Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Process in the Rational Analysis of Memory

Abstract The rational analysis of memory (Anderson, 1990) proposes that memory's sensitivity to statistical structure in the environment enables it to optimally estimate the odds that a memory trace will be needed. We have analyzed sources of informational demand in the environment: speech to children and word usage in the front page headlines of the New York Times. In a previous paper (Anderson & Schooler, 1991) we have shown that factors that govern memory performance, including recency, also predict the odds that an item (e.g., a word) will be encountered. In the present paper we develop the theory to make precise predictions about how the odds of encountering an item now varies as a joint function of (1) the statistical associations between the item and elements of the current context and (2) how long it has been since the item was last encountered. The prediction was confirmed environmentally by analyses of the New York Times and speech to children. The corresponding behavioral prediction was tested, using a cued recall task in which the cues were either strongly associated or unassociated to the targets. In contrast to the environmental results, recall performance is more sensitive to the length of the retention interval in the presence of unassociated cues than in the presence of associated cues. Further modeling shows that incorporating estimates of the influence of non-retrieval processes (e.g., reading a word, deciding to respond, etc.) on overall performance reduces the discrepancy between the theoretical predictions and the observed data.
Anti-Ids in Allergy: Timeliness of a Classic Concept

Anti-idiotypic antibodies (anti-ids) are part of natural immune responses with regulatory capacity. Their effect on an antigen-specific, so-called Ab1 antibody response, is dependent on 1) the original antigen, which they mirror, being Ab2 antibodies, and 2) their isotype. In the case of IgE-mediated allergy, natural anti-ids against allergen-specific IgE represent internal images of allergen molecules. A key biologic feature of allergens is that they can crosslink IgE, expressed by B-lymphocytes or passively bound via high affinity receptors to effector cells, which renders cellular activation. Therefore, the IgE cross linking capability of anti-ids determines whether they dampen or enhance immediate-type hypersensitivity. Correspondingly to classic antiallergen blocking IgG antibodies, anti-ids may also interact with inhibitory FcγRIIb receptors and, thereby, down-regulate TH2-type inflammation. Anti-ids and other B-cell epitope mimetics, like mimotopes and DARPins, represent antigen surrogates, which can be used for vaccination. Intriguingly, they may induce antibody responses without activating potentially proinflammatory, antiallergen T-lymphocytes. Taken together, collective evidence suggests that anti-ids, although representing immunologic classics, are a timeless concept in allergology.
Regulation of Axonal Extension and Growth Cone Motility by Calmodulin-Dependent Protein Kinase I

Calcium and calmodulin (CaM) are important signaling molecules that regulate axonal or dendritic extension and branching. The Ca2+-dependent stimulation of neurite elongation has generally been assumed to be mediated by CaM-kinase II (CaMKII), although other members of the CaMK family are highly expressed in developing neurons. We have examined this assumption using a combination of dominant–negative CaMKs (dnCaMKs) and other specific CaMK inhibitors. Here we report that inhibition of cytosolic CaMKI, but not CaMKII or nuclear CaMKIV, dramatically decreases axonal outgrowth and branching in cultured neonatal hippocampal and postnatal cerebellar granule neurons. CaMKI is found throughout the cell cytosol, including the growth cone. Growth cones of neurons expressing dnCaMI or dnCaMKK, the upstream activator of CaMKI, exhibit collapsed morphology with a prominent reduction in lamellipodia. Live-cell imaging confirms that these morphological changes are associated with a dramatic decrease in growth cone motility. Treatment of neurons with 1,8-naphthoylene benzimidazole-3-carboxylic acid (STO-609), an inhibitor of CaMKK, causes a similar change in morphology and reduction in growth cone motility, and this inhibition can be rescued by transfection with an STO-609-insensitive mutant of CaMKK or by transfection with constitutively active CaMKI. These results identify CaMKI as a positive transducer of growth cone motility and axon outgrowth and provide a new physiological role for the CaMKK–CaMKI pathway.
Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques

In this paper, we approximate the empirical findings of Papadamou and Markopoulos (2012) on the NOK/USD exchange rate under a Machine Learning (ML) framework. By applying Support Vector Regression (SVR) on a general monetary exchange rate model and a Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) to extract model structure, we test for the validity of popular monetary exchange rate models. We reach to mixed results since the coefficient sign of interest rate differential is in favor only with the model proposed by Bilson (1978), while the inflation rate differential coefficient sign is approximated by the model of Frankel (1979). By adopting various inflation expectation estimates, our SVR model fits actual data with a small Mean Absolute Percentage Error when an autoregressive approach excluding energy prices is adopted for inflation expectation. Overall, our empirical findings conclude that for a small open petroleum producing country such as Norway, fundamentals possess significant forecasting ability when used in exchange rate forecasting.
Definition and Construction of Multi-prover Zero-Knowledge Argument

