Miracle-dz/UniFAR-FTUs huggingface dataset:
A new test for the Box-Cox transformation model: An analysis of length of hospital stay for diabetes patients in Japan

The Box-Cox (1964) transformation model (BC model) is widely used to examine various problems. The likelihood function under the normality assumption is misspecified, and the maximum likelihood estimator (BC MLE) cannot in general be consistent. However, under the “small sigma” assumption described in Bickel and Doksum (1981), the BC MLE can be consistent. It is, therefore, necessary to test whether this assumption is satisfied when the BC model is used. In this paper, we propose a new test of whether or not the BC MLE can be used based on the estimator proposed by Nawata (2013). We then analyze length of hospital stay for type 2 diabetes patients hospitalized for educational programs about managing diabetes at home. A dataset of 970 patients collected from 27 general hospitals in Japan is used.
Continuous approximation of binomial lattices

A systematic analysis of a continuous version of a binomial lattice, containing a real parameter γ and covering the Toda field equation as γ→∞, is carried out in the framework of group theory. The symmetry algebra of the equation is derived. Reductions by one-dimensional and two-dimensional subalgebras of the symmetry algebra and their corresponding subgroups, yield notable field equations in lower dimensions whose solutions allow us to find exact solutions to the original equation. Some reduced equations turn out to be related to potentials of physical interest, such as the Fermi–Pasta–Ulam and the Killingbeck potentials, and others. An instantonlike approximate solution is also obtained which reproduces the Eguchi–Hanson instanton configuration for γ→∞. Furthermore, the equation under consideration is extended to n+1 dimensions. A spherically symmetric form of this equation, studied by means of the symmetry approach, provides conformally invariant classes of field equations comprising remarkable special cases. One of these (n=4) enables us to establish a connection with the Euclidean Yang–Mills equations, another appears in the context of Differential Geometry in relation to the so-called Yamabe problem. All the properties of the reduced equations are shared by the spherically symmetric generalized field equation.
Novel hybrid image content descriptor

Efficient descriptors perform a key role in the success of object recognition, scene classification, image retrieval and many other computer vision related tasks which may learn the descriptors to decide on favorable class. In this paper we have proposed a hybrid descriptor which leverages the benefits of color as well as edges features. We extract color histogram from H component of the mean-shift filtered image. For edge features, we apply Primitive Set (PS) description on I component. The mean shift filtered color descriptor facilitates the identification of similar color images and the PS Descriptor adds value from its strict pass of similar structural elements. The usage of PS adds resiliency to the noise and blur to the descriptor. The results are conforming to the expectations and shown with the help of a retrieval system on Corel data-set.
A Locked Nucleic Acid Antisense Oligonucleotide (LNA) Silences PCSK9 and Enhances LDLR Expression In Vitro and In Vivo

Background The proprotein convertase subtilisin/kexin type 9 (PCSK9) is an important factor in the etiology of familial hypercholesterolemia (FH) and is also an attractive therapeutic target to reduce low density lipoprotein (LDL) cholesterol. PCSK9 accelerates the degradation of hepatic low density lipoprotein receptor (LDLR) and low levels of hepatic PCSK9 activity are associated with reduced levels of circulating LDL-cholesterol. Methodology/Principal Findings The present study presents the first evidence for the efficacy of a locked nucleic acid (LNA) antisense oligonucleotide (LNA ASO) that targets both human and mouse PCSK9. We employed human hepatocytes derived cell lines HepG2 and HuH7 and a pancreatic mouse β-TC3 cell line known to express high endogenous levels of PCSK9. LNA ASO efficiently reduced the mRNA and protein levels of PCSK9 with a concomitant increase in LDLR protein levels after transfection in these cells. In vivo efficacy of LNA ASO was further investigated in mice by tail vein intravenous administration of LNA ASO in saline solution. The level of PCSK9 mRNA was reduced by ∼60%, an effect lasting more than 16 days. Hepatic LDLR protein levels were significantly up-regulated by 2.5–3 folds for at least 8 days and ∼2 fold for 16 days. Finally, measurement of liver alanine aminotransferase (ALT) levels revealed that long term LNA ASO treatment (7 weeks) does not cause hepatotoxicity. Conclusion/Significance LNA-mediated PCSK9 mRNA inhibition displayed potent reduction of PCSK9 in cell lines and mouse liver. Our data clearly revealed the efficacy and safety of LNA ASO in reducing PCSK9 levels, an approach that is now ready for testing in primates. The major significance and take home message of this work is the development of a novel and promising approach for human therapeutic intervention of the PCSK9 pathway and hence for reducing some of the cardiovascular risk factors associated with the metabolic syndrome.
The power of Pi

