Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective DNA-Directed Assembly on Dual-Functionalized Microparticles

The bottom-up assembly of functional devices requires novel building blocks to facilitate the incorporation of functional and structural hierarchy. Anisotropic building blocks can substantially broaden the creation of self-assembled devices with unique properties because of their morphological and/or chemical asymmetry. In this regard, we have created microspheres with one hemispherical face exposing silica and the other exposing gold. These microspheres were formed by the shadow deposition of gold onto silica microspheres. The two chemical surfaces allowed use of different surface reactions—silane chemistry for the silica side and thiol chemistry for the gold side—for immobilizing different oligonucleotide sequences on each of the two faces. These dual-functionalized microspheres were used in the selective orthogonal assembly of fluorophore-tagged target oligonucleotides. The DNA-directed assembly was confirmed by confocal microscopy of the microspheres. In essence, employing DNA as the linker molecule, these “Janus” particles can be assembled into various novel 1-D, 2-D, and 3-D structures, which are difficult to realize using symmetric building blocks.
Data Encryption using DES/Triple-DES Functionality in Spartan-II FPGAs

Summary Today’s connected society requires secure data encryption devices to preserve data privacy and authentication in critical applications. Of the several data encryption types, Data Encryption Standard (DES) and its variant Triple-DES (TDES) have emerged to be the most commonly used in varying applications. The Spartan™-II devices with their extensive features and cost effectiveness compete effectively against ASICs and ASSPs. Through the value proposition of the DES/TDES IP in a Spartan-II FPGA, the programmable ASSP message is further confirmed. There is an immense value in integrating critical IP solutions like Discrete Cosine Transform/Inverse DCT (DCT/IDCT) and DES within a Xilinx FPGA to enhance performance and security in communication applications. A FPGA-based DES/TDES solution provides the necessary scalability and flexibility to handle all these applications and allows for tracking of new standards.
Cyrptographic logical relations 1

Using contextual equivalence (a.k.a. observational equivalence) to specify security properties is an important idea in the field of formal verification of cryptographic protocols. While contextual equivalence is difficult to prove directly, one is usually able to deduce it using so-called logical relations in typed -calculi. We apply this technique to the cryptographic metalanguage — an extension of Moggi’s computational -calