Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Guided Local Search for Solving Binary DisCSPs

We introduce the Distributed Guided Local Search (Dist- GLS) algorithm for solving Distributed Constraint Satisfaction Problems. Our algorithm is based on the centralised Guided Local Search algorithm, which is extended with additional heuristics in order to enhance its efficiency in distributed scenarios. We discuss the strategies we use for dealing with local optima in the search for solutions and compare performance of Dist-GLS with that of Distributed Breakout (DBA). In addition, we provide the results of our experiments with distributed versions of random binary constraint satisfaction and graph colouring problems.
Two Ridge Extraction Methods for Portal Imaging

In this paperan existing ridge detectorbasedon theHessiantensoranda new onebasedon thestructure tensorare evaluatedfor usein portal/simulator imaging. Tensorsmoothingis introducedasa scaling parameter . By changingthis scale(as opposed to theimagescale)thedetectorscanbetunedto suppressspuriousridgeswithoutmoving them.Desirable ridgedetectorpropertiesfor portal/simulatorimaging aredescribedandthe ridge definitionsaretestedaccordingly. Experimentsshow our new ridgedetector to outperformtheexistingonein simulator/portalimagematching.
Deep Sequencing Identifies Tissue-Specific MicroRNAs and Their Target Genes Involving in the Biosynthesis of Tanshinones in Salvia miltiorrhiza

Salvia miltiorrhiza is one of the most popular traditional medicinal herbs in Asian nations. Its dried root contains a number of tanshinones, protocatechuic aldehyde, salvianolic acid B and rosmarinic, and is used for the treatment of various diseases. The finding of microRNAs (miRNAs) and their target genes will help understand their biological role on the biosynthesis of tanshinones in S. miltiorrhiza. In the present study, a total of 452 known miRNAs corresponding to 589 precursor miRNAs (pre-miRNAs), and 40 novel miRNAs corresponding to 24 pre-miRNAs were identified in different tissues of S. miltiorrhiza by high-throughput sequencing, respectively. Among them, 62 miRNAs express only in root, 95 miRNAs express only in stem, 19 miRNAs express only in leaf, and 71 miRNAs express only in flower, respectively. By the degradome analysis, 69 targets potentially cleaved by 25 miRNAs were identified. Among them, acetyl-CoA C-acetyltransferase was cleaved by miR5072, and involved in the biosynthesis of tanshinones. This study provided valuable information for understanding the tissue-specific expression patterns of miRNAs in S. miltiorrhiza, and offered a foundation for future studies of the miRNA-mediated biosynthesis of tanshinones.
Observer design for a class of non-linear systems with linearisable error dynamics

This study aims to study the existence of a change of coordinates which permits to transform a class of non-linear dynamical systems into the so-called non-linear observer canonical form. Moreover, an algorithm used to construct such a change of coordinates is given. Based on this form, the authors can design an observer with a linear error dynamic. This study is motivated by the results obtained by Krener and Respondek in 1985, where they studied the existence of change of coordinates for a class of multi-outputs non-linear systems. In 1989, Xia and Gao improved their work. The objective of this study is to show that the counterexample given by Xia and Gao for one result obtained by Krener and Respondek is not a real counterexample. For this purpose, they consider the case where the difference between any two observability indices is not larger than 1. Moreover, they allow a diffeomorphism on the outputs in the obtained non-linear observer canonical form.
Video transcoding by reducing spatial resolution

In network delivery of digital video, if the bandwidth required for a video is not available, the video has to be recoded at a reduced bit rate. It is highly desirable that the transcoding is carried out in real time while maintaining reasonable image quality. In this paper, we propose a fast approach to derive from an MPEG stream a new MPEG stream with half the spatial resolution. For the downsized video, we first generate from the original compressed video an improved estimate of the motion vectors. We then propose a compressed domain approach with data hiding to produce DCT residues by an open-loop method. The computational complexity is significantly lower than a number of previous approaches. Simulation suggests that our approach produces reasonable image quality.
Manifold-Based Reinforcement Learning via Locally Linear Reconstruction

Feature representation is critical not only for pattern recognition tasks but also for reinforcement learning (RL) methods to solve learning control problems under uncertainties. In this paper, a manifold-based RL approach using the principle of locally linear reconstruction (LLR) is proposed for Markov decision processes with large or continuous state spaces. In the proposed approach, an LLR-based feature learning scheme is developed for value function approximation in RL, where a set of smooth feature vectors is generated by preserving the local approximation properties of neighboring points in the original state space. By using the proposed feature learning scheme, an LLR-based approximate policy iteration (API) algorithm is designed for learning control problems with large or continuous state spaces. The relationship between the value approximation error of a new data point and the estimated values of its nearest neighbors is analyzed. In order to compare different feature representation and learning approaches for RL, a comprehensive simulation and experimental study was conducted on three benchmark learning control problems. It is illustrated that under a wide range of parameter settings, the LLR-based API algorithm can obtain better learning control performance than the previous API methods with different feature representation schemes.
Mobility Reflection : Exploiting Mobility-Awareness in Applications by Reflecting on Distributed Object Collaborations