This paper exhibits the power of programming with dependent types by dint of embedding three domain-specific languages: Cryptol, a language for cryptographic protocols; a small data description language; and relational algebra. Each example demonstrates particular design patterns inherent to dependently-typed programming. Documenting these techniques paves the way for further research in domain-specific embedded type systems.
A bipolar functionality of Q/N-rich proteins: Lsm4 amyloid causes clearance of yeast prions

Prions are epigenetic modifiers that cause partially loss‐of‐function phenotypes of the proteins in Saccharomyces cerevisiae. The molecular chaperone network that supports prion propagation in the cell has seen a great progress in the last decade. However, the cellular machinery to activate or deactivate the prion states remains an enigma, largely due to insufficient knowledge of prion‐regulating factors. Here, we report that overexpression of a [PSI+]‐inducible Q/N‐rich protein, Lsm4, eliminates the three major prions [PSI+], [URE3], and [RNQ+]. Subcloning analysis revealed that the Q/N‐rich region of Lsm4 is responsible for the prion loss. Lsm4 formed an amyloid in vivo, which seemed to play a crucial role in the prion elimination. Fluorescence correlation spectroscopy analysis revealed that in the course of the Lsm4‐driven [PSI+] elimination, the [PSI+] aggregates undergo a size increase, which ultimately results in the formation of conspicuous foci in otherwise [psi−]‐like mother cells. We also found that the antiprion activity is a general property of [PSI+]‐inducible factors. These data provoked a novel “unified” model that explains both prion induction and elimination by a single scheme.
Multimedia communication in cognitive radio networks based on sample division multiplexing

In this paper, we address the problem of channel sensing, channel allocation and transmission of multimedia signals in a cognitive radio network, while maintaining the required quality of service (QoS) constraints. Depending on the type of signal and QoS requirements, different types of multimedia signals need different bandwidths for communication. In typical wireless systems, unless a contiguous frequency band in the spectrum with width at least equal to the required bandwidth is obtained, multimedia communication can not occur with the desired QoS. We propose here a novel technique based on sample division multiplexing (SDM) to overcome this issue. Our proposed approach is based on utilizing several frequency bands, each of smaller width than the required bandwidth but whose sum total equals at least the required bandwidth. Towards this end, we present algorithms for channel reservation, channel sensing and allocation along with protocols for transmission and reception. Our approach also ensures preemption of secondary users by the primary users as typically demanded in a cognitive radio based communication environment.
TF-IDF method in ranking keywords of Instagram users' image captions

Instagram is one of the popular social media applications used by a wide range of people around the world. The significant growth of active Instagram users affects the size of Instagram data. The more number of users, the larger and more various Instagram data is posted. In line with its popularity, in recent years many researchers begin to study and analyze it for various purposes, such as detecting event photos based on location, clustering the photo content, advertising strategies based on user types, and so on. As of now there are three types of data available in Instagram which are text, image, and video. In this paper we propose Term-Frequency and Inverse Document Frequency (TF-IDF) method to rank keywords of top twenty most followed Instagram users based on image captions of Instagram. The objective of this research is to automatically know the main idea of Instagram users based on 50 recent image captions posted. In our experiments, TF-IDF has been successfully implemented to reveal a set of keywords with its ranking. The highest ranking of keyword is indeed the main topic of a user, indicated by the value of TF-IDF. The result of study indicates that TF-IDF method is very useful to find and rank the keywords of Instagram users image captions. In the future research, the ranking keywords are needed in solving classification and clustering tasks as feature extractions.
Multi label prediction using association rule generation and simple k-means

Lately, modern applications like information retrieval, semantic scene classification, music categorization and functional genomics classification highly require multi label classification. A rule mining algorithm apriori is widely used for rule generation. But Apriori is used many times on categorical data, it is seldom used for numerical data. This leads to an idea that with proper data pre-processing, a lot of intangible rules can be derived from such numerical datasets. Since the algorithm will check each and every datasets, we used a simple k-means clustering approach for dividing the processing space of Apriori and thus rules are generated for each cluster. The accuracy of the algorithm is calculated using hamming loss and is presented in the paper. This hybrid algorithm directly aims to find out hidden patterns in huge numerical datasets and make reliable label prediction easier.
Prediction of HIV-1 and human protein interactions based on a novel evolution-aware structure alignment method

