Miracle-dz/UniFAR-FTUs huggingface dataset:
Sublinear Randomized Algorithms for Skeleton Decompositions

Let $A$ be a $n$ by $n$ matrix. A skeleton decomposition is any factorization of the form $CUR$ where $C$ comprises columns of $A$, and $R$ comprises rows of $A$. In this paper, we consider uniformly sampling $\l\simeq k \log n$ rows and columns to produce a skeleton decomposition. The algorithm runs in $O(\l^3)$ time, and has the following error guarantee. Let $\norm{\cdot}$ denote the 2-norm. Suppose $A\simeq X B Y^T$ where $X,Y$ each have $k$ orthonormal columns. Assuming that $X,Y$ are incoherent, we show that with high probability, the approximation error $\norm{A-CUR}$ will scale with $(n/\l)\norm{A-X B Y^T}$ or better. A key step in this algorithm involves regularization. This step is crucial for a nonsymmetric $A$ as empirical results suggest. Finally, we use our proof framework to analyze two existing algorithms in an intuitive way.
Cyclostationary Spectrum Sensing in Cognitive Radios at Low SNR Regimes

Spectrum sensing is one of the key elements in the establishment of cognitive radio. One of the most effective approaches for spectrum sensing is cyclostationary feature detection. Since modulated signals can be modeled as cyclostationary random signals, this feature can be used to recognize the cyclostationary modulated signal in a background of stationary noise even at low SNR regimes. This chapter reviews non-cooperative cyclostationary sensing approaches and reports recent advances in cooperative cyclostationary sensing algorithms. New results for cooperative cyclostationary spectrum sensing are then presented, which ensure better performance as well as faster and simpler operation. In the proposed schemes, each Secondary User (SU) performs Single-Cycle (SC) cyclostationary detection for fast and simple implementation, while collaboration between SUs in final decision on the presence or absence of the PU is explored to improve its performance. Furthermore, this chapter presents another look at the performance evaluation of cyclostationary detectors in terms of deflection coefficients.
Physical capacity tasks in chronic low back pain: What is the contributing role of cardiovascular capacity, pain and psychological factors?

Purpose. To explore the association of personal and body functions with physical capacity tasks used in the evaluation of chronic low back pain (CLBP) treatment. Method. Cross-sectional study in which 221 patients with non-specific CLBP participated. Physical capacity was assessed by six capacity tasks, and several personal and body functions were assessed by questionnaires (age, gender, pain intensity, duration of pain, radiating pain to leg, fear of injury/movement, depression, pain catastrophizing and internal control of pain). Cardiovascular capacity was measured by a modified Åstrand submaximal bicycle test. The association of these hypothetically influential personal and body function factors with capacity tasks was examined with multiple linear regression analyses. Results. The total explained variance was low to moderate (9 – 19%), except for stair climbing for which the variance just reached 30%. Many putative factors did not contribute significantly to the level of outcome of several different tasks. Conclusions. Cardiovascular capacity, pain intensity, fear of injury/movement, cognitions, and depression had statistically significant but clinically minor effects on several, but not all capacity tasks. Radiating pain, age and duration of complaints had no significant influence at all. Due to anthropometric differences men outperformed women on most tasks. Apparently the influence of many personal, physical but especially psychological factors on the selected capacity tasks is not high at all. This might indicate that these tasks are more objectively measuring physical capacity than expected.
Making Exploration History Interactive for Web-based Learning

The main problem addressed in this paper is how to help learners reflect on knowledge that they have constructed in exploring existing hypermedia/hypertext based learning resources on the Web. Our approach to this problem is to provide each learner with a kind of reflection support proper to his/her exploration process. In this paper, we describe an interactive history that encourages learners to annotate their exploration history with the reasons why they have explored, which reasons have a great influence on knowledge construction in hyperspace. It also generates a knowledge map, which spatially represents the semantic relationships among the WWW pages visited by the learners. This paper also describes a preliminary analysis and evaluation with the interactive history system. The results indicate that the system facilitates a rethink on exploration processes, and that the system produces good effects on learning such as integrating the contents of some nodes in more complicated hyperspace.
Bayesian mixed-membership models of complex and evolving networks

