Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic and prognostic accuracy of miR-21 in renal cell carcinoma: a systematic review protocol

Introduction Renal cell carcinoma (RCC) is the most common neoplasm in adult kidneys. One of the most important unmet medical needs in RCC is a prognostic biomarker to enable identification of patients at high risk of relapse after nephrectomy. New biomarkers can help improve diagnosis and hence the management of patients with renal cancer. Thus, this systematic review aims to clarify the prognostic and diagnostic accuracy of miR-21 in patients with RCC. Methods and analysis We will include observational studies evaluating the diagnostic and prognostic roles of miR-21 in patients with renal cancer. The index test and reference standards should ideally be performed on all patients. We will search PubMed, SCOPUS and ISI Web of Science with no restriction of language. The outcome will be survival measures in adult patients with RCC. Study selection and data extraction will be performed by two independent reviewers. QUADAS-1 will be used to assess study quality. Publication bias and data synthesis will be assessed by funnel plots and Begg's and Egger's tests using Stata software V.11. 1. Ethics and dissemination No ethical issues are predicted. These findings will be published in a peer-reviewed journal and presented at national and international conferences. Trail registration number This systematic review protocol is registered in the PROSPERO International Prospective Register of Systematic Reviews, registration number CRD42015025001.
Visualiz'em: "show me more about him!"

Our Personal Information is now scattered among several applications and personal devices in an unrelated, but still connected, information network. We have never had so much information at our disposal as we have now, giving us the opportunities to find information about other people, a very frequent need. While previous works have proposed several solutions to relate data from multiple sources that does not happen at the presentation level. When needing information about people, we still have to navigate among sources to find it. Besides troublesome, it is difficult to have an overall characterization of what each person represents to us. We argue that gathering information from all these sources and presenting it in a unified interface benefits the user by providing a quick and meaningful overview of who that person is and how he/she is related to him/her. We present Visualiz'em, a personal visualization tool based on three interconnected views: Profile, Tagcloud and Timeline. When compared to traditional applications Visualiz'em provides a faster and richer overview of whom a person is and his/her relationship with the user. Moreover, results show that our visualization tool promotes serendipitous behaviors, allowing users to easily explore data and find interaction patterns.
N3ASICs: Designing nanofabrics with fine-grained CMOS integration

We propose a novel nanofabric approach that mixes unconventional nanomanufacturing with CMOS manufacturing flow and design rules in order to build a reliable nanowire-CMOS fabric called N3ASIC with no new manufacturing constraints added. Active devices are formed on a dense uniform semiconductor nanowire array and standard area distributed pins/vias; metal interconnects route the signals in 3D. CMOS design rules are followed. Novel nanowire based devices are envisioned and characterized based on 3D physics modeling. Overall N3ASIC fabric design, associated circuits, interconnection approach, and a layer-by-layer assembly sequence for the fabric are introduced. Key system level metrics such as power, performance, and density for a nanoprocessor design built using N3ASICs were evaluated and compared against a functionally equivalent CMOS design synthesized with state-of-the-art CAD tools. We show that the N3ASICs version of the processor is 3X denser and 5X more power efficient for a comparable performance than the 16-nm scaled CMOS version even without any new/unknown-manufacturing requirement added.
Finding Clusters Within a Class to Improve Classification Accuracy

In machine learning, classification is defined as the task of taking an instance of the dataset and assigning it to a particular class. Classifiers are constructed using the training set, such that a novel instance is labeled with the “correct” class. Usually, there is the underlying assumption that instances within a class are similar and instances across classes are dissimilar. For example, given a dataset of images of cars and bicycles, the shapes and appearances of the two classes are different. While this is a valid assumption, in reality, there may also be differences within a particular class, although it may be less pronounced. For example, the car class may be composed of side-view, front-view, and rear-view images of cars. Therefore, variations can be found within each class and homogeneous groups can be formed for each variation. Then, each group can be considered to be a “sub-class” for training a classifier that can focus on the specific aspect of the class.
A pornographic web page detecting method based on SVM model using text and image features

In this paper, a new web page filtering method directed at pornographic material and based on image and text content analysis is proposed. First, the features of image and text are extracted from the web page content. An analysis is performed and the results are merged. The page is then rated pornographic or non-pornographic. To facilitate real-time analysis, we also propose an acceleration method. In experimental results, text classification accuracy is 95.8% and image classification accuracy is 84%. In addition, the accuracy of web page classification after merging analysis of text and image features is 91.8%.
Oxide Semiconductor Phototransistor with Organolead Trihalide Perovskite Light Absorber