Multi-prover zero-knowledge proof is an interesting proof in which a few provers synchronously prove validity of a statement to an verifier, however, the verifier will learn nothing beyond the fact that the statement is correct. We call multi-prover zero-knowledge proof as multi-prover zero-knowledge arguments if all provers are probabilistic polynomial-time participants. In this paper, we give the formal definition of multi-prover zero-knowledge argument and construct it based on discrete logarithm problem (DLP).
Low Power VLSI CMOS design by DCG Technique

-The demand for power-sensitive design has grown significantly in recent years due to tremendous growth in portable applications. Consequently, the need for power efficient design techniques has grown considerably. Several efficient design techniques have been proposed to reduce both dynamic as well as static power in state-of-the-art VLSI circuit applications. With the scaling of technology and the need for higher performance and more functionality, power dissipation is becoming a major bottleneck for microprocessor designs. Clock power is significant in high-performance processors. Deterministic Clock Gating (DCG) technique effectively reduces the clock power. DCG is based on the key observation that for many of the pipelined stages of a modern processor, the circuit block usage in the near future is known a few cycles ahead of time. DCG exploits this advance knowledge to clock-gate the unused blocks. Because individual circuit usage varies within and across applications, not all the circuits are used all the time, giving rise to power reduction opportunity. By ANDing the clock with a gate-control signal, clock-gating essentially disables the clock to a circuit whenever the circuit is not used, avoiding power dissipation due to unnecessary charging and discharging of the unused circuits. Results show that DCG is very effective in reducing clock power. Power consumption is reduced by using this method. As high-performance processor pipelines get deeper and power becomes a more critical factor, DCG’s effectiveness and simplicity will continue to be important.
Comparison of Impulse Osillometry System and Spirometry for Diagnosis of Obstructive Lung Disorders

Background Obstructive lung disease is a growing health problem, especially in developed countries. This study aimed to compare Impulse Osillometry System (IOS) and Spirometry for evaluation of Chronic Obstructive Pulmonary Disease and asthma. Materials and Methods The study groups contained 87 healthy people, 87 asthmatic patients and 56 COPD patients. Spirometry (FVC, FEV1, FEV1/FVC) and IOS (R5, R20, X5) measurements were performed for all the healthy subjects and patients. The results of IOS were compared with spirometric results. Results Significant differences were detected among the 3 groups (control, COPD and asthma) in terms of all the spirometric parameters (FVC, FEV1, FEV1/FVC) and some parameters of IOS (R5,R20,X5) measurements (p<0.05). Among COPD patients, sensitivity for X5 (the best value of IOS measurements in the COPD group) was 76%. Also, in asthmatic patients, sensitivity for R20 (the best value of IOS measurements in the asthma group) was 77%. We found a correlation between R5, R20 and X5 with FEV1 in asthmatic patients, but only R5 had this correlation with FEV1 in COPD patients. Conclusion We concluded that IOS can be an alternative for spirometry in the diagnosis of obstructive lung disease in patients with minimal cooperation. R5 can represent COPD severity.
Alinear weakness in the Klimov-Shamir T-function

Linear equations have always been powerful tools in cryptanalysis. In this paper, we present a general linear equation in the binary alphabet of minimum weight 3 that holds for all state lengths and all shifts of sequences generated by the T-function proposed by Klimov and Shamir. It is surprising that these linear properties exist, and they indicate that the T-functions are not as 'wild' and non-algebraic as claimed by Klimov and Shamir. We also use the equation to propose a simple algebraic attack on cryptographic T-functions
Toward Detecting Collusive Ranking Manipulation Attackers in Mobile App Markets

Incentivized by monetary gain, some app developers launch fraudulent campaigns to boost their apps' rankings in the mobile app stores. They pay some service providers for boost services, which then organize large groups of collusive attackers to take fraudulent actions such as posting high app ratings or inflating apps' downloads. If not addressed timely, such attacks will increasingly damage the healthiness of app ecosystems. In this work, we propose a novel approach to identify attackers of collusive promotion groups in an app store. Our approach exploits the unusual ranking change patterns of apps to identify promoted apps, measures their pairwise similarity, forms targeted app clusters (TACs), and finally identifies the collusive group members. Our evaluation based on a dataset of Apple's China App store has demonstrated that our approach is able and scalable to report highly suspicious apps and reviewers. App stores may use our techniques to narrow down the suspicious lists for further investigation.
Manifold Learning for Semantic Visualization

