, we developed a new method that allowed the rapid localization of T-DNA insertion sites in 55 out of 64 mutant plants isolated in a screen for gyrase inhibition hypersensitivity.
The Role of Learning and Risk Perception in Compliance

Human factors are implicated in most security (and safety) problems, a ubiquitous aspect being erosion of compliance. We discuss several theories of the role of human factors and present system dynamic models based on the theoretical paradigm of instrumental conditioning (the behavioral regulation theory). The proposed mechanism involves learning – both adequate and inadequate, ‘superstitious’ learning – and it conforms to basic facts of human character (propensity to misperceive risk, biological roots of instrumental conditioning). Our generic models are able to render generic reference behavior. Also, they suggest possible reasons for why technological advances paradoxically may worsen human compliance. The concept of the behavioral bliss point – immanent to the behavioral regulation theory – makes the learning aspect an inseparable companion to different mechanisms promoting erosion of compliance (such as throughput/security priority conflicts, mismatch between organizational and personal goal, etc). To counteract erosion of compliance we suggest policies involving educational and social interventions.
Overlay Error Compensation Using Advanced Process Control With Dynamically Adjusted Proportional-Integral R2R Controller

As semiconductor manufacturing reaching nanotechnology, to obtain high resolution and alignment accuracy via minimizing overlay errors within the tolerance is crucial. To address the needs of changing production and process conditions, this study aims to propose a novel dynamically adjusted proportional-integral (DAPI) run-to-run (R2R) controller to adapt equipment parameters to enhance the overlay control performance. This study evaluates the performance of controllers via the variation of each overlay factor and the variation of maximum overlay errors in real settings. To validate the effectiveness of the proposed approach, an empirical study was conducted in a leading semiconductor company in Taiwan and the results showed practical viability of the proposed DAPI controller to reduce overlay errors effectively than conventional exponentially weighted moving average controller used in this company.
Inhibition of RAC1 GTPase sensitizes pancreatic cancer cells to γ-irradiation

Radiation therapy is a staple treatment for pancreatic cancer. However, owing to the intrinsic radioresistance of pancreatic cancer cells, radiation therapy often fails to increase survival of pancreatic cancer patients. Radiation impedes cancer cells by inducing DNA damage, which can activate cell cycle checkpoints. Normal cells possess both a G1 and G2 checkpoint. However, cancer cells are often defective in G1 checkpoint due to mutations/alterations in key regulators of this checkpoint. Accordingly, our results show that normal pancreatic ductal cells respond to ionizing radiation (IR) with activation of both checkpoints whereas pancreatic cancer cells respond to IR with G2/M arrest only. Overexpression/hyperactivation of Rac1 GTPase is detected in the majority of pancreatic cancers. Rac1 plays important roles in survival and Ras-mediated transformation. Here, we show that Rac1 also plays a critical role in the response of pancreatic cancer cells to IR. Inhibition of Rac1 using specific inhibitor and dominant negative Rac1 mutant not only abrogates IR-induced G2 checkpoint activation, but also increases radiosensitivity of pancreatic cancer cells through induction of apoptosis. These results implicate Rac1 signaling in the survival of pancreatic cancer cells following IR, raising the possibility that this pathway contributes to the intrinsic radioresistance of pancreatic cancer.
Training Data Modeling Using Counter Propagation Networks for Improved Generalization Abilities

This paper presents a new method for improved generalization abilities of back propagation networks (BPNs). The method is based on topological data mapping used in counter propagation networks (CPNs). The CPNs save input data into a category map while retaining topological data structures. We used weights and labels of the category map for new training data of the BPN Our method provides the following benefits: 1) the number of training data can be controlled by changing category map sizes; 2) interpolation training data can be produced under the topological space; and 3) overlapping training data can be avoided through the use of Winner-Take-All competition. Experimental results show that expanded training data improved the generalization ability
Practicability of Dataspace Systems

Nowadays there is rarely a scenario where all the data can be fit nicely into one relational database management system or any other single data model. In acknowledgement of this fact a new concept of Dataspaces was introduced, according to which a dataspace system is assumed to be a hybrid of a search engine, a databases management system, an information integration system and a data sharing system. However the concept was presented in a visionary way and its implementation in the real world scenario has opened up many complex research challenges. Moreover so far the efforts put forth by the research community are quite disparate and it is highly desirable to have a unified effort which would hopefully enable rapid progress. Furthermore due to very high end-user expectations of such systems there are a lot of challenges and problems that need to be resolved and much scope for future work remains.
Local Popularity based Page Link Analysis

In this paper we introduce the concept of dynamic link pages. A web site/page contains a number of links to other pages. All the links are not equally important. Few links are more frequently visited and few rarely visited. In this scenario, identifying the frequently used links and placing them in the top left corner of the page will increase the user's satisfaction. This process will reduce the time spent by a visitor on the page, as most of the times, the popular links are presented in the visible part of the screen itself. Also, a site can be indexed based on the popular links in that page. This will increase the efficiency of the retrieval system. We presented a model to display the popular links, and also proposed a method to increase the quality of retrieval system.
Estimation of aggregate agricultural supply response from time series of cross-country data

