Miracle-dz/UniFAR-FTUs huggingface dataset:
Hippocampal–Prefrontal Engagement and Dynamic Causal Interactions in the Maturation of Children's Fact Retrieval

Children's gains in problem-solving skills during the elementary school years are characterized by shifts in the mix of problem-solving approaches, with inefficient procedural strategies being gradually replaced with direct retrieval of domain-relevant facts. We used a well-established procedure for strategy assessment during arithmetic problem solving to investigate the neural basis of this critical transition. We indexed behavioral strategy use by focusing on the retrieval frequency and examined changes in brain activity and connectivity associated with retrieval fluency during arithmetic problem solving in second- and third-grade (7- to 9-year-old) children. Children with higher retrieval fluency showed elevated signal in the right hippocampus, parahippocampal gyrus (PHG), lingual gyrus (LG), fusiform gyrus (FG), left ventrolateral PFC (VLPFC), bilateral dorsolateral PFC (DLPFC), and posterior angular gyrus. Critically, these effects were not confounded by individual differences in problem-solving speed or accuracy. Psychophysiological interaction analysis revealed significant effective connectivity of the right hippocampus with bilateral VLPFC and DLPFC during arithmetic problem solving. Dynamic causal modeling analysis revealed strong bidirectional interactions between the hippocampus and the left VLPFC and DLPFC. Furthermore, causal influences from the left VLPFC to the hippocampus served as the main top–down component, whereas causal influences from the hippocampus to the left DLPFC served as the main bottom–up component of this retrieval network. Our study highlights the contribution of hippocampal–prefrontal circuits to the early development of retrieval fluency in arithmetic problem solving and provides a novel framework for studying dynamic developmental processes that accompany children's development of problem-solving skills.
Completeness and Soundness results for X with Intersection and Union Types ( with 15 extra pages with examples and systems with choice ) (

With the eye on defining a type-based semantics, this paper defines intersection and union type assignment for the sequent calculus X , a substitution-free language that enjoys the Curry-Howard correspondence with respect to the implicative fragment of Gentzen’s sequent calculus for classical logic. We investigate the minimal requirements for such a system to be complete (i.e. closed under redex expansion), and show that the non-logical nature of both intersection and union types disturbs the soundness (i.e. closed uder reduction) properties. This implies that this notion of intersection-union type assignment needs to be restricted to satisfy soundness as well, making it unsuitable to define a semantics. We will look at two (confluent) notions of reduction, called Call-by-Name and Call-by-Value, and prove soundness results for those. keywords: Classical Logic, Sequent Calculus, intersection types, union types.
P3DB: An Integrated Database for Plant Protein Phosphorylation

Protein phosphorylation is widely recognized as the most widespread, enzyme-catalyzed post-translational modification in eukaryotes. In particular, plants have appropriated this signaling mechanism as evidenced by the twofold higher frequency of protein kinases within the genome compared to other eukaryotes. While all aspects of plant protein phosphorylation research have grown in the past 10 years; phosphorylation site mapping using high-resolution mass spectrometry has grown exponentially. In Arabidopsis alone there are thousands of experimentally determined phosphorylation sites. To archive these events in a user-intuitive format we have developed P3DB, the Plant Protein Phosphorylation Database (p3db.org). This database is a repository for plant protein phosphorylation site data, currently hosting information on 32,963 non-redundant sites collated from 23 experimental studies from six plant species. These data can be queried for a protein-of-interest using an integrated BLAST module to query similar sequences with known phosphorylation sites among the multiple plants currently investigated. The paper demonstrates how this resource can help identify functionally conserved phosphorylation sites in plants using a multi-system approach.
Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures

This paper introduces a new structured model for learning anaphoricity detection and coreference resolution in a joint fashion. Specifically,we use a latent tree to represent the full coreference and anaphoric structure of a document at a global level, and we jointly learn the parameters of the two models using a version of the structured perceptron algorithm. Our joint structured model is further refined by the use of pairwise constraints which help the model to capture accurately certain patterns of coreference. Our experiments on the CoNLL-2012 English datasets show large improvements in both coreference resolution and anaphoricity detection, compared to various competing architectures. Our best coreference system obtains a CoNLL score of 81.97 on gold mentions, which is to date the best score reported on this setting.
Jump-Diffusion Models with Correlated Brownian Motion and Compound Poisson Processes