Over the past three years I have been working on probabilistic models of multivariate attributes and relations. My work suggests a probabilistic framework and a general modeling approach to complex and evolving networks, which is based on the four concepts of mixed membership, motifs, dynamics & integration. In this thesis, I present such a framework and discuss its properties. In particular, the main goal of the research is to establish the essential elements of formal models of complexity that reconcile the global properties of a system with local phenomena of interest, in a generative fashion. A solution to the global/local trade-off is to express complexity through hierarchical mixtures of simple patterns, i.e., motifs, that evolve over time. Complex global behavior emerges from the combination of local interaction patterns and their dynamics. I discuss the extent to which this novel framework incorporates, generalizes, and extends other probabilistic approaches present in the literature, and argue that it provides the foundations of a statistical theory of (random) graphs. A major part of the effort is devoted to the analysis of modeling issues related to the four essential aspects listed above, in the context of applications to social and biological networks. I also investigate theoretical and computational issues such as the geometrical intuition of the latent allocation task---an important inference objective shared by the various models encompassed by this framework.
The role of chordin/Bmp signals in mammalian pharyngeal development and DiGeorge syndrome

The chordin/Bmp system provides one of the best examples of extracellular signaling regulation in animal development. We present the phenotype produced by the targeted inactivation of the chordin gene in mouse. Chordin homozygous mutant mice show, at low penetrance, early lethality and a ventralized gastrulation phenotype. The mutant embryos that survive die perinatally, displaying an extensive array of malformations that encompass most features of DiGeorge and Velo-Cardio-Facial syndromes in humans. Chordin secreted by the mesendoderm is required for the correct expression of Tbx1 and other transcription factors involved in the development of the pharyngeal region. The chordin mutation provides a mouse model for head and neck congenital malformations that frequently occur in humans and suggests that chordin/Bmp signaling may participate in their pathogenesis.
Embedding Robust Labels into Images for Copyright Protection

This paper describes a set of novel steganographic methods to secretly embed robust labels into image data for identifying image copyright holder and original distributor in digital networked environment. The embedded label is undetectable, unremovable and unalterable. Furthermore it can survive processing which does not seriously reduce the quality of the image, such as lossy image compression, low pass filtering and image format conversions.
A scalable architecture for ordered parallelism

We present Swarm, a novel architecture that exploits ordered irregular parallelism, which is abundant but hard to mine with current software and hardware techniques. In this architecture, programs consist of short tasks with programmer-specified timestamps. Swarm executes tasks speculatively and out of order, and efficiently speculates thousands of tasks ahead of the earliest active task to uncover ordered parallelism. Swarm builds on prior TLS and HTM schemes, and contributes several new techniques that allow it to scale to large core counts and speculation windows, including a new execution model, speculation-aware hardware task management, selective aborts, and scalable ordered commits. We evaluate Swarm on graph analytics, simulation, and database benchmarks. At 64 cores, Swarm achieves 51–122 × speedups over a single-core system, and outperforms software-only parallel algorithms by 3–18 ×.
Migrating Open Science Grid to RPMs*

We recently completed a significant transition in the Open Science Grid (OSG) in which we moved our software distribution mechanism from the useful but niche system called Pacman to a community-standard native package system, RPM. In this paper we explore some of the lessons learned during this transition as well as our earlier work, lessons that we believe are valuable not only for software distribution and packaging, but also for software engineering in a distributed computing environment where reliability is critical. We discuss the benefits found in moving to a community standard, including the abilities to reuse existing packaging, to donate existing packaging back to the community, and to leverage existing skills in the community. We describe our approach to testing in which we test our software against multiple versions of the OS, including pre-releases of the OS, in order to find surprises before our users do. Finally, we discuss our large-scale evaluation testing and community testing, which are essential for both quality and community acceptance.
Fuzzy rule based systems for interpretable sentiment analysis

