Miracle-dz/UniFAR-FTUs huggingface dataset:
Selected new training documents to update user profile

Relevance Feedback (RF) has been proven very effective for improving retrieval accuracy. Adaptive information filtering (AIF) technology has benefited from the improvements achieved in all the tasks involved over the last decades. A difficult problem in AIF has been how to update the system with new feedback efficiently and effectively. In current feedback methods, the updating processes focus on updating system parameters. In this paper, we developed a new approach, the Adaptive Relevance Features Discovery (ARFD). It automatically updates the system's knowledge based on a sliding window over positive and negative feedback to solve a nonmonotonic problem efficiently. Some of the new training documents will be selected using the knowledge that the system currently obtained. Then, specific features will be extracted from selected training documents. Different methods have been used to merge and revise the weights of features in a vector space. The new model is designed for Relevance Features Discovery (RFD), a pattern mining based approach, which uses negative relevance feedback to improve the quality of extracted features from positive feedback. Learning algorithms are also proposed to implement this approach on Reuters Corpus Volume 1 and TREC topics. Experiments show that the proposed approach can work efficiently and achieves the encouragement performance.
Reduction of Market Power and Stabilisation of Outcomes in a Novel and Simplified Two-Settlement Electricity Market

Modern electricity markets conduct a two-settlement procedure. Ahead of time, they allocate definite supply as well as reserves. Close to the time of consumption, they balance supply and demand. Bidding in these two auctions poses a challenge for automated bidding by agents, which will be more common in future electricity markets and so-called "smart grids". In a decision-theoretic model, we implement the current bidding practice that uses two independent bids and a novel, unified format that simplifies computation. We show through Monte-Carlo simulations in one-shot settings that the unified format restricts market power of suppliers in exploitable settings, and is also less vulnerable to uncertainty of bidders about market outcomes.
Plagiarism detection for multithreaded software based on thread-aware software birthmarks

The availability of inexpensive multicore hardware presents a turning point in software development. In order to benefit from the continued exponential throughput advances in new processors, the software applications must be multithreaded programs. As multithreaded programs become increasingly popular, plagiarism of multithreaded programs starts to plague the software industry. Although there has been tremendous progress on software plagiarism detection technology, existing dynamic approaches remain optimized for sequential programs and cannot be applied to multithreaded programs without significant redesign. This paper fills the gap by presenting two dynamic birthmark based approaches. The first approach extracts key instructions while the second approach extracts system calls. Both approaches consider the effect of thread scheduling on computing software birthmarks. We have implemented a prototype based on the Pin instrumentation framework. Our empirical study shows that the proposed approaches can effectively detect plagiarism of multithread programs and exhibit strong resilience to various semantic-preserving code obfuscations.
Verifying Identifier-Authenticity in Ubiquitous Computing Environment

In ubiquitous computing environment, identification of objects and places in the real world is important, and 2D printing code is useful to store identifiers of them. However, since it is easy to modify the content stored in the 2D code, we must verify whether the identifier written in the 2D code is indeed issued by the authorized organization. In this paper, we propose a verification method for identifiers stored in the 2D code. Our system makes sure that the identifier is indeed issued by the authorized organization, and the size of pair of identifier and its authenticator is small enough to be written in a 2D code. Furthermore, the proposed system has compatibility with the existing 2D code reading software. Our system is now operated in practical use, and some services based on this mechanism have been developed.
Towards a social governance framework for Internet of Things

Internet of Things (IoT) infuses computing intelligence into the physical world. This introduces new problems in areas such as safety and security of physical properties and privacy of the people inhabiting the spaces surrounded by the things. Because IoT can be deployed in public, private, and corporate spaces, formulating and enforcing the policies to manage the actions of the things such that user data and physical properties are safeguarded are major challenges. This paper proposes a novel framework called Social Governance for evolving the policies and enforcing them in typical IoT scenarios. The key idea of Social Governance is that a device should participate in a collaborative protocol to setup the policies that govern the space that it is using. The device would have incentives to adhere to the policies that are formulated by the collaborative process. The goal of the framework is to create agile but stable governance that is context and time sensitive.
Transmigration of Neutrophils across Inflamed Endothelium Is Signaled through LFA-1 and Src Family Kinase1