In this paper we provide a closed form option pricing model with underlying uncertainty modeled as an exponential Levy process. The stochastic structure of our model relaxes the restrictive assumption of zero covariance between the Brownian motion and Poisson process jump size found in all traditional jump-diffusion models. The resulting equity diffusion and jump risk premia of the equilibrium consumption capital asset pricing relationship are non-linear functions of time. The option pricing model is developed using Rubinstein’s method of pricing by substitution in equilibrium and it is shown that the sign and magnitude of this covariance plays a crucial role in determining the slope of the implied volatility term structure.
Tube based Quasi-min-max Output Feedback MPC for LPV Systems1

Abstract Output feedback model predictive control of polytopic linear parameter varying (LPV) systems subject to input constraints is considered in this paper. The proposed control scheme incorporates robust observer and robust state feedback control. To handle disturbances and model uncertainty, disturbance invariant tube and quasi-min-max MPC are combined to achieve recursive feasibility and robust stability. Simulation results are provided to verify the effectiveness of the proposed algorithm.
Tool assisted identifier naming for improved software readability: an empirical study

This paper describes an empirical study investigating whether programmers improve the readability of their source code if they have support from a source code editor that offers dynamic feedback on their identifier naming practices. An experiment, employing both students and professional software engineers, and requiring the maintenance and production of software, demonstrated a statistically significant improvement in source code readability over that of the control.
Continuous recognition and visualization of pen strokes and touch-screen gestures

We present a technique that enables continuous recognition and visualization of pen strokes and touch-screen gestures. We describe an incremental recognition algorithm that provides probability distributions over template classes as a function of users' partial or complete stroke articulations. We show that this algorithm can predict users' intended template classes with high accuracy on several different datasets. We use the algorithm to design two new visualizations that reveal various aspects of the recognition process to users. We then demonstrate how these visualizations can help users to understand how the recognition process interprets their input and how interactions between different template classes affect recognition outcomes.
Performance Analysis of Mobile Phone Radiation Minimization Through Characteristic Impedance Measurement

As mobile phones have become more essential and necessary handheld devices in present world generation. Because of the cost reduction and simplicity of mobile phones, the number of mobile users, particularly children, increases drastically within few years. But the effect of mobile Electro Magnetic (EM) radiation on children is more severe than adults. This paper investigates a method to limit radiation of mobile antenna using impedance matching technique by varying the length of antenna. We make use of simulation tool called Computer Simulation Technology (CST) Microwave Studio. The characteristics of impedance of antenna placed inside the TEM (Transverse Electro Magnetic) cell are measured.
High performance computing using ProActive environment and the asynchronous iteration model

This paper presents a new library for the ProActive environment, called AIL-PA (Asynchronous Iterative Library for ProActive). This new library allows to execute programs for solving large scale problems on various architectures. Two models of algorithm can be used: the synchronous iteration model which is efficient on single clusters; the asynchronous iteration model which is more efficient on distributed clusters. Both approaches are tested on both architectures, using Kernel CG of the NAS Parallel Benchmarks on the Grid'5000 platform. These tests also allow us to compare ProActive with AIL-PA and with the Jace programming environment. The results show that the asynchronous iteration model with AIL-PA is more efficient on distributed clusters than the synchronous iteration model. Moreover, these experiments also show that AIL-PA does not involve additional overhead to ProActive.
Fadable scaffolding for investigative learning with web resources

In investigative learning with Web resources, learners are required to build a learning scenario, which explains what/how to investigate. However, it is difficult for them to build it concurrent with learning the resources. The main issue addressed in this paper is how to activate the learning scenario building in order to develop skill in investigative learning. This paper discusses a fadable scaffolding method for the skill development, in which parts of the learning scenario to be built are presented as a scaffold and are faded in accordance with their skill.
How much influence do economics professors have on rankings? The case of Australia and New Zealand

This study ranks Australian and New Zealand economics teaching departments on the basis of the research productivity of its economics professors in economics teaching departments using quality adjusted journal articles listed on the ECONLIT database for the periods 1988 to 2002 and for 1996 to 2002. The per capita research productivity of professors is highest for University of Melbourne, University of Western Australia and University of Canterbury. For a number of economics departments, the per capita research productivity is lower than the research productivity of all faculty members, using a number of criteria for 1988–2002 and 1996–2002. These universities are University of Auckland, Royal Melbourne Institute of Technology University, Griffith University and Macquarie University.
An Introduction to Mathematical Finance: Options and Other Topics

1. Probability 2. Normal random variables 3. Geometric Brownian motion 4. Interest rates and present value analysis 5. Pricing contracts via arbitrage 6. The arbitrage theorem 7. The Black-Scholes formula 8. Valuing by expected utility 9. Exotic options 10. Beyond geometric Brownian motion models 11. Autoregressive models and mean reversion.
A Leisurely Reading of the Life Cycle Consumption Data