Sentiment analysis, which is also known as opinion mining, aims to recognise the attitude or emotion of people through natural language processing, text analysis and computational linguistics. In recent years, many studies have focused on sentiment classification in the context of machine learning, e.g. to identify that a sentiment is positive or negative. In particular, the bag-of-words method has been popularly used to transform textual data into structured data, in order to enable the direct use of machine learning algorithms for sentiment classification. Through the bag-of-words method, each single term in a text document is turned into a single attribute to make up a structured data set, which results in high dimensionality of the data set and thus negative impact on the interpretability of computational models for sentiment analysis. This paper proposes the use of fuzzy rule based systems as computational models towards accurate and interpretable analysis of sentiments. The use of fuzzy logic is better aligned with the inherent uncertainty of language, while the “white box” characteristic of the rule based learning approaches leads to better interpretability of the results. The proposed approach is tested on four datasets containing movie reviews; the aim is to compare its performance in terms of accuracy with two other approaches for sentiment analysis that are known to perform very well. The results indicate that the fuzzy rule based approach performs marginally better than the well-known machine learning techniques, while reducing the computational complexity and increasing the interpretability.
Demonstration of Cytotoxic Antibodies in Rabbits Bearing Tumors Induced by Shope Fibroma Virus

The 51Cr-release test was used to detect cytotoxic antibodies in adult rabbits bearing tumors induced by Shope fibroma virus. The following are the recommended experimental conditions: the infection of RK-13 cells with a multiplicity of 1 to 2 infectious units per cell for 48 hr, 51Cr labeling of infected cells during the last 12 hr of incubation, sensitization of suspended labeled infected cells for 1 hr with immune serum, and quantitation of cell damage by the amount of 51Cr released after 6 hr of incubation in the presence of complement. The immune sera reacted only with fibroma virus-infected cells but not with cells infected with vaccinia virus or herpesvirus type 1. Similarly, sera prepared against vaccinia virus and herpesvirus type 1 were not cytotoxic to fibroma virus-infected cells, although they were cytotoxic to cells infected with homologous viruses. The total antibody activity in sera of rabbits infected with Shope fibroma virus was detected first on day 7, gradually rose to its peak by day 23, and persisted at that level for at least 50 days. The 19S antibody was detected on day 7, reached peak titers by day 13, and disappeared by day 17. The 7S antibody was barely detectable on day 7, reached maximum titers on day 13, and remained high for at least 50 days. The tumors appeared on the 3rd day after virus inoculation, reached maximum size on day 13, and regressed completely by day 23.
Cosmological Feedback from High-Redshift Dwarf Galaxies

We model how repeated supernova explosions in high-redshift dwarf starburst galaxies drive superbubbles and winds out of the galaxies. We compute the efficiencies of metal and mass ejection and energy transport from the galactic potentials, including the effect of cosmological infall of external gas. The starburst bubbles quickly blow out of small, high-redshift galactic disks, but must compete with the ram pressure of the infalling gas to escape into intergalactic space. We show that the assumed efficiency of the star formation rate dominates the bubble evolution and the metal, mass, and energy feedback efficiencies. With a star formation efficiency f* = 0.01, the ram pressure of infall can confine the bubbles around high-redshift dwarf galaxies with circular velocities vc ≳ 52 km s-1. We can expect high metal and mass ejection efficiencies and moderate energy transport efficiencies in halos with vc ≈ 30-50 km s-1 and f* ≈ 0.01 as well as in halos with vc ≈ 100 km s-1 and f* ≫ 0.01. Such halos collapse successively from 1-2 σ peaks in ΛCDM Gaussian density perturbations as time progresses. These dwarf galaxies can probably enrich low- and high-density regions of intergalactic space with metals to 10-3 to 10-2 Z☉ as they collapse at z ≈ 8 and z ≲ 5, respectively. They also may be able to provide adequate turbulent energy to prevent the collapse of other nearby halos, as well as to significantly broaden Ly-α absorption lines to vrms ≈ 20-40 km s-1. We compute the timescales for the next starbursts if gas freely falls back after a starburst, and find that for star formation efficiencies as low as f* ≲ 0.01 the next starburst should occur in less than half the Hubble time at the collapse redshift. This suggests that episodic star formation may be ubiquitous in dwarf galaxies.
HAPTIC REPRESENTATION OF SCIENTIFIC DATA FOR VISUALLY IMPAIRED OR BLIND PERSONS