Leukocyte capture on inflamed endothelium is facilitated by a shift in LFA-1 from low to high affinity that supports binding to ICAM-1. LFA-1 bonds help anchor polymorphonuclear leukocytes (PMN) to inflamed endothelium in shear flow, and their redistribution to the leading edge guides pseudopod formation, migration, and extravasation. These events can be disrupted at the plasma membrane by stabilizing LFA-1 in a low- or intermediate-affinity state with allosteric small molecules. We hypothesized that a minimum dimeric bond formation between high-affinity LFA-1 and ICAM-1 under shear stress is necessary to catalyze transmembrane signaling of directed cell migration. Microspheres and substrates were derivatized with monomeric or dimeric ICAM-1 to simulate the surface of inflamed endothelium under defined ligand valence. Binding to dimeric ICAM-1, and not monomeric ICAM-1, was sufficient to elicit assembly of F-actin and phosphorylation of Src family kinases that colocalized with LFA-1 on adherent PMN. Genetic deletion or small molecule inhibition of Src family kinases disrupted their association with LFA-1 that correlated with diminished polarization of arrested PMN and abrogation of transmigration on inflamed endothelium. We conclude that dimeric bond clusters of LFA-1/ICAM-1 provide a key outside-in signal for orienting cytoskeletal dynamics that direct PMN extravasation at sites of inflammation.
On the optimal dividend problem for a spectrally negative Lévy process

In this paper we consider the optimal dividend problem for an insurance company whose risk process evolves as a spectrally negative L\'{e}vy process in the absence of dividend payments. The classical dividend problem for an insurance company consists in finding a dividend payment policy that maximizes the total expected discounted dividends. Related is the problem where we impose the restriction that ruin be prevented: the beneficiaries of the dividends must then keep the insurance company solvent by bail-out loans. Drawing on the fluctuation theory of spectrally negative L\'{e}vy processes we give an explicit analytical description of the optimal strategy in the set of barrier strategies and the corresponding value function, for either of the problems. Subsequently we investigate when the dividend policy that is optimal among all admissible ones takes the form of a barrier strategy.
An eight-user time-slotted SPECTS O-CDMA testbed: demonstration and simulations

This paper demonstrates an eight-user 9 Gb/s/user time-slotted spectral phase-encoded time-spreading (SPECTS) optical code division multiple access (O-CDMA) testbed. Experimentally measured performance is compared to numerical simulations. The testbed employs a novel compact fiber-pigtailed bulk-optics setup that utilizes a single two-dimensional (2-D) phase modulator for encoding multiple channels, each with a unique 64-chip Walsh code. The time-gated receiver is composed of a nonlinear optical loop mirror (NOLM) and a nonlinear thresholder each utilizing a highly nonlinear fiber (HNLF) as the nonlinear element. The testbed operates error free with up to six users and at a bit error rate BER<10/sup -9/ for eight simultaneous users. Careful modeling of each component in the testbed allows a close match between simulated and experimentally measured testbed performance.
Enhancing tissue integration in cartilage repair procedures

Arguably, the gold standard of biological repair of articular cartilage lesions is autologous chondrocyte transplantation. Although the clinical outcomes appear to range between good and excellent in most cases, there are, nevertheless, both clinical and biological challenges that remain to improve rehabilitation and clinical outcome. One of the major biological problems relates to tissue integration of the reparative tissue into the host tissue at a predictable level. Often within a single lesion, varying degrees of integration can be observed from total integration through to non‐integration as one passes through the defect. Here we briefly review some of the literature relating to this problem and include some of our own data drawn from questions we have posed about the biological nature of cartilage/cartilage integration. The nature and status of the tissue that comprises the wound lesion edge is central to tissue integration, and controlling aspects of trauma and free‐radical‐induced cell death together with matrix synthesis are identified as two components that require further investigation. Interestingly, there appears to be a limited ability of chondrocytes to be able to infiltrate existing cartilage matrices and even to occupy empty chondrocyte lacunae. Proliferation as a result of blunt and sharp trauma shows differential responses. As expected, blunt trauma induces a greater proliferative burst than sharp trauma and is more widespread from the lesion edge. However, in the case of sharp trauma, the basal cells enter proliferation before surface zone chondrocytes, which is not the case in blunt wounds. Regulation of these and associated processes will be necessary in order to devise strategies that can predict successful integration in biological repair procedures.
Characterization of continuously distributed cortical water diffusion rates with a stretched‐exponential model

Experience with diffusion‐weighted imaging (DWI) shows that signal attenuation is consistent with a multicompartmental theory of water diffusion in the brain. The source of this so‐called nonexponential behavior is a topic of debate, because the cerebral cortex contains considerable microscopic heterogeneity and is therefore difficult to model. To account for this heterogeneity and understand its implications for current models of diffusion, a stretched‐exponential function was developed to describe diffusion‐related signal decay as a continuous distribution of sources decaying at different rates, with no assumptions made about the number of participating sources. DWI experiments were performed using a spin‐echo diffusion‐weighted pulse sequence with b‐values of 500–6500 s/mm2 in six rats. Signal attenuation curves were fit to a stretched‐exponential function, and 20% of the voxels were better fit to the stretched‐exponential model than to a biexponential model, even though the latter model had one more adjustable parameter. Based on the calculated intravoxel heterogeneity measure, the cerebral cortex contains considerable heterogeneity in diffusion. The use of a distributed diffusion coefficient (DDC) is suggested to measure mean intravoxel diffusion rates in the presence of such heterogeneity. Magn Reson Med 50:727–734, 2003. © 2003 Wiley‐Liss, Inc.
Crown Plasticity and Competition for Canopy Space: A New Spatially Implicit Model Parameterized for 250 North American Tree Species

