Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling information flow and energy use via adaptive synaptogenesis

The adaptive synaptogenesis algorithm is a mathematically defined, random process that, in its present form, creates a feedforward network of excitatory synapses without supervision. The algorithm is fully local and consists of three separate modification processes: random synapse formation, modification of an existing synapse's strength (both strengthening and weakening), and shedding of very weak synapses. The algorithm is shown to have desirable stability properties; further, the algorithm can be parameterized to control the synaptic energy use by a neuron and to control the net information received by a neuron. In addition to the fundamental mathematics on which the algorithm is based, the interaction of parameter settings with characterized random inputs are described. Finally, specific extensions of the algorithm are suggested.
Robust stability analysis of stochastic genetic regulatory networks with discrete and distributed delay in both mRNA and protein dynamics

This paper addresses robust stability of genetic regulatory networks (GRNs) with stochastic perturbation and discrete and distributed time-varying delays. Aside from discrete delays, there are few results about stability of GRNs with distributed delay. In this paper, noise perturbation and delays have been considered in both mRNA and protein dynamics. Based on Lyapunov functional approach and linear matrix inequality (LMI) techniques, sufficient conditions are established to guarantee the robust stability of genetic regulatory networks. Stability conditions are derived in the form of LMIs, which are very easy to be verified. An example is presented to verify the theoretical results.
Reducing Overhead for Soft Error Coverage in High Availability Systems

—High reliability/availability systems typically use redundant computation and components to achieve detection, isolation and recovery from faults. Chip multiprocessors (CMPs) incorporate multiple identical components on a chip to provide high performance/watt. These identical components can be used in a redundant configuration to build cost-effective high availability systems. Current high availability systems like NonStop and Stratus replicate all components of the system including cores, system circuitry and memory. However, it would likely be infeasible to replicate all the components of a system in low cost commodity CMP-based high availability system. In this paper, we focus on low overhead techniques to detect logic soft errors in high availability systems. We specifically focus on reducing the overhead of replicating 1) memory because it is the most expensive component of a system and 2) cores because the number of cores in a system affects its throughput significantly. Our memory duplication overhead reduction technique is based on the intuition that memory storage itself is protected with ECC and errors in logic (cores, on-chip circuitry) can not propagate to pages that are never written. Therefore, we propose limiting the replication to memory pages that are written. For reducing the core duplication we leverage the fact that only certain applications are availability sensitive in a general purpose environment. We propose to selectively use redundant computation for only availability-sensitive applications. Our techniques can provide significant reduction in overheads for a minor performance penalty. We believe that the combination of these two techniques will improve the economic attractiveness of high availability systems built using commodity CMPs.
Activation of PI3K Signaling in Merkel Cell Carcinoma

Purpose: Merkel cell carcinoma (MCC) is an aggressive cutaneous neuroendocrine tumor, often metastatic at presentation, for which current chemotherapeutic regimens are largely ineffective. As its pathogenesis is still unknown, we hypothesized that deregulation of signaling pathways commonly activated in cancer may contribute to MCC tumorigenesis and may provide insights into targeted therapy approaches for this malignancy. Experimental Design: We retrospectively profiled 60 primary MCC samples using a SNaPshot-based tumor genotyping assay to screen for common mutations in 13 cancer genes. Results: We identified mutations in 9 (15%) MCC primary tumors, including mutations in TP53 (3 of 60) and activating mutations in the PIK3CA gene (6 of 60). Sanger sequencing of the primary MCC tumors detected one additional PIK3CA mutation (R19K) that had not been previously described in cancer. Merkel cell polyoma virus (MCPyV) was detected in 38 (66%) MCC cases and patients with MCPyV-positive cancers showed a trend toward better survival. With one exception, the presence of MCPyV and activating mutations in PIK3CA appeared mutually exclusive. We observed that signaling through the PI3K/pAKT pathway was active in one MCPyV-positive and in all MCPyV-negative MCC cell lines, as evidenced by AKT phosphorylation. Importantly, the presence of a PIK3CA-activating mutation was associated with sensitivity to treatment with ZST474, a specific phosphoinositide 3-kinase (PI3K) inhibitor, and to NVP-BEZ235, a dual PI3K/mTOR inhibitor, targeted agents under active clinical development. Conclusions: PI3K pathway activation may drive tumorigenesis in a subset of MCC and screening these tumors for PIK3CA mutations could help identify patients who may respond to treatment with PI3K pathway inhibitors. Clin Cancer Res; 18(5); 1227–36. ©2012 AACR.
Integrating Affect Sensors in an Intelligent Tutoring System