Interpretation and understanding of complex scientific data is enhanced by graphic representation. For those people with visual impairments, this method is inadequate. An alternative, or additional, method is to represent the data using other modalities, namely touch and hearing. Haptic display of such graphical data adds kinesthetic feedback that provides the sense of touch. Audio feedback adds additional information to the haptic display to further improve conceptualization. In this paper, we review some of the existing methods of non-visual data representation, and present two new methods. The first method extends the capabilities of printing tactile graphics. With the aid of image processing techniques, complex images can be printed in a fashion that is more comprehensible as a tactile graphic. The second technique takes advantage of emerging haptic interface technology with a three degree of freedom (DOF) force feedback mechanism. This allows users to interact with two and three dimensional data plots with their fingertip. In addition, aural information and various haptic techniques can be added to aid in navigation and understanding. These two methods of non-visual data rendering extend current techniques and will eventually allow people to gain insight into scientific data without the use of
SKILL‐BASED CONDITIONING GAMES AS AN ALTERNATIVE TO TRADITIONAL CONDITIONING FOR RUGBY LEAGUE PLAYERS

This study investigated the effects of skill-based conditioning games and traditional conditioning for improving speed, agility, muscular power, and maximal aerobic power in rugby league players. Sixty-nine subelite rugby league players performed either a skill-based conditioning games program (N = 32) or a traditional conditioning (i.e., running activities with no skill component) program (N = 37). Each player participated in a 9-week in-season training program, performed over 2 competitive seasons. Players performed 2 organized field-training sessions each week. Players underwent measurements of speed (10-m, 20-m, and 40-m sprint), muscular power (vertical jump), agility (L run), and maximal aerobic power (multi-stage fitness test) before and after the training period. Skill-based conditioning games induced a significant improvement (p < 0.05) in 10-m, 20-m, and 40-m speed, muscular power, and maximal aerobic power, whereas traditional conditioning activities improved 10-m speed and maximal aerobic power only. No significant differences (p > 0.05) were detected between the traditional conditioning and skill-based conditioning games groups for changes in 10-m speed, agility, and maximal aerobic power. Both groups won 6 of 8 matches played within the training period, resulting in a win-loss ratio of 75%. However, on average, the skill-based conditioning games group scored more points in attack (p < 0.05) and had a greater (p < 0.05) points differential than the traditional conditioning group. The results of this study demonstrate that skill-based conditioning games offer an effective method of in-season conditioning for rugby league players. In addition, given that skills learned from skill-based conditioning games are more likely to be applied in the competitive environment, their use may provide a practical alternative to traditional conditioning for improving the physiological capacities and playing performance of rugby league players.
A Contextual Query Expansion Approach by Term Clustering for Robust Text Summarization

This paper describes the different steps which lead to the construction of the LIP6 extractive summarizer. The basic idea behind this system is to expand question and title keywords of each topic with their respective cluster terms. Term clusters are found by unsupervised learning using a classification variant of the well-known EM algorithm. Each sentence is then characterized by 4 features, each of which uses bag-of-words similarities between expanded topic title or questions and the current sentence. A final score of the sentences is found by manually tuning the weights of a linear combination of these features ; these weights are chosen in order to maximize the Rouge-2 AvF measure on the Duc 2006 corpus.
A Review- Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment

A B S T R A C T A cloud computing infrastructure is a complex system with a large number of shared resources. Cloud resource management requires complex policies and decisions for multi-objective optimization. Cloud computing is an effective computing model since it allows for the provision of resources on demand. In the resource management problems the Dynamic resource allocation problem is one of the most important problems. To present a better solution for solving the problem of dynamic resource allocation in a cloud computing environment, the proposed system represents the skewness algorithm to determine the unevenness in the multi-dimensional resource utilization of server. In this paper system uses virtualization technology to allocate data centre resources dynamically based on application demands and support green computing by optimizing the number of servers in use.
Effects of the Molecular Weight and the Degree of Deacetylation of Chitosan Oligosaccharides on Antitumor Activity

Effects of the degree of deacetylation (DDA) and the molecular mass of chitosan oligosaccharides (CTS-OS), obtained from the enzymatic hydrolysis of high molecular weight chitosan (HMWC), on antitumor activity was explored. The DDA and molecular weights of CTS-OS were determined by matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-TOF MS) analysis. The CTS-OS were found to be a mixture of mainly dimers (18.8%), trimers (24.8%), tetramers (24.9%), pentamers (17.7%), hexamers (7.1%), heptamers (3.3%), and octamers (3.4%). The CTS-OS were further fractionated by gel-filtration chromatography into two major fractions: (1) COS, consisting of glucosamine (GlcN)n, n = 3–5 with DDA 100%; and (2) HOS, consisting of (GlcN)5 as the minimum residues and varying number of N-acetylglucosamine (GlcNAc)n, n = 1–2 with DDA about 87.5% in random order. The cytotoxicities, expressed as the concentration needed for 50% cell death (CC50), of CTS-OS, COS, and HOS against PC3 (prostate cancer cell), A549 (lung cancer cell), and HepG2 (hepatoma cell), were determined to be 25 μg·mL−1, 25 μg·mL−1, and 50 μg·mL−1, respectively. The HMWC was approximately 50% less effective than both CTS-OS and COS. These results demonstrate that the molecular weight and DDA of chitosan oligosaccharides are important factors for suppressing cancer cell growth.
Photoluminescence Properties of Sulfur-Doped Sr3SiO5: Eu2+ Phosphor for White LEDs

A novel S-doped Sr3SiO5: Eu2+ phosphor was synthesized employing high temperature solid-state reaction method under reductive atmosphere environment. The crystal structure and photoluminescence properties of the phosphor, and the effect of S2- doping concentration on the photoluminescence intensity of Eu2+ was studied in details. The results show that the crystal structure of Sr3SiO5: Eu2+is not changed upon S2- doping. When the doping concentration of S2- is 1 wt%, the phosphor shows the maximum emission intensity, which is higher than that of Sr3SiO5: Eu2+ without S2-. Moreover, the addition of S2- results in an slightly shift of its emission spectra towards the red region, indicating that the S-doped Sr3SiO5: Eu2+ is an excellent yellow-orange phosphor for warm white LEDs.
New Computational Models for Electrostatics of Macromolecules in Solvents

Electrostatic fields of macromolecules (e.g., protein molecules) in solvents are often described by the Poisson-Boltzmann equation. This paper introduces two substantial amendments to the electrostatic model: first, the effective dielectric permittivity of the aqueous solvent layer on the molecular surface is drastically different from its bulk value of ~80 and, second, the recently developed flexible local approximation methods produce different schemes with much higher accuracy than the classical ones
Thin-Layer Drying of Pumpkin Fruit Slices

Pumpkin (Cucuribita Spp.) fruit slices were subjected to pre-drying treatments and drying using methods of uncontrolled sun and oven drying. The two-way interaction between drying methods and pre-drying treatments had significant effect on chemical qualities. Pumpkin subjected to salt solution dipping treatment and oven dried had higher chemical concentrations. Among the pumpkin fruit accessions, pumpkin accession 8007 had the superior TSS after drying. Among the three pre-drying treatments, salt solution dipping treatment had significant effect and the most efficient pre-drying treatment to retain the quality of dried pumpkin fruits without significant chemical quality deterioration. Salt dipping treatment combined with low temperature (60°C) oven air circulation drying is recommended to maintain quality of dried pumpkin slices. Integrated pre-and postharvest treatment including the genetic factor was shown to have significant effect on the drying kinetics as well as quality after drying of pumpkin.