A hybrid phototransistor is developed with solution‐processed organolead trihalide perovskite (MAPbI3) capping indium gallium zinc oxide (IGZO), which well fuses the properties of the two materials in sensitive photodetecting and high‐mobility charge transporting, respectively. The MAPbI3‐capped IGZO phototransistor demonstrates excellent responsivities of over 25 mA W−1 for lights with photon energies above the bandgap of perovskite light absorber. Besides the high sensitivity to light in both ultraviolet and visible regions, hybrid phototransistor maintains a fair on/off ratio of over 106 in the dark, and a field effect mobility of 12.9 cm2 V−1 s−1. The perovskite light absorber also obviates the long‐standing problem for metal oxide phototransistor, the persistent photoconductivity behavior. Furthermore, fast transient response has been achieved by showing rise‐time and fall‐time within tens of milliseconds. The newly developed device opens variable optic‐electric sensing applications for the integrated oxide–perovskite hybrid phototransistors.
Non-Linear Analysis of Concrete Frames Using a Direct Stiffness Line Element Approach

A computationally efficient method of non-linear analysis is described for reinforced concrete frames. The Crisfield arc-length solution procedure is used together with a previously developed line element formulation which takes account of both the material non-linearities and the geometric non-linear effects. The resulting method has been found to be more reliable and more efficient than the previously-used methods which relied on load-control and displacement-control procedures. Results obtained from the analysis of several columns and frames are compared with test results reported in the literature.
Polar codes are optimal for lossy source coding

We consider lossy source compression of a binary symmetric source with Hamming distortion function. We show that polar codes combined with a low-complexity successive cancellation encoding algorithm achieve the rate-distortion bound. The complexity of both the encoding and the decoding algorithm is O(N log(N)), where N is the blocklength of the code. Our result mirrors Ar¿kan's capacity achieving polar code construction for channel coding.
Server-assisted generation of a strong secret from a password

A roaming user, who accesses a network front different client terminals, can be supported by a credentials server that authenticates the user by password then assists in launching a secure environment for the user. However, traditional credentials server designs are vulnerable to exhaustive password guessing attack at the server. We describe a credentials server model and supporting protocol that overcomes that deficiency. The protocol provides for securely generating a strong secret from a weak secret (password), based on communications exchanges with two or more independent servers. The result can be leveraged in various ways, for example, the strong secret can be used to decrypt an encrypted private key or it can be used in strongly authenticating to an application server. The protocol has the properties that a would-be attacker cannot feasibly complete the strong secret and has only a limited opportunity to guess the password, even if he or she has access to all messages and has control over some, but not all, of the servers.
An Efficient Distributed Event Scheduling Algorithm for Large Scale Wireless Communications Simulation Modelling

An improved model is presented in this paper for simulating resource allocation strategies in large scale wireless communications based on new distributed event scheduling mechanisms involving real time systems methodologies. It is herein shown that such mechanisms are more suitable than the currently utilized calendar queue algorithm for simulating concurrent events in wireless networks. The proposed simulation model is based on a novel distributed real time event scheduling algorithm extending the known in discrete event simulation methodology, calendar queue algorithm. The new algorithm is analyzed and its implementation issues involving multithreading technology are discussed. The application of the new scheme in modeling wireless cellular networks shows significant enhancements with regards to network modeling performance compared to rival simulation modeling techniques.
Enhanced Wear-Rate Leveling for PRAM Lifetime Improvement Considering Process Variation

The limited write endurance is one of the major obstacles for phase-change random access memory (PRAM)-based main memory. Traditionally, wear-leveling (WL) techniques were proposed to enhance its lifetime by balancing write traffic. However, these techniques do not concern the endurance variation in PRAM chips. When different PRAM cells have distinct endurance, balanced writes results in lifetime degradation due to the weakest cells. In this paper, we first define a new metric-wear rate (i.e., writes/endurance) considering both the write traffic and endurance distribution from application and hardware, respectively. After investigating the writing behavior of applications and endurance variation, we propose an architecture-level leveling mechanism to balance wear rate of cells across the PRAM chip. Hardware and algorithm to support the proposed leveling mechanism are presented. Moreover, there is an important tradeoff between endurance improvement and swapping data volume. To co-optimize endurance and swapping, this situation is formulated as a maximum weight perfect matching problem in bipartite graph. Thereafter, a novel algorithm that minimizes wear-rate and swapping by employing Kuhn-Munkras algorithm is proposed to maximize PRAM lifetime and minimize performance degradation. The experimental results show ~17× lifetime improvement over prior WL.
Anterior temporal face patches: a meta-analysis and empirical study