This project augments an existing intelligent tutoring system (AutoTutor) that helps learners construct explanations by interacting with them in natural language and helping them use simulation environments. The research aims to develop an agile learning environment that is sensitive to a learner’s affective state, presuming that this will promote learning. We integrate state-of-the-art, non-intrusive, affect-sensing technology with AutoTutor in an endeavor to classify emotions on the bases of facial expressions, gross body movements, and conversational cues. This paper sketches our broad theoretical approach, our methods for data collection and evaluation, and our emotion classification techniques.
Localization through Map Stitching in Wireless Sensor Networks

Node localization is an essential problem in ad hoc wireless sensor networks. Map stitching is a type of localization algorithm that has received a great deal of attention recently. In this algorithm, the network is divided into small overlapping sub-regions, each of which creates a local map. Then, the local maps are stitched together to form a single global map. In this paper, we first propose a new technique for map-to-map stitching that exploits every available distances between two maps. Next, we propose a few anchor-free localization algorithms based on our stitching technique. We experimentally studied the performance of our algorithms under various settings. The results show that our method achieves a significant performance improvement upon the existing method.
How much have the wages of unskilled workers in China increased? Data from seven factories in Guangdong

China has experienced unprecedented economic growth for a long time. How long will such a high growth be sustained? The answer to this question, to a large extent, depends on whether China has exhausted its rural surplus labour. In this paper, I use an unique payroll data from seven large manufacturing factories to show that wages of unskilled labour in these factories have not increased significantly, if at all. These findings may shed some light on whether China has reached a "Lewistian Turning Point" and the extent to which Chinese economic growth may be sustainable.
EvalBench: A Software Library for Visualization Evaluation

It is generally acknowledged in visualization research that it is necessary to evaluate visualization artifacts in order to provide empirical evidence on their effectiveness and efficiency as well as their usability and utility. However, the difficulties of conducting such evaluations still remain an issue. Apart from the required know‐how to appropriately design and conduct user studies, the necessary implementation effort for evaluation features in visualization software is a considerable obstacle. To mitigate this, we present EvalBench, an easy‐to‐use, flexible, and reusable software library for visualization evaluation written in Java. We describe its design choices and basic abstractions of our conceptual architecture and demonstrate its applicability by a number of case studies. EvalBench reduces implementation effort for evaluation features and makes conducting user studies easier. It can be used and integrated with third‐party visualization prototypes that need to be evaluated via loose coupling. EvalBench supports both, quantitative and qualitative evaluation methods such as controlled experiments, interaction logging, laboratory questionnaires, heuristic evaluations, and insight diaries.
License plate image classification based on bag of verbs framework

Bag Of computational verbs (BoCV) is a new framework based on computational verb theory. In this framework, verb similarities are summed up or averaged. The value of result is put into a bag as an entry for description in order that many verbs similarities can make up the final feature vector implicitly. A novel model called spatiotemporal verb bag (SVB) is proposed and it is trained in a supervised learning manner for two classes classification problem. While the application of this framework for the same problem is not confined to the proposed model. We compare the classification result of the proposed model with some baseline methods, e.g. histogram of oriented gradient feature with adaptive boosting (HOG-Adaboost) and principal component analysis with support vector machine (PCA-SVM). The proposed algorithm achieves excellent performance in distinguishing Chinese license plates captured from different parts of the day and variety of weather condition.
Identifying contextual information for multi-word term extraction