Competition between HIV-1 proteins and human proteins is important in the course of HIV-1 infection. Understanding the interaction between HIV-1 and human proteins will help to understand how the pathogen manipulates the biological pathways and processes of the host. Based on the hypothesis that proteins with similar structures share similar interaction partners, we have developed a novel structure alignment method (Unialign) using the co-evolutionary information of the aligned proteins to predict the interaction between HIV-1 protein gp41 and human proteins. We applied Unialign to each of the five gp41 structures available in Protein Data Bank, structurally comparing them against all the human proteins in the PDB. Combining the structural hits with a human PPI database, we generated over 922 interaction predictions between this HIV-1 protein and human proteins. This predicted host proteins list could be very effective in assisting identification of interaction partners of HIV-1 experimentally.
Measurement of Human Sensitivity across the Vertical-Temporal Video Spectrum for Interlacing Filter Specification

Good quality conversion from progressive to interlaced video is highly relevant to today's broadcast systems, in which interlaced content is still common. The interlacing process is a form of down-sampling, and hence requires an anti-alias filter. For best results the anti-alias filter should be matched to the reconstruction filter, which is comprised of the display and the human visual system. Additionally, it must meet the technical requirements of the down sampling process. In this paper we present a novel method of measuring the combined response to interlacing artefacts that is simple and powerful. We use the results to derive an optimal anti-alias filter template, using a new region-growing technique that is specifically designed to match the measured response whilst keeping to the technical constraints of an interlaced sampling structure. Our results provide support for an existing, heuristically-defined filter, and show that the same filter could be used for a range of viewing distances.
Design and analysis of a dualband antenna for small LEO satellite applications

Dualband antennas have many applications in small LEO satellites since use of this type of antenna makes the satellite structure simpler, thus it leads to a low cost and easy construction. The separated turnstile antenna (STA) can provide saddle-shaped and hemispherical patterns. The ability of dual-band operation of this antenna has been investigated and a dual band STA has been designed for the satellite. Its ability in making an arbitrary Earth covering pattern for data communications and an omnidirectional pattern for satellite control has made a lot of applications in small LEO satellites. A wire model of the STA is analyzed by the moment method and its gain pattern, polarization and input impedance are calculated. Also, effects of antenna geometrical parameters on its pattern and input impedance have been researched and calculated.
Bottom-up visual saliency map using wavelet transform domain

Object or region based image processing can be performed more efficiently with information pertaining locations that are visually salient to human perception with the aid of a saliency map. The saliency map is a master topological map having the possible locations of objects or regions which a human perceived as important/salient. In this paper, a method to compute the saliency map in the wavelet transform domain is explored. Previous works involving saliency in this domain usually involves salient points, which are in fact accurate but they do not cover the area as a region and involve heavy repeated iterations. The method explored in this paper is compared to two state-of-art methods in which these methods involve the frequency domain. The presented method provides more accurate salient regions compared to the other two methods while retaining a resolution which the salient regions are visually identifiable.
Significance Test in Speaker Recognition Data Analysis with Data Dependency

To evaluate the performance of speaker recognition systems, a detection cost function defined as a weighted sum of the probabilities of type I and type II errors is employed. The speaker datasets may have data dependency due to multiple uses of the same subjects. Using the standard errors of the detection cost function computed by means of the two-layer nonparametric two-sample bootstrap method, a significance test is performed to determine whether the difference between the measured performance levels of two speaker recognition algorithms is statistically significant. While conducting the significance test, the correlation coefficient between two detection cost functions for two algorithms, respectively, is taken into account. Examples are provided.
Bridging the Abyss: Adding Value and Validity to Leadership Development through Action Learning--Cases-in-Point.

Action Learning (AL) is one instructional method used to bridge the gap between theory and practice in university courses. This study reports the results of a pilot test that applied AL techniques to an educational leadership preparation program. Participants were 47 doctoral students, most of whom were practicing administrators or teacher leaders. Participants worked on sets of actual problems provided by school districts and a community college (clients). The study employed Merriam's descriptive case-study method. Documents reviewed include email correspondence between participants and clients, memoranda, meetings minutes, participants' reflective journals, and presentations to clients. Results of the study persuaded facilitators and instructors to continue to use AL because, among other reasons, AL: (1) adds validity to the leadership program and to students by providing pathways to the workplace; (2) appeals to most participants; (3) allows participants to apply earlier academic learning to real problems; (4) enhances participants' understanding of themselves as leaders; (5) helps leaders deal with ambiguity and adversity; (6) helps leaders build community; (7) fosters better relationships with local educational agencies; and (8) provides insights into why it is difficult to change educational institutions. (Contains 55 references and an outline of contributions and limitations of various instructional methods.) (WFA) Reproductions supplied by EDRS are the best that can be made from the original document. Bridging the Abyss: Adding Value and Validity to Leadership Development Through Action LearningCases-in-Point Michele Acker-Hocevar, Associate Professor, John Pisapia, Professor, Eleni Coukos-Semmel, Research Director Florida Atlantic University Boca Raton, FL U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This is document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) Travel to this conference was made possible through a grant by the Pew and Toppel Foundations Paper Presented at the American Educational Association Annual Conference, April 2002, New Orleans BEST COPY AVAILA LE r)
Improving instruction cache performance in OLTP