We present Mobility Reflection, which demonstrates a reflection-based approach for designing mobilityawareness into applications. We propose an architecture that gives an application the ability to “reflect” upon its current behavior and to modify that behavior to adapt to changing network conditions caused by its host roaming in a mobile data network. The proposed architecture tailors dynamic decision making and adaptive behavior to the context of application-specific object collabor ations. This focused context helps to manage the complex constraints of maintaining application requirements in a dynamic environment of changing device characteristics and network resource availabi lity.
Ieee Transactions on Signal Processing 1 a Mapping-based Design for Nonsubsampled Hourglass Filter Banks in Arbitrary Dimensions

—Multidimensional hourglass filter banks decompose the frequency spectrum of input signals into hourglass-shaped directional subbands, each aligned with one of the frequency axes. The directionality of the spectral partitioning makes these filter banks useful in separating the directional information in multidimensional signals. Despite the existence of various design techniques proposed for the 2-D case, to our best knowledge, the design of hourglass filter banks in 3-D and higher dimensions with finite impulse response (FIR) filters and perfect reconstruction has not been previously reported. In this paper, we propose a novel mapping-based design for the hourglass filter banks in arbitrary dimensions, featuring perfect reconstruction, FIR filters, efficient implementation using lifting/ladder structures, and a near-tight frame construction. The effectiveness of the proposed mapping-based design depends on the study of a set of conditions on the frequency supports of the mapping kernels. These conditions ensure that we can still get good frequency responses when the component filters used are nonideal. Among all feasible choices, we then propose an optimal specification for the mapping kernels, which leads to the simplest passband shapes and involves the fewest number of frequency variables. Finally, we illustrate the proposed techniques by a design example in 3-D, and an application in video denoising.
Peptide-enhanced mRNA transfection in cultured mouse cardiac fibroblasts and direct reprogramming towards cardiomyocyte-like cells

The treatment of myocardial infarction is a major challenge in medicine due to the inability of heart tissue to regenerate. Direct reprogramming of endogenous cardiac fibroblasts into functional cardiomyocytes via the delivery of transcription factor mRNAs has the potential to regenerate cardiac tissue and to treat heart failure. Even though mRNA delivery to cardiac fibroblasts has the therapeutic potential, mRNA transfection in cardiac fibroblasts has been challenging. Herein, we develop an efficient mRNA transfection in cultured mouse cardiac fibroblasts via a polyarginine-fused heart-targeting peptide and lipofectamine complex, termed C-Lipo and demonstrate the partial direct reprogramming of cardiac fibroblasts towards cardiomyocyte cells. C-Lipo enabled the mRNA-induced direct cardiac reprogramming due to its efficient transfection with low toxicity, which allowed for multiple transfections of Gata4, Mef2c, and Tbx5 (GMT) mRNAs for a period of 2 weeks. The induced cardiomyocyte-like cells had α-MHC promoter-driven GFP expression and striated cardiac muscle structure from α-actinin immunohistochemistry. GMT mRNA transfection of cultured mouse cardiac fibroblasts via C-Lipo significantly increased expression of the cardiomyocyte marker genes, Actc1, Actn2, Gja1, Hand2, and Tnnt2, after 2 weeks of transfection. Moreover, this study provides the first direct evidence that the stoichiometry of the GMT reprogramming factors influence the expression of cardiomyocyte marker genes. Our results demonstrate that mRNA delivery is a potential approach for cardiomyocyte generation.
Clustering underlying stock trends via non-negative matrix factorization

Building a diversified portfolio is an appealing strategy in the analysis of stock market dynamics. It aims at reducing risk in market capital investments. Grouping stocks by similar latent trend can be cast into a clustering problem. The classical K-Means clustering algorithm does not fit the task of financial data analysis. Hence, we investigate Non-negative Matrix Factorization (NMF) techniques which, contrary to K-Means, turn out to be very effective when applied to stock data. In particular, recently developed NMF techniques, which incorporate convexity constraints, generate more disjoint latent trend groupings than the traditional sector-based groupings. In this paper, the NMF technique and its variants are applied to NASDAQ stock data (i.e., daily closing prices). Experimental results confirm that (convex ) NMF techniques are highly recommended to produce trend based assets and build a good diversified portfolio.
A history-based I-cache for low-energy multimedia applications