Visualization of high-dimensional data, such as text documents, is useful to map out the similarities among various data points. In the high-dimensional space, documents are commonly represented as bags of words, with dimensionality equal to the size of the vocabulary. Classical approaches to document visualization directly reduce this into visualizable two or three dimensions, using techniques such as multidimensional scaling. Recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. While aiming for a good fit between the model parameters and the observed data, previous approaches have not considered the local consistency among data instances in terms of the intrinsic geometric structure of the document manifold. We consider the problem of semantic visualization by jointly modeling topics and visualization on the intrinsic document manifold. Each document has both a topic distribution and visualization coordinate. Specifically, we propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces through a regularization framework designed for the semantic visualization task. To validate the efficacy of Semafore, our comprehensive experiments on a number of real-life text datasets of news articles and web pages show that the proposed methods outperform the the state-of-the-art baselines on objective evaluation metrics.
How do the OECD Growth Projections for the G7 Economies Perform

The quality of the OECD's Economic Outlook growth projections was last evaluated in-house at the peak of the previous business cycle, calling for a reassessment. This paper analyses the OECD's annual GDP growth projections for the G7 countries over the period 1991-2006 and compares them with the Consensus Economics forecasts. It shows that OECD growth projections display a number of desirable features: projections for the current year are unbiased and efficient; projection errors tend to shrink as the horizon shortens; and projections are directionally accurate most of the time. Like those produced elsewhere, the OECD projections also suffer from shortcomings: one-year-ahead projections display a positive bias, mainly reflecting a propensity to overpredict during slowdowns; spring one-year-ahead projections are far less informative than autumn ones; and turning points are poorly anticipated one year ahead. Regression tests suggest that the OECD and Consensus add value to naive forecasts for spring current-year and autumn one-year-ahead projections.
Sparse precoding in multicell MIMO systems

In this work, we consider the joint precoding across K distant transmitters (TXs) towards K single-antenna receivers (RXs) and we let the TXs have access to perfect Channel State Information (CSI). Instead of considering the conventional method of clustering to allocate the user's data symbols, we focus on determining the most efficient symbol sharing patterns. Consequently, we optimize directly the user's data symbol allocation subject to a constraint on the total number of user's data bits transmitted through the core network. We develop a novel approach whereby sparse precoders approximating the true precoders are computed. These precoders require only a fraction of the overall symbol sharing overhead while introducing only limited losses. Thereby, allocating the symbols only to their nonzero coefficients leads to very efficient symbol sharing (or routing) algorithms. Furthermore, these algorithms have a much lower complexity that conventional approaches. By simulations, we show that our approach outperforms clustering-based multicell MIMO methods from the literature and that the routing obtained is mainly dependent on the pathloss structure and can be applied using only long term CSI with reduced losses.
Edinburgh Research Explorer Associations between hippocampal morphology, diffusion characteristics, and salivary cortisol in older men

High, unabated glucocorticoid (GC) levels are thought to selectively damage certain tissue types. The hippocampus is thought to be particularly susceptible to such effects, and though ﬁndings from ani-mal models and human patients provide some support for this hypothesis, evidence for associations between elevated GCs and lower hippocampal volumes in older age (when GC levels are at greater risk of dysregulation) is inconclusive. To address the possibility that the effects of GCs in non-pathological ageing may be too subtle for gross volumetry to reliably detect, we analyse associations between salivary cortisol (diurnal and reactive measures), hippocampal morphology and diffusion characteristics in 88 males, aged ∼ 73 years. However, our results provide only weak support for this hypothesis. Though nominally signiﬁcant peaks in morphology were found in both hippocampi across all salivary cortisol measures (standardised ˇ magnitudes < 0.518, p uncorrected > 0.0000003), associations were both positive and negative, and none survived false discovery rate correction. We found one single signiﬁcant associa- tion (out of 12 comparisons) between a general measure of hippocampal diffusion and reactive cortisol slope ( ˇ = 0.290, p = 0.008) which appeared to be driven predominantly by mean diffusivity but did not survive correction for multiple testing. The current data therefore do not clearly support the hypothesis that elevated cortisol levels are associated with subtle variations in hippocampal shape or microstructure in non-pathological older age.
Path planning for mobile robots with leg/wheel hybrid locomotion system on outdoor terrain

As the increasing of the population of leg/wheel robots in field trip, a typical path planning method is needed. This paper proposed a multi-layer strategy based on fuzzy logic for a multi leg/wheel hybrid mobile robot. Three main fuzzy logic controllers are proposed for adjusting direction, velocity and selecting locomotion type. Simulations were made step by step in Matlab. Positive results proved that our method is a possible solution.
Access flexibility, trust and performance in achieving competitiveness: An empirical study of Chinese suppliers and distributors

Purpose – This paper aims to advance research in the challenging area of achieving competitiveness through supply chain flexibility fit and trust development.Design/methodology/approach – A structured survey questionnaire was administered to a potential list of 773 participants resulting in 348 valid responses. An empirical study was conducted on these responses.Findings – Leveraging of flexibility in supply chain access structures and improving of trust relationships can ensure significant performance improvement.Research limitations/implications – The research relies on the use of cross sectional data instead of longitudinal data, thereby limiting the generalisibility of the findings.Practical implications – The strong evidence of relationship between improved performance and capability of supply chain linkages provides Chinese business managers with practical guidelines for improving competitive edge.Originality/value – The value of the paper lies in the originality of the data and context‐relevant fin.
Spectral transitions in networks