Methods for multi-word term extraction have traditionally involved statistical techniques. More recently, hybrid techniques have been evolving which incorporate some linguistic knowledge. This information is generally very shallow, and researchers have tended to ignore any real understanding of either terms or the context in which they appear. We adopt an approach which uses a variety of knowledge sources-syntactic, semantic and statistical-and attempt to both enlighten and make use of the theoretical foundations of terminology in a practical application. We seek particularly to identify those parts of the context which are most relevant to the terms. We incorporate contextual weights, based on a new similarity measure, into a method for term recognition, and thereby improve the ranking of terms and enable disambiguation to be achieved.
Under Consideration for Publication in J. Functional Programming from Natural Semantics to C: a Formal Derivation of Two Stg Machines

The Spineless Tag-less G-machine (STG machine) was defined as the target abstract machine for compiling the lazy functional language Haskell. It is at the heart of the Glasgow Haskell Compiler (GHC) which is claimed to be the Haskell compiler that generates the most efficient code. A high-level description of the STG machine can be found at Should the reader be interested in a more detailed view, then the only additional information available is the Haskell code of GHC and the C code of its runtime system. It is hard to prove that this machine correctly implements the lazy semantics of Haskell. Part of the problem lies in the fact that the STG machine executes a bare-bones functional language, called STGL, much lower level than Haskell. Therefore, part of the correctness should be —and it is— established by showing that the translation from Haskell to STGL preserves Haskell's semantics. The other part involves showing that the STG machine correctly implements the lazy semantics of STGL. In this paper we provide a step-by-step formal derivation of the STG machine and of its compilation to C, starting from a natural semantics of STGL. Thus, our starting point is higher level than the descriptions found at (Peyton Jones, 1992) and (Marlow & Peyton Jones, 2004), and our arrival point is lower level than those works. Additionally, there has been substantial changes between the so-called push/enter model of the STG machine described in (Peyton Jones, 1992), and the eval/apply model of the STG machine described in (Marlow & Peyton Jones, 2004). So, in fact, we derive two machines instead of one, starting from the same initial semantics. At each step we provide enough intuitions and explanations in order to understand the refinement, and then the formal definitions and statements proving that the derivation step is sound and complete. The main contribution of the paper is to show that an efficient machine such as the STG can be presented, understood, and formally reasoned about at different levels of abstraction.
CSB.DB: a comprehensive systems-biology database

SUMMARY The open access comprehensive systems-biology database (CSB.DB) presents the results of bio-statistical analyses on gene expression data in association with additional biochemical and physiological knowledge. The main aim of this database platform is to provide tools that support insight into life's complexity pyramid with a special focus on the integration of data from transcript and metabolite profiling experiments. The central part of CSB.DB, which we describe in this applications note, is a set of co-response databases that currently focus on the three key model organisms, Escherichia coli, Saccharomyces cerevisiae and Arabidopsis thaliana. CSB.DB gives easy access to the results of large-scale co-response analyses, which are currently based exclusively on the publicly available compendia of transcript profiles. By scanning for the best co-responses among changing transcript levels, CSB.DB allows to infer hypotheses on the functional interaction of genes. These hypotheses are novel and not accessible through analysis of sequence homology. The database enables the search for pairs of genes and larger units of genes, which are under common transcriptional control. In addition, statistical tools are offered to the user, which allow validation and comparison of those co-responses that were discovered by gene queries performed on the currently available set of pre-selectable datasets. AVAILABILITY All co-response databases can be accessed through the CSB.DB Web server (http://csbdb.mpimp-golm.mpg.de/).
Aligning Large-scale Medical and Biological Data Sets: Exploring a Monkey Brain