This paper proposes a history-based tag-comparison scheme for reducing energy consumption of direct-mapped instruction caches. The proposed cache efficiently exploits program-execution footprints recorded in the branch target buffer (BTB), and attempts to detect; and eliminate unnecessary tag checks at run time. Simulation results show that our approach can eliminate up to 95% of tag checks, saving the cache energy by 17%, while affecting the processor performance by only 0.2%.
Relief Impression Image Detection : Unsupervised Extracting Objects Directly From Feature Arrangements of Deep CNN

Traditional sliding windows method for region proposals always focus on the numerical feature values. Instead, we find that the arrangements of convolutional features in deep CNN keep a lot of spatial information of objects in image, and by extracting the positions of high value features as region proposals, we get a unified structure of object detection only based on forward CNN features.Our method has high speed and reasonable performance, since we get the idea from the representations of relievo, so we call this relief impression object detection. We also propose an assumption that the deep detector can act as human’s eye sight tracing without specialized training, we verify this assumption by creating a process called recursive finetuen in our method and get obvious promotion. Our method can get a good perfomace on the limited region proposals situation with very high speed and less computation resource. As far as we know, our work is the first one that notice the massive spatial information stored by the CNN convolutional features arrangement , and use them on the object detection task.
Verified Solution and Propagation of Uncertainty in Physiological Models

We demonstrate here a method for the veri(cid:12)ed solution of nonlinear ODE models in physiology, computing rigorous bounds on the trajectories of the state variables, based on the ranges of the uncertain parameters. We also demonstrate an approach for the propagation of uncertain probability distributions in one or more model parameters and/or initial conditions. Assuming an uncertain probability distribution (p-box) for each parameter and/or initial condition of interest, we propagate these distributions through the dynamic model to the state variables. As a result, we obtain a p-box describing the probability distribution for each state variable at times of interest. As test problems, we use two physiological models. The (cid:12)rst model simulates the metabolism of glucose in diabetic patients. The second is a simulation of long-term starvation that models the human body over time given uncertain metabolic rates. In both problems, comparisons are made with results obtained from Monte Carlo analysis.
Multilevel mixed linear model analysis using iterative generalized least squares

SUMMARY Models for the analysis of hierarchically structured data are discussed. An iterative generalized least squares estimation procedure is given and shown to be equivalent to maximum likelihood in the normal case. There is a discussion of applications to complex surveys, longitudinal data, and estimation in multivariate models with missing responses. An example is given using educational data.
What does this Mean? :Understanding how Women make Meaning of Text Messages while Developing an Intimate Relationship

This qualitative study explores how women in emerging adulthood make meaning of text messaging while developing an intimate heterosexual relationship. A focus group consisting of eight women was used in order to understand the way women interpret text messages during the formation of a romantic relationship. Data were analyzed using thematic analysis. Four themes emerged from the data: (1) what is the landscape? , (2) how do I figure it out? , (3) red flags, and (4) a red flag means. Participants described the context in which they enter relationships, and the role texting plays. Different strategies they used to make meaning of ambiguous text messages were discussed. Participants reported behaviors that were identified as red flags and how participants made meaning of those red flags. Limitations, clinical implications, and future directions are identified and discussed. “What does this Mean? ”: Understanding how Women make Meaning of Text Messages while Developing an Intimate Relationship
The Sparse Eigenvalue Problem

In this paper, we consider the sparse eigenvalue problem wherein the goal is to obtain a sparse solution to the generalized eigenvalue problem. We achieve this by constraining the cardinality of the solution to the generalized eigenvalue problem and obtain sparse principal component analysis (PCA), sparse canonical correlation analysis (CCA) and sparse Fisher discriminant analysis (FDA) as special cases. Unlike the l1-norm approximation to the cardinality constraint, which previous methods have used in the context of sparse PCA, we propose a tighter approximation that is related to the negative log-likelihood of a Student’s t-distribution. The problem is then framed as a d.c. (difference of convex functions) program and is solved as a sequence of convex programs by invoking the majorization-minimization method. The resulting algorithm is proved to exhibit global convergence behavior. The performance of the algorithm is empirically demonstrated on both sparse PCA (finding few relevant genes that explain as much variance as possible in a high-dimensional gene dataset) and sparse CCA (cross-language document retrieval and vocabulary selection for music retrieval) applications.
Deletion of immunomodulator C6 from vaccinia virus strain Western Reserve enhances virus immunogenicity and vaccine efficacy