We study the level spacing distribution p(s) in the spectrum of random networks. According to our numerical results, the shape of p(s) in the Erds–Rényi (E–R) random graph is determined by the average degree ⟨k⟩ and p(s) undergoes a dramatic change when ⟨k⟩ is varied around the critical point of the percolation transition, ⟨k⟩ = 1. When ⟨k⟩ ≫ 1, the p(s) is described by the statistics of the Gaussian orthogonal ensemble (GOE), one of the major statistical ensembles in Random Matrix Theory, whereas at ⟨k⟩ = 1 it follows the Poisson level spacing distribution. Closely above the critical point, p(s) can be described in terms of an intermediate distribution between Poisson and the GOE, the Brody-distribution. Furthermore, below the critical point p(s) can be given with the help of the regularized Gamma-function. Motivated by these results, we analyse the behaviour of p(s) in real networks such as the internet, a word association network and a protein–protein interaction network as well. When the giant component of these networks is destroyed in a node deletion process simulating the networks subjected to intentional attack, their level spacing distribution undergoes a similar transition to that of the E–R graph.
Improved QMDP Policy for Partially Observable Markov Decision Processes in Large Domains: Embedding Exploration Dynamics

Abstract Artificial Intelligence techniques were primarily focused on domains in which at each time the state of the world is known to the system. Such domains can be modeled as a Markov Decision Process (MDP). Action and planning policies for MDPs have been studied extensively and several efficient methods exist. However, in real world problems pieces of information useful for the process of action selection are often missing. The theory of Partially Observable Mazkov Decision Processes (POMDP’s) covers the problem domain in which the full state of the environment is not directly perceivable by the agent. Current algorithms for the exact solution of POMDP’s are only applicable to domains with a small number of states. To cope with more extended state spaces, a number of methods that achieve sub-optimal solutions exist and among these the QI,IDP approach seems to be the best. We introduce a novel technique, called Explorative [Qtilde]P (EQI-IDP) which constitutes an important enhancement of the [Qtilde]P .
Spatiotemporal change analysis of urban land surface component based on V-I-S Model —A case study in Guangzhou

The components of urban surface are diversified and mainly included building area, vegetation, water body and so on. To give urbanization a quantitative analysis in order to describe and forecast some problems, which occurred in the process of urbanization, the analysis of urban land surface components is important and necessary. One of the recent major advances in urban land surface components analysis is V-I-S (vegetation, impervious surface and soil) model, which is presented for urban land surface components change and deemed the urban as the combo of three components: Vegetation, Impervious surface and Soil. Ignoring water surfaces, the V-I-S model included the most fundamental components of urban land surface cover and may reflect the connotation of urban expansion. Remote sensing have developed a variety of approaches to distinguish surface cover through multi-spectral data and has became the most convenient and popular means to map the V-I-S components in urban area for its wide area coverage and regular orbiting period. As the third biggest city in China, Guangzhou has been in a rapid urbanization process during the past two decades. The V-I-S model is implied in the study to quantify the composition of urban land surface with multi-temporal Landsat images between 1990 and 2009 to explore a spatiotemporal change of urban land surface components. The linear spectral mixture analysis method was used to extract three components in V-I-S model at sub-pixel level. The results show that: temporally, V-I-S proportional changes in the time series reveal different patterns of urban development in different period. Spatially, spatial variation analysis in different districts can highlight regional differences in urban sprawl process. The significant difference of urban expansion pattern is analyzed in the study by assessing the changing abundance and area of vegetation, impervious surface and soil from 1990 to 2009.
Temporary special measures : accelerating de facto equality of women under Article 4(1) UN Convention on the Elimination of All Forms of Discrimination against Women

Article 4(1) UN Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) deals with temporary special (affirmative action) measures aimed at accelerating de facto equality between men and women. With a view to support the CEDAW Committee in the process of drafting a General Recommendation to Article 4(1) and in consultation with the Committee members, an expert seminar on this topic was organised in Maastricht, the Netherlands, in October 2002. The meeting brought together highly qualified experts on the subject of affirmative action from a variety of legal cultures, academic backgrounds and non-governmental organisations. The aim of the meeting was threefold: to provide input for the CEDAW Committee; stimulate the legal debate on the issue of temporary special measures; and contribute towards the promotion of positive action measures in the Netherlands. This volume contains the revised papers and comments that were presented at the meeting. Also included is the report on the seminar with a number of concrete suggestions for a General Recommendation to Article 4(1).