A puzzle in consumption theory is the observation of a hump in age-consumption profiles. We study a general equilibrium life-cycle economy with capital in which households include both consumption and leisure in their period utility function. We calibrate the model and find that a significant hump in life-cycle consumption is a feature of the equilibrium. Thus inclusion of leisure in household preferences may provide part of the explanation of observed life-cycle consumption humps.
Test Sequence Generation from Communicating UML State Charts: An Industrial Application of Symbolic Transition Systems

Today's multimedia solutions in the automotive industry are complex and distributed hardware/software systems that interact with a multitude of different environments. Assuring the functional correctness of such software-enabled systems is a major issue to maintain and improve overall product quality. In this article we report on a test case generation approach, that allows engineers to employ well-known UML state chart models to deduce test cases fully automatically. Our approach is based on symbolic transition systems (STSs) and overcomes the intricacies resulting from straightforward application of enumerative test models (e.g. state space explosion). The derivation of the test sequences is performed by searching paths through the STS models with respect to their communication structure. By calculating the weakest preconditions of these paths the validity of the paths (and their related parameters) is ensured. Notably, our first empirical results on an industry showcase - a flashing indicator model developed with our industry partner - indicate that our model composition approach is applicable for both - systematic as well as randomized test case generation.
A neural network left-inversion flux estimation for induction motor filed-oriented control

This paper presents a new rotor flux estimation algorithm using neural network for induction motors, based on the left-inversion method. Using the fifth order model of the three-phase induction machines in a stationary two axes reference frame, a rotor flux "assumed inherent sensor" is constructed and its left-invertible is validated. The ANN left-inversion flux estimator is composed of two relatively independent parts - a static ANN used to approximate the complex nonlinear function and several differentiators used to represent its dynamic behaviors, so that the ANN left-inversion is a special kind of dynamic ANN in essence. The performance of the proposed algorithm is tested through simulation, proving the driven system has good behavior both in transient and steady-state operating conditions.
Zombie-cookies: Case studies and mitigation

Putting the user in charge of their online identity is a necessary precondition for privacy on the web. This requires the ability to manage identifying information that remote parties store in data repositories on the user's device. We performed a crawl of thousands of popular websites with the intention of assessing the prevalence of various problematic patterns of clientside data storage. The focus of this paper is on techniques that make use of the browser cache, especially when they impede active identity management by the user. From these results, we derive a novel privacy-enhancing policy for client-side data storage - self-destructing identifiers - improving the current defacto standard. This policy has already seen some adoption in practice in the form of a Firefox add-on that we developed as a proof of concept.
Physical Activity, Health Benefits, and Mortality Risk

A plethora of epidemiologic evidence from large studies supports unequivocally an inverse, independent, and graded association between volume of physical activity, health, and cardiovascular and overall mortality. This association is evident in apparently healthy individuals, patients with hypertension, type 2 diabetes mellitus, and cardiovascular disease, regardless of body weight. Moreover, the degree of risk associated with physical inactivity is similar to, and in some cases even stronger than, the more traditional cardiovascular risk factors. The exercise-induced health benefits are in part related to favorable modulations of cardiovascular risk factors observed by increased physical activity or structured exercise programs. Although the independent contribution of the exercise components, intensity, duration, and frequency to the reduction of mortality risk is not clear, it is well accepted that an exercise volume threshold defined at caloric expenditure of approximately 1,000 Kcal per week appears to be necessary for significant reduction in mortality risk. Further reductions in risk are observed with higher volumes of energy expenditure. Physical exertion is also associated with a relatively low and transient increase in risk for cardiac events. This risk is significantly higher for older and sedentary individuals. Therefore, such individuals should consult their physician prior to engaging in exercise. “Walking is man’s best medicine” Hippocrates
Design Method of Logical Topologies in WDM Network with Quality of Protection

According to a rapid growth of the bandwidth capacity of the WDM network, Traffic loss due to the failure of the network components is becoming unacceptable. Against this problem, protection preparing backup pathes for the working paths is now considered. By protection, the traffic traversing the damaged component is immediately switched to the backup paths. In this paper, we newly introduce a notion of QoP (Quality of Protection), which is one realization of QoS suitable to the WDM network. We denote QoP as a processing time from the occurrence of the failure to the end of recovery time of paths. We propose two heuristic algorithms to design the logical topology satisfying QoP requirements from every traffic. Its objective is to minimize the number of wavelengthes on the fiber in the logical topology. We finally compare those algorithms through numerical evaluation.
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays

In this letter, the global asymptotic stability analysis problem is considered for a class of stochastic Cohen-Grossberg neural networks with mixed time delays, which consist of both the discrete and distributed time delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the addressed stochastic Cohen-Grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two LMIs are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also pointed out that the main results comprise some existing results as special cases. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria
Recognizing face images with disguise variations

Automatic face recognition, required in law enforcement applications such as surveillance, border security and forensic investigation, is a process in which an individual is identified or verified based on facial characteristics. Researchers have proposed several algorithms that can effectively recognize individuals in controlled environment with minor variations in pose, expression, and illumination (Zhao et al., 2003), (Li & Jain, 2005), (Wechsler, 2006), (Delac & Grgic, 2007). In recent face recognition test reports such as FRVT 2002, FRGC 2004, and FRGC 2006 (Philips et al., 2006 & Philips et al., 2007), the results show that under normal changes in constrained environment, the performance of existing face recognition systems is greatly enhanced. However, in most real world applications, images may not be of good quality or user may not be cooperative or there may be temporal variations and dissimilarities in facial characteristics that are artificially created using disguise accessories. Challenges in automatic face recognition can be classified into six categories: illumination, image quality, expression, pose, aging, and disguise. Among these challenges, recognition of faces with disguise is a major challenge and has only been recently addressed by few researchers (Alexander & Smith, 2003), (Ramanathan et al, 2004), (Silva & Rosa, 2003), (Singh et al., 2008). As shown in Fig. 1, the inter-personal and intra-personal characteristics can be modeled using disguise accessories to alter the appearance of an individual, to impersonate another person, or to hide one’s identity. For example, a criminal can alter facial features and appearance using makeup tools and accessories to remain elusive from law enforcement. The challenges due to disguise cause change in visual perception, alter actual data, make pertinent facial information disappear, mask features to varying degrees, or introduce extraneous artifacts in the face image. Existing face recognition algorithms may not be able to provide the desired level of security for such cases. In literature, Ramanathan et al. (Ramanathan et al., 2004) studied facial similarity for several variations including disguise by forming two eigenspaces from two halves of the face, one using the left half and other using the right half. From the test image, optimally illuminated half face is chosen and is projected into the eigenspace. This algorithm has been tested on the AR face database (Martinez & Benavente, 1998) and the National Geographic database (Ramanathan et al., 2004) which consists of variations in smile, glasses, and illumination. An accuracy of around 39% for best two matches is reported on the AR database. Silva and Rosa O pe n A cc es s D at ab as e w w w . in te ch w eb . o rg
Conventional vs . Fuzzy modeling of Diagnostic Attributes for Classifying Acute Stroke Cases

Knowledge acquisition techniques investigate ways to pick up information about the way that medical experts work and decide in order to resemble their behaviour and assist them in analysing a patient's condition. Available data can sometimes be complicated and by representing them in crisp or nominal values the diagnosis scheme loses in terms of interpretability by the medical doctor. One of the most important aspects of a methodology in the field of medical diagnosis is the interpretability of the results it produces. Especially the acute stroke sector demands not only accuracy and efficiency, but also comprehensibility and transparency of the derived results. It is hard to describe a patient's overall condition from the parameters that describe his physical condition. Moreover, the doctor has to decide quickly for the proper treatment and therefore to assign a primary class to the patient. Entropy-based induction trees are established data mining techniques, which construct human-like diagnostic rules from data. Although these rules are interpretable, attributes are considered in their crisp values and in some cases we interpret them in categorical ones via segmentation with crisp boundaries. One possible solution presented in this paper is to substitute the crisp field values of attributes, which are important in the diagnostic procedure with their corresponding interpretation via fuzzy sets formulated by the medical expert. We illustrate that this approach has equally good classification power but also gives better output for medical interpretation.
Network inference and hypotheses-generation from single-cell transcriptomic data using multivariate information measures

D evelopmental processes are carefully orchestrated. A multi-cellular organism can only emerge from a single fertilised egg cell because gene expression is robustly regulated in space and time by networks of transcriptional regulators. Single cell transcriptomic data allow us to probe and map these networks in unprecedented detail. Here we develop an information theoretical framework to infer candidate gene (co)regulatory networks and distill mechanistic hypotheses from single cell data. Information theory offers clear advantages for such data, where cell-to-cell variability is all pervasive and sample sizes are large. Higher-order information theoretical functionals capture interactions and dependencies between genes reliably in both in silico and real data.