Evidence suggests the anterior temporal lobe (ATL) plays an important role in person identification and memory. In humans, neuroimaging studies of person memory report consistent activations in the ATL to famous and personally familiar faces and studies of patients report resection or damage of the ATL causes an associative prosopagnosia in which face perception is intact but face memory is compromised. In addition, high-resolution fMRI studies of non-human primates and electrophysiological studies of humans also suggest regions of the ventral ATL are sensitive to novel faces. The current study extends previous findings by investigating whether similar subregions in the dorsal, ventral, lateral, or polar aspects of the ATL are sensitive to personally familiar, famous, and novel faces. We present the results of two studies of person memory: a meta-analysis of existing fMRI studies and an empirical fMRI study using optimized imaging parameters. Both studies showed left-lateralized ATL activations to familiar individuals while novel faces activated the right ATL. Activations to famous faces were quite ventral, similar to what has been reported in previous high-resolution fMRI studies of non-human primates. These findings suggest that face memory-sensitive patches in the human ATL are in the ventral/polar ATL.
Extending two-variable logic on data trees with order on data values and its automata

Data trees are trees in which each node, besides carrying a label from a finite alphabet, also carries a data value from an infinite domain. They have been used as an abstraction model for reasoning tasks on XML and verification. However, most existing approaches consider the case where only equality test can be performed on the data values. In this article we study data trees in which the data values come from a linearly ordered domain, and in addition to equality test, we can test whether the data value in a node is greater than the one in another node. We introduce an automata model for them which we call ordered-data tree automata (ODTA), provide its logical characterisation, and prove that its non-emptiness problem is decidable in 3-NExpTime. We also show that the two-variable logic on unranked data trees, studied by Bojanczyk et al. [2009], corresponds precisely to a special subclass of this automata model. Then we define a slightly weaker version of ODTA, which we call weak ODTA, and provide its logical characterisation. The complexity of the non-emptiness problem drops to NP. However, a number of existing formalisms and models studied in the literature can be captured already by weak ODTA. We also show that the definition of ODTA can be easily modified, to the case where the data values come from a tree-like partially ordered domain, such as strings.
Minding the Gap - An Expectation-Disconfirmation Approach to Reward-Based Crowdfunding

The remarkable growth of crowdfunding has sparked academic interest. By collecting small contributions from a large number of individuals, entrepreneurs, artists and other individuals and organizations can raise considerable amounts of money. Despite the increasing academic interest in this topic, there still appears to be a lack of understanding regarding the factors that determine individuals' intentions to contribute to projects. In this research-in-progress paper, we propose a research model that examines future funding intentions by using Expectation-Disconfirmation Theory (EDT). By applying our research model to reward-based crowdfunding, we intend to shed light on the ways in which previous funding experiences influence future funding intentions. Through the data collected by this study, our research will contribute to the body of literature on reward-based crowdfunding and EDT. It will also provide practical implications for crowdfunding platforms and project initiators, providing them indications for their management of expectations.
A real option analysis approach to evaluating digital government investment

Real option analysis has been studied and applied in evaluating information technology (IT) investments for more than a decade. Its advantages over the traditional discounted cash flow techniques have been widely recognized in the information systems (IS) community. However real option analysis has yet to be seen in valuing digital government projects where the real option analysis and option thinking seem to be even more applicable. This study endeavors to fill this void by developing a digital government investment evaluation framework centered on real option analysis, and applying a recently developed option pricing model to a real world digital government project.
Object-Oriented Representation and Hierarchical Reinforcement Learning in Infinite Mario

In this work, we analyze and improve upon reinforcement learning techniques used to build agents that can learn to play Infinite Mario, an action game. We extend the object-oriented representation by introducing the concept of object classes which can be effectively used to constrain state spaces. We then use this representation combined with the hierarchical reinforcement learning model as a learning framework. We also extend the idea of hierarchical RL by designing a hierarchy in action selection using domain specific knowledge. With the help of experimental results, we show that this approach facilitates faster and efficient learning for this domain.
Including people with disabilities in actions to reduce poverty and hunger

According to the United Nations (UN), there are approximately 650 million people with disabilities in the world, and at least 80 percent of them live in developing countries. More often than not, they are among the poorest of the poor. The UN Convention on the Rights of Persons with Disabilities, adopted in 2006, defines such persons as “those who have long-term physical, mental, intellectual or sensory impairments which in interaction with various barriers may hinder their full and effective participation in society on an equal basis with others” (Article 1). Recent World Bank estimates suggest that as many as one in five of the world’s poorest people has a disability. While prevalence is difficult to measure effectively, it appears that about 10–12 percent of the population in developing countries lives with disabilities, 2–4 percent with severe disabilities. Males at all ages have higher levels of disability, and the prevalence increases dramatically across the world in people older than 60. People with disabilities are not a homogeneous group. The following groups are particularly vulnerable:
A theoretical retention model for unsaturated uniform soils