Background Canopy structure, which can be defined as the sum of the sizes, shapes and relative placements of the tree crowns in a forest stand, is central to all aspects of forest ecology. But there is no accepted method for deriving canopy structure from the sizes, species and biomechanical properties of the individual trees in a stand. Any such method must capture the fact that trees are highly plastic in their growth, forming tessellating crown shapes that fill all or most of the canopy space. Methodology/Principal Findings We introduce a new, simple and rapidly-implemented model–the Ideal Tree Distribution, ITD–with tree form (height allometry and crown shape), growth plasticity, and space-filling, at its core. The ITD predicts the canopy status (in or out of canopy), crown depth, and total and exposed crown area of the trees in a stand, given their species, sizes and potential crown shapes. We use maximum likelihood methods, in conjunction with data from over 100,000 trees taken from forests across the coterminous US, to estimate ITD model parameters for 250 North American tree species. With only two free parameters per species–one aggregate parameter to describe crown shape, and one parameter to set the so-called depth bias–the model captures between-species patterns in average canopy status, crown radius, and crown depth, and within-species means of these metrics vs stem diameter. The model also predicts much of the variation in these metrics for a tree of a given species and size, resulting solely from deterministic responses to variation in stand structure. Conclusions/Significance This new model, with parameters for US tree species, opens up new possibilities for understanding and modeling forest dynamics at local and regional scales, and may provide a new way to interpret remote sensing data of forest canopies, including LIDAR and aerial photography.
Sentiment Analysis ( Almost ) from Scratch

A popular application in Natural Language Processing (NLP) is the Sentiment Analysis, i.e., the task of extracting contextual polarity from a given text. The social network Twitter provides an immense amount of text (called tweets) generated by users with a maximum number of 140 characters. In this paper, we propose to learn a tweet representation from publicly provided data from tweets in order to infer sentiment from them. One challenge of this task is the fact that tweets are generated from very different users, making the data very heterogeneous (different from regular data which is written in proper English). Another challenge is, clearly, the large scale of the problem. We propose a deep learning sentence representation (called tweet representation) from user generated data to infer sentiment from tweets. This representation is learned from scratch (directly from the words in tweets) over a large unlabeled corpus of tweets. We demonstrate that we achieve stateof-the-art results for sentiment analysis on tweets.
Supporting Semantic Capture during Kinesthetic Teaching of Collaborative Industrial Robots

Industrial robot systems being deployed today do not contain domain knowledge to aid robot operators in setup and operational use. To gather such knowledge in a robot context requires mechanisms for entering and capturing semantic data, that will gradually build a working vocabulary while interacting with environment and operators, for bootstrapping system knowledge and ensuring data collection over time. This paper presents a prototype user interface, assisting kinesthetic teaching of a collaborative industrial robot, that allows for capturing semantic information while working with the robot in day-to-day use. A graphical user interface with natural language processing builds a working vocabulary of the environment while modifying and/or creating robot programs. A simple demonstration illustrates the approach.
Insertion heuristics for central cycle problems

A central cycle problem requires a cycle that is reasonably short and keeps the maximum distance from any node not on the cycle to its nearest node on the cycle reasonably low. The objective may be to minimize maximum distance or cycle length. Most classes of central cycle problems are NP‐hard. This article investigates insertion heuristics for central cycle problems, drawing on insertion heuristics for p centers and travelling salesman tours. It shows that a modified farthest insertion heuristic has reasonable worst‐case bounds. It then compares the performance of two farthest insertion heuristics on a range of problems from TSPLIB. It shows that a simple farthest insertion heuristic performs well in practice. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010
β-Lactam Resistance Response Triggered by Inactivation of a Nonessential Penicillin-Binding Protein

It has long been recognized that the modification of penicillin-binding proteins (PBPs) to reduce their affinity for β-lactams is an important mechanism (target modification) by which Gram-positive cocci acquire antibiotic resistance. Among Gram-negative rods (GNR), however, this mechanism has been considered unusual, and restricted to clinically irrelevant laboratory mutants for most species. Using as a model Pseudomonas aeruginosa, high up on the list of pathogens causing life-threatening infections in hospitalized patients worldwide, we show that PBPs may also play a major role in β-lactam resistance in GNR, but through a totally distinct mechanism. Through a detailed genetic investigation, including whole-genome analysis approaches, we demonstrate that high-level (clinical) β-lactam resistance in vitro, in vivo, and in the clinical setting is driven by the inactivation of the dacB-encoded nonessential PBP4, which behaves as a trap target for β-lactams. The inactivation of this PBP is shown to determine a highly efficient and complex β-lactam resistance response, triggering overproduction of the chromosomal β-lactamase AmpC and the specific activation of the CreBC (BlrAB) two-component regulator, which in turn plays a major role in resistance. These findings are a major step forward in our understanding of β-lactam resistance biology, and, more importantly, they open up new perspectives on potential antibiotic targets for the treatment of infectious diseases.
eHumanities Desktop - An Architecture for Flexible Annotation in Iconographic Research