Do prices determine the supply response in agriculture? This paper attempts to answer this question by utilizing time series, cross country data in a sample of 58 countries for the period 1969-1978. By analyzing the effects of price and various public inputs on the aggregate agricultural supply, the paper discusses two major findings. They include a weak positive supply response from the variations over time for the individual countries (within-country variations). A negative supply response is obtained from the between-country variations. The shifters or public inputs account for most of the variations in supply in the within-country and between-country analysis. Does it mean then that prices have no or almost no role in the determination of supply? The answer is no, but the role is somewhat more difficult to detect.
Succinct Set-Encoding for State-Space Search

We introduce the level-ordered edge sequence (LOES), a suc- cinct encoding for state-sets based on prefix-trees. For use in state-space search, we give algorithms for member testing and element hashing with runtime dependent only on state- size, as well as space and memory efficient construction of and iteration over such sets. Finally we compare LOES to binary decision diagrams (BDDs) and explicitly packed set- representation over a range of IPC planning problems. Our results show LOES produces succinct set-encodings for a wider range of planning problems than both BDDs and ex- plicit state representation, increasing the number of problems that can be solved cost-optimally.
GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based multimodal Geospatial Information Retrieval and Indexing System (GeoIRIS) which includes automatic feature extraction, visual content mining from large-scale image databases, and high-dimensional database indexing for fast retrieval. Using these underpinnings, we have developed techniques for complex queries that merge information from heterogeneous geospatial databases, retrievals of objects based on shape and visual characteristics, analysis of multiobject relationships for the retrieval of objects in specific spatial configurations, and semantic models to link low-level image features with high-level visual descriptors. GeoIRIS brings this diverse set of technologies together into a coherent system with an aim of allowing image analysts to more rapidly identify relevant imagery. GeoIRIS is able to answer analysts' questions in seconds, such as "given a query image, show me database satellite images that have similar objects and spatial relationship that are within a certain radius of a landmark. "
Measured sensitivity of the first mark II phased array feed on an ASKAP antenna

This paper presents the measured sensitivity of CSIRO's first Mk. II phased array feed (PAF) on an ASKAP antenna. The Mk. II achieves a minimum system-temperature-over-efficiency Tsys/η of 78 K at 1.23 GHz and is 95 K or better from 835 MHz to 1.8 GHz. This PAF was designed for the Australian SKA Pathfinder telescope to demonstrate fast astronomical surveys with a wide field of view for the Square Kilometre Array (SKA).
Packing Graphs with ASP for Landscape Simulation

This paper describes an application of Answer Set Programming (ASP) to crop allocation for generating realistic landscapes. The aim is to cover optimally a bare landscape, represented by its plot graph, with spatial patterns describing local arrangements of crops. This problem belongs to the hard class of graph packing problems and is modeled in the framework of ASP. The approach provides a compact solution to the basic problem and at the same time allows extensions such as a flexible integration of expert knowledge. Particular attention is paid to the treatment of symmetries, especially due to sub-graph isomorphism issues. Experiments were conducted on a database of simulated and real landscapes. Currently, the approach can process graphs of medium size, a size that enables studies on real agricultural practices.
An analysis of ambiguity in word sense annotations

Word sense annotation is a challenging task where annotators distinguish which meaning of a word is present in a given context. In some contexts, a word usage may elicit multiple interpretations, resulting either in annotators disagreeing or in allowing the usage to be annotated with multiple senses. While some works have allowed the latter, the extent to which multiple sense annotations are needed has not been assessed. The present work analyzes a dataset of instances annotated with multiple WordNet senses to assess the causes of the multiple interpretations and their relative frequencies, along with the effect of the multiple senses on the contextual interpretation. We show that contextual underspecification is the primary cause of multiple interpretations but that syllepsis still accounts for more than a third of the cases. In addition, we show that sense coarsening can only partially remove the need for labeling instances with multiple senses and we provide suggestions for how future sense annotation guidelines might be developed to account for this need.
Experience in Building a Comparative Performance Analysis Engine for a Commercial System

Performance testing is a standard practice for evolving systems to detect performance issues proactively. It samples various performance metrics that will be compared with a stable baseline to judge whether the measurement data is abnormal. This type of comparative analysis requires domain expertise, which can take experienced performance analysts days to conduct. In an effort to build an automatic solution for a leading data warehousing company to improve the comparative performance analysis efficiency, we implemented machine learning approaches proposed by existing research. But the initial result has a 86% false negative rate on average, which means the majority of performance defects would be missed. To investigate causes for this unsatisfying result, we take a step back to revisit the performance data itself and find several important data related issues that are overlooked by existing work. In this paper, we discuss in detail these issues and share our hindsights to address them. With the new learning scheme we devise, we are able to reduce the false negative rate to as low as 16% and achieve a balanced accuracy of 0.91, which enables the analysis engine to be practically adopted.