The present study focuses on a theoretical model for unsaturated soils based on elastic spherical particle arrangement, which can be considered to be a simplified Discrete Element Method. As a first step, a uniform model is introduced with a single soil particle diameter. This approach follows a new research methodology so that the physical relations between soil particles, interstitial water and air are used. With such a model, the number of experimental parameters needed will be limited. The theoretical model takes into account the different arrangement of soil particles so that the entire voids ratio scale is covered. The wetting and drying paths are modelled. A comparison with previous theoretical model and the proposed new theory is achieved on experimental results with two uniform glass samples. It shows that this new theory improves the precision of the prediction, using only simple physical parameters.
The glutamate receptor subunit delta2 is capable of gating its intrinsic ion channel as revealed by ligand binding domain transplantation

The family of ionotropic glutamate receptors includes 2 subunits, delta1 and delta2, the physiological relevance of which remains poorly understood. Both are nonfunctional in heterologous expression systems, although the isolated, crystallized ligand binding domain (LBD) of delta2 is capable of binding D-serine. To investigate these seemingly contradictory observations we tested whether delta receptors can be ligand gated at all. We used a strategy that replaced the native LBD of delta2 by a proven glutamate-binding LBD. Test transplantations between α-amino-3-hydroxy-5-methylisoxazole propionate (AMPA) and kainate receptors (GluR1 and GluR6, respectively) showed that this approach can produce functional chimeras even if only one part of the bipartite LBD is swapped. Upon outfitting delta2 with the LBD of GluR6, the chimera formed glutamate-gated ion channels with low Ca2+ permeability and unique rectification properties. Ligand-induced conformational changes can thus gate delta2, suggesting that the LBD of this receptor works fundamentally differently from that of other ionotropic glutamate receptors.
Efficient and User-Friendly Verification

A compositional verification method from a high-level resource-management standpoint is presented for dense-time concurrent systems and implemented in the tool of SGM (State-Graph Manipulators) with graphical user interface. SGM packages sophisticated verification technology into state-graph manipulators and provides a user interface which views state-graphs as basic data-objects. Hence, users do not have to be verification theory experts and do not have to trace inside state-graphs to analyze state and path properties to make the best use of verification theory. Instead, users can construct their own verification strategies based on observation on the state-graph complexity changes after experimenting with some combinations of manipulators. Moreover, SGM allows users to control the complexity of state-graphs through iterative state-graphs merging and reductions before they become out of control, Reduction techniques specially designed for the context of state-graph iteration composition and shared variable manipulations are developed and used in SGM. Experiments on different benchmarks to show SGM performance are reported. An algorithm based on group theory to pick a manipulator combination is presented.
Shift and Mean Algorithm for Functional Imaging with High Spatio-Temporal Resolution

Understanding neuronal physiology requires to record electrical activity in many small and remote compartments such as dendrites, axon or dendritic spines. To do so, electrophysiology has long been the tool of choice, as it allows recording very subtle and fast changes in electrical activity. However, electrophysiological measurements are mostly limited to large neuronal compartments such as the neuronal soma. To overcome these limitations, optical methods have been developed, allowing the monitoring of changes in fluorescence of fluorescent reporter dyes inserted into the neuron, with a spatial resolution theoretically only limited by the dye wavelength and optical devices. However, the temporal and spatial resolutive power of functional fluorescence imaging of live neurons is often limited by a necessary trade-off between image resolution, signal to noise ratio (SNR) and speed of acquisition. Here, I propose to use a Super-Resolution Shift and Mean (S&M) algorithm previously used in image computing to improve the SNR, time sampling and spatial resolution of acquired fluorescent signals. I demonstrate the benefits of this methodology using two examples: voltage imaging of action potentials (APs) in soma and dendrites of CA3 pyramidal cells and calcium imaging in the dendritic shaft and spines of CA3 pyramidal cells. I show that this algorithm allows the recording of a broad area at low speed in order to achieve a high SNR, and then pick the signal in any small compartment and resample it at high speed. This method allows preserving both the SNR and the temporal resolution of the signal, while acquiring the original images at high spatial resolution.
Locality and Hard SAT-Instances

In this note we construct a family of SAT-instance based on Eulerian graphs which are aimed at being hard for resolution based SAT-solvers. We discuss some experiments made with instances of this type and how a solver can try to avoid at least some of the pitfalls presented by these instances. Finally we look at how the density of subformulae can influence the hardness of SAT instances.