Vectors based on vaccinia virus (VACV), the vaccine used to eradicate smallpox, are currently popular candidates for the vaccination against numerous infectious diseases including malaria and AIDS. Although VACV induces robust cellular and humoral responses, enhancing the safety and efficacy of these vectors remains an important area of research. Here, we describe the enhanced immunogenicity of a recombinant VACV Western Reserve (WR) strain lacking the immunomodulatory protein C6 (vΔC6). Intradermal infection of mice with vΔC6 was shown previously to induce smaller lesions, indicating viral attenuation, and this was confirmed here using a different inoculation dose. In addition, data presented show that vaccination with vΔC6 provided better protection against challenge with a lethal dose of VACV WR, indicating this virus is a better vaccine. Increased protection was not due to improved humoral responses, but instead enhanced cytotoxic activity of T-cells 1 month post-inoculation in the spleens of vΔC6-vaccinated mice.
An Attribute-Based Anonymous Broadcast Encryption Scheme with Adaptive Security in the Standard Model

In broadcast encryption schemes, a distribution center broadcasts an encrypted message to a subset chosen from a universe of receivers and only the intended users are able to decrypt the message. Most broadcast encryption schemes do not provide anonymity and the identities of target receivers are sent in plaintext. However, in several applications, the authorized users’ identities have the same sensitivity as the broadcasted messages. Yu, Ren and Lou (YRL) considered this issue and introduced an eicient anonymous aribute-based broadcast encryption scheme. In this paper, we first propose an aack on the YRL scheme and showthat the unauthorized receivers can also decrypt the broadcasted message. Next, we propose the Improved-YRL scheme and prove that it achieves anonymity and semantic security under adaptive corruptions in the chosen ciphertext seing. The proof is provided using the dual system encryption technique and is based on three complexity assumptions in composite order bilinear maps. The Improved-YRL scheme is a step forwardin solving the long-standing problem of secure and low overhead anonymous broadcast encryption.
Towards Modeling and Decomposing Loop-Based Electronic Music

Electronic Music (EM) is a popular family of genres which has increasingly received attention as a research subject in the ﬁeld of MIR. A fundamental structural unit in EM are loops—audio fragments whose length can span several seconds. The devices commonly used to produce EM, such as sequencers and digital audio workstations, impose a musical structure in which loops are repeatedly triggered and overlaid. This particular structure allows new perspectives on well-known MIR tasks. In this paper we ﬁrst review a prototypical production technique for EM from which we derive a simpliﬁed model. We then use our model to illustrate approaches for the following task: given a set of loops that were used to produce a track, decompose the track by ﬁnding the points in time at which each loop was activated. To this end, we repurpose established MIR techniques such as ﬁngerprinting and non-negative matrix factor deconvolution.
Automated Suggestions for Miscollocations

One of the most common and persistent error types in second language writing is collocation errors, such as learn knowledge instead of gain or acquire knowledge, or make damage rather than cause damage. In this work-in-progress report, we propose a probabilistic model for suggesting corrections to lexical collocation errors. The probabilistic model incorporates three features: word association strength (MI), semantic similarity (via Word-Net) and the notion of shared collocations (or intercollocability). The results suggest that the combination of all three features outperforms any single feature or any combination of two features.
Acceptance of augmented reality instructions in a real work setting

The differences between Augmented Reality (AR) systems and computer display based systems create a need for a different approach to the design and development of AR systems. To understand the potential of AR systems in real world tasks the technology must be tested in real world scenarios. This case study includes two qualitative user studies where AR was used for giving instructions to users in a hospital. The data show that the users in the context of medical care are positive towards AR systems as a technology and as a tool for instructions in terms of usefulness and social acceptance. The results indicate that AR technology, should it be introduced as technical support in this context, may become an accepted, and appreciated, part of every day work.
A decoy receptor 3 analogue reduces localised defects in phagocyte function in pneumococcal pneumonia

Background Therapeutic strategies to modulate the host response to bacterial pneumonia are needed to improve outcomes during community-acquired pneumonia. This study used mice with impaired Fas signalling to examine susceptibility to pneumococcal pneumonia and decoy receptor 3 analogue (DcR3-a) to correct factors associated with increased susceptibility. Methods Wild-type mice and those with varying degrees of impairment of Fas (lpr) or Fas ligand signalling (gld) were challenged with Streptococcus pneumoniae and microbiological and immunological outcomes measured in the presence or absence of DcR3-a. Results During established pneumonia, neutrophils became the predominant cell in the airway and gld mice were less able to clear bacteria from the lungs, demonstrating localised impairment of pulmonary neutrophil function in comparison to lpr or wild-type mice. T-cells from gld mice had enhanced activation and reduced apoptosis in comparison to wild-type and lpr mice during established pneumonia. Treatment with DcR3-a reduced T-cell activation and corrected the defect in pulmonary bacterial clearance in gld mice. Conclusions The results suggest that imbalance in tumour necrosis factor superfamily signalling and excessive T-cell activation can impair bacterial clearance in the lung but that DcR3-a treatment can reduce T-cell activation, restore optimal pulmonary neutrophil function and enhance bacterial clearance during S pneumoniae infection.