This project addresses the issue of developing interactive rendering methods for datasets which cannot be stored on a single hard drive or in main memory anymore. Our dataset is a set of 1400 slices (single cross-sections) of a monkey brain, which has been sliced more than 15 years ago at the Center for Neuroscience at UC Davis, and recently has been scanned at a very high resolution (more than 10MB per image in compressed format). The enormous resolution allows us to zoom from a global view down to the cell level, all in one image. This exciting range of rendering options requires scalable, multiresolution rendering techniques. The challenges we encounter with this data set is an extreme misalignment of the slices due to manual placement onto glass object carriers and manual insertion in the film scanner. We present a semi-automated method which compensates for most of these artifacts and identifies those slices that cannot be handled and aligned automatically. The algorithm reduces the number of slices that need to be treated manually enormously.
A Study of Influential Factors in the Adoption and Diffusion of B2C E-Commerce

This paper looks at the present standing of ecommerce in Saudi Arabia, as well as the challenges and strengths of Business to Customers (B2C) electronic commerce. Many studies have been conducted around the world in order to gain a better understanding of the demands, needs and effectiveness of online commerce. A study was undertaken to review the literature identifying the factors influencing the adoption and diffusion of B2C e-commerce. It found four distinct categories: businesses, customers, environmental and governmental support, which must all be considered when creating an e-commerce infrastructure. A concept matrix was used to provide a comparison of important factors in different parts of the world. The study found that e-commerce in Saudi Arabia was lacking in Governmental support as well as relevant involvement by both customers and retailers.
Clustering high dimensional data streams at multiple time granularities

In this paper, we extend our DGStream (dense grid-tree based data stream clustering) method which is developed recently [Yan Xiaolong, et al., 2007] and propose a new method DGMStream (dense grid-tree based multiple time granularity adaptable data stream clustering) to cluster dynamic data streams. In DGMStream, we incorporate the technique of tilted time window in DGStream to find clusters for data streams over multiple time granularities. Implementation results show that this method has a better cluster purity and scalability than other methods.
Higher dimensional algebras via colored PROPs

Starting from any unital colored PROP $P$, we define a category $P(P)$ of shapes called $P$-propertopes. Presheaves on $P(P)$ are called $P$-propertopic sets. For $0 \leq n \leq \infty$ we define and study $n$-time categorified $P$-algebras as $P$-propertopic sets with some lifting properties. Taking appropriate PROPs $P$, we obtain higher categorical versions of polycategories, 2-fold monoidal categories, topological quantum field theories, and so on.
Physical Activity and Adherence to Mediterranean Diet Increase Total Antioxidant Capacity: The ATTICA Study

We studied the association of physical activity and adherence to the Mediterranean diet, in total antioxidant capacity (TAC). A random sample of 1514 men and 1528 women was selected from Attica region. Physical activity was assessed with a translated version of the validated “International Physical Activity Questionnaire” (iPAQ), and dietary intake through a validated Food Frequency Questionnaire (FFQ). Adherence to the Mediterranean diet was assessed by the MedDietScore that incorporated the inherent characteristics of this diet. TAC was positively correlated with the degree of physical activity (P < .05). TAC was also positively correlated with MedDietScore (r = 0.24, P < .001). Stratified analysis by diet status revealed that the most beneficial results were observed to highly active people as compared to inactive, who also followed the Mediterranean diet (288 ± 70 μmol/L, 230 ± 50 μmol/L, resp.), after adjusting for various confounders. Increased physical activity and greater adherence to the Mediterranean diet were associated with increased total antioxidant capacity.
An interval method for global inequality-constraint optimization problems

An interval method is suggested for globally solving optimization problems of the following type: minimize a given objective function subject to both functional inequality constraints and simple bounds on the variables. The present method appeals to a new interval linearization of each nonlinear function and is based, essentially, on two computation techniques: linear programming and constraint propagation. The use of these techniques in the computational scheme of the present method seems to lead to improved performance as compared to other known interval methods of the same class.
Dividend Taxes and International Portfolio Choice