This article addresses challenges in maintaining and annotating image resources in the field of iconographic research. We focus on the task of bringing together generic and extensible techniques for resource and annotation management with the highly specific demands in this area of research. Special emphasis is put on the interrelation of images, image segements and textual contents. In addition, we describe the architecture, data model and user interface of the open annotation system used in the image database application that is a part of the eHumanities Desktop.
Efficient Data Mapping and Buffering Techniques for Multilevel Cell Phase-Change Memories

New phase-change memory (PCM) devices have low-access latencies (like DRAM) and high capacities (i.e., low cost per bit, like Flash). In addition to being able to scale to smaller cell sizes than DRAM, a PCM cell can also store multiple bits per cell (referred to as multilevel cell, or MLC), enabling even greater capacity per bit. However, reading and writing the different bits of data from and to an MLC PCM cell requires different amounts of time: one bit is read or written first, followed by another. Due to this asymmetric access process, the bits in an MLC PCM cell have different access latency and energy depending on which bit in the cell is being read or written. We leverage this observation to design a new way to store and buffer data in MLC PCM devices. While traditional devices couple the bits in each cell next to one another in the address space, our key idea is to logically decouple the bits in each cell into two separate regions depending on their read/write characteristics: fast-read/slow-write bits and slow-read/fast-write bits. We propose a low-overhead hardware/software technique to predict and map data that would benefit from being in each region at runtime. In addition, we show how MLC bit decoupling provides more flexibility in the way data is buffered in the device, enabling more efficient use of existing device buffer space. Our evaluations for a multicore system show that MLC bit decoupling improves system performance by 19.2%, memory energy efficiency by 14.4%, and thread fairness by 19.3% over a state-of-the-art MLC PCM system that couples the bits in its cells. We show that our results are consistent across a variety of workloads and system configurations.
The minimum information required for a glycomics experiment (MIRAGE) project: improving the standards for reporting glycan microarray-based data

Abstract MIRAGE (Minimum Information Required for A Glycomics Experiment) is an initiative that was created by experts in the fields of glycobiology, glycoanalytics and glycoinformatics to produce guidelines for reporting results from the diverse types of experiments and analyses used in structural and functional studies of glycans in the scientific literature. As a sequel to the guidelines for sample preparation (Struwe et al. 2016, Glycobiology, 26:907–910) and mass spectrometry data (Kolarich et al. 2013, Mol. Cell Proteomics, 12:991–995), here we present the first version of guidelines intended to improve the standards for reporting data from glycan microarray analyses. For each of eight areas in the workflow of a glycan microarray experiment, we provide guidelines for the minimal information that should be provided in reporting results. We hope that the MIRAGE glycan microarray guidelines proposed here will gain broad acceptance by the community, and will facilitate interpretation and reproducibility of the glycan microarray results with implications in comparison of data from different laboratories and eventual deposition of glycan microarray data in international databases.
Prognostic value of tissue vascular endothelial growth factor expression in bladder cancer: a meta-analysis.

OBJECTIVE The prognostic role of vascular endothelial growth factor (VEGF) in bladder cancer remains controversial. This meta-analysis aimed to explore any association between overexpression and survival outcomes. METHODS We systematically searched for studies investigating the relationships between VEGF expression and outcome of bladder cancer patients. Study quality was assessed using the Newcastle-Ottawa Scale. After careful review, survival data were extracted from eligible studies. A meta-analysis was performed to generate combined hazard ratios (HRs) for overall survival (OS), disease-free survival (DFS) and disease-specific survival (DSS). RESULTS A total of 1,285 patients from 11 studies were included in the analysis. Our results showed that tissue VEGF overexpression in patients with bladder cancer was associated with poor prognosis in terms of OS (HR, 1.843; 95% CI, 1.231-2.759; P = 0.003), DFS (HR, 1.498; 95% CI, 1.255-1.787; P = 0.000) and DSS (HR, 1.562; 95% CI, 0.996-1.00; P = 0.052), though the difference for DSS was not statistically significant. In addition, there was no evidence of publication bias as suggested by Begg's and Egger's tests except for DFS (Begg's test, P = 0.221; Egger's test, P = 0.018). CONCLUSION The present meta-analysis indicated elevated VEGF expression to be associated with a poor prognosis in patients with bladder cancer.