Instruction-cache misses account for up to 40% of execution time in online transaction processing (OLTP) database workloads. In contrast to data cache misses, instruction misses cannot be overlapped with out-of-order execution. Chip design limitations do not allow increases in the size or associativity of instruction caches that would help reduce misses. On the contrary, the effective instruction cache size is expected to further decrease with the adoption of multicore and multithreading chip designs (multiple on-chip processor cores and multiple simultaneous threads per core). Different concurrent database threads, however, execute similar instruction sequences over their lifetime, too long to be captured and exploited in hardware. The challenge, from a software designer's point of view, is to identify and exploit common code paths across threads executing arbitrary operations, thereby eliminating extraneous instruction misses.In this article, we describe Synchronized Threads through Explicit Processor Scheduling (STEPS), a methodology and tool to increase instruction locality in database servers executing transaction processing workloads. STEPS works at two levels to increase reusability of instructions brought in the cache. At a higher level, synchronization barriers form teams of threads that execute the same system component. Within a team, STEPS schedules special fast context-switches at very fine granularity to reuse sets of instructions across team members. To find points in the code where context-switches should occur, we develop autoSTEPS, a code profiling tool that runs directly on the DBMS binary. STEPS can minimize both capacity and conflict instruction cache misses for arbitrarily long code paths.We demonstrate the effectiveness of our approach on Shore, a research prototype database system shown to be governed by similar bottlenecks as commercial systems. Using microbenchmarks on real and simulated processors, we observe that STEPS eliminates up to 96% of instruction-cache misses for each additional team thread and at the same time eliminates up to 64% of mispredicted branches by providing a repetitive execution pattern to the processor. When performing a full-system evaluation on real hardware using TPC-C, the industry-standard transactional benchmark, STEPS eliminates two-thirds of instruction-cache misses and provides up to 1.4 overall speedup.
Constructing Adaptive Indoor Radio Maps for Dynamic Wireless Environments

In received signal strength fingerprints based indoor localization systems, the radio map built by labeled wireless fingerprints is easily outdated over time, while re-calibrating the overall radio map is time consuming. To avoid the tedious task, we propose to employ manifold alignment to label the current radio map from outdated radio map, with the constraint of the Hidden Markov Model trained by trajectories of the received signal strength readings. Manifold alignment can align the low-dimensional manifold structures of two different data sets and transfer knowledge across them. Transition matrix generated by Hidden Markov Model is used to constrain the alignment of manifolds. The proposed algorithms are tested in a real world ZigBee environment. Experiment results show that our method outperforms state-of-the-art transfer learning algorithms.
Optimal Identification of Semi-Rigid Domains in Macromolecules from Molecular Dynamics Simulation

Biological function relies on the fact that biomolecules can switch between different conformations and aggregation states. Such transitions involve a rearrangement of parts of the biomolecules involved that act as dynamic domains. The reliable identification of such domains is thus a key problem in biophysics. In this work we present a method to identify semi-rigid domains based on dynamical data that can be obtained from molecular dynamics simulations or experiments. To this end the average inter-atomic distance-deviations are computed. The resulting matrix is then clustered by a constrained quadratic optimization problem. The reliability and performance of the method are demonstrated for two artificial peptides. Furthermore we correlate the mechanical properties with biological malfunction in three variants of amyloidogenic transthyretin protein, where the method reveals that a pathological mutation destabilizes the natural dimer structure of the protein. Finally the method is used to identify functional domains of the GroEL-GroES chaperone, thus illustrating the efficiency of the method for large biomolecular machines.
Behavioural/Macro Modelling To Speed-Up Analogue Fault Simulation

1 University of Southampton, Department of Electronics and Computer Science, Southampton, UK, SO17 1BJ Abstract The main difficulty in generating test patterns for analogue and mixed-signal circuits is fault simulation. Analogue fault simulation is much slower than the digital equivalent. Two of the techniques to speed up the analogue fault simulation process are: fault dropping/collapsing, in which faults that have similar circuit responses compared with the fault-free circuit response and/or with another faulty circuit response are considered equivalent; and behavioural/macro modelling, whereby par ts of the circuit are modelled at a more abstract level, therefore reducing the complexity and the simulation time. This paper discusses behavioural/macro modelling in order to speed-up fault simulation for analogue circuits.