Abstract This paper investigates how dividend taxes influence portfolio choices, using the response to the distinctive treatment of a subset of foreign dividends in the Jobs and Growth Tax Relief Reconciliation Act (JGTRRA) of 2003. An open-economy after-tax capital asset pricing model is used to derive the hypothesis that JGTRRA should lead to a portfolio reallocation by U.S. investors toward equities in tax-favored countries. A difference-in-difference analysis that compares U.S. equity holdings in affected and unaffected countries finds a substantial portfolio reallocation toward the former. This effect cannot be explained by several potential alternative hypotheses, including differential changes to the preferences of American investors, differential changes in investment opportunities, differential time trends in investment, changed tax evasion behavior, or changes in stock prices associated (or contemporaneous) with JGTRRA.
Outputs of human walking for bipedal robotic controller design

This paper presents a method to determine outputs associated with human walking data that can be used to design controllers that achieve human-like robotic walking. We consider a collection of human outputs, i.e., functions of the kinematics computed from experimental human data, that satisfy criteria necessary for human-inspired bipedal robot control construction. These human outputs are described in a form amendable to controller design through a special class of time based functions - termed canonical walking functions. An optimization problem is presented to determine the parameters of this controller that yields the best fit to the human data that simultaneously produces stable robotic walking. The optimal value of the cost function is used as a metric to determine which human outputs result in the most “human-like” robotic walking. The human-like nature of the resulting robotic walking is verified through simulation.
Oxidative DNA damage in prostate cancer patients consuming tomato sauce-based entrees as a whole-food intervention.

BACKGROUND Human prostate tissues are vulnerable to oxidative DNA damage. The risk of prostate cancer is lower in men reporting higher consumption of tomato products, which contain high levels of the antioxidant lycopene. We examined the effects of consumption of tomato sauce-based pasta dishes on lycopene uptake, oxidative DNA damage, and prostate-specific antigen (PSA) levels in patients already diagnosed with prostate cancer. METHODS Thirty-two patients with localized prostate adenocarcinoma consumed tomato sauce-based pasta dishes for the 3 weeks (30 mg of lycopene per day) preceding their scheduled radical prostatectomy. Serum and prostate lycopene concentrations, serum PSA levels, and leukocyte DNA oxidative damage (ratio of 8-hydroxy-2'-deoxyguanosine [8-OHdG] to 2'-deoxyguanosine [dG]) were assessed before and after the dietary intervention. DNA oxidative damage was assessed in resected prostate tissue from study participants and from seven randomly selected prostate cancer patients. All statistical tests were two-sided. RESULTS After the dietary intervention, serum and prostate lycopene concentrations were statistically significantly increased, from 638 nM (95% confidence interval [CI] = 512 to 764 nM) to 1258 nM (95% CI = 1061 to 1455 nM) (P<.001) and from 0.28 nmol/g (95% CI = 0.18 to 0.37 nmol/g) to 0.82 nmol/g (95% CI = 0.57 to 1.11 nmol/g) (P <.001), respectively. Compared with preintervention levels, leukocyte oxidative DNA damage was statistically significantly reduced after the intervention, from 0.61 8-OHdG/10(5) dG (95% CI = 0.45 to 0.77 8-OHdG/10(5) dG) to 0.48 8-OHdG/ 10(5) dG (95% CI = 0.41 to 0.56 8-OHdG/10(5) dG) (P =.005). Furthermore, prostate tissue oxidative DNA damage was also statistically significantly lower in men who had the intervention (0.76 8-OHdG/10(5) dG [95% CI = 0.55 to 0.96 8-OHdG/10(5) dG]) than in the randomly selected patients (1.06 8-OHdG/10(5) dG [95% CI = 0.62 to 1.51 8-OHdG/10(5) dG]; P =. 03). Serum PSA levels decreased after the intervention, from 10.9 ng/mL (95% CI = 8.7 to 13.2 ng/mL) to 8.7 ng/mL (95% CI = 6.8 to 10.6 ng/mL) (P<.001). CONCLUSION These data indicate a possible role for a tomato sauce constituent, possibly lycopene, in the treatment of prostate cancer and warrant further testing with a larger sample of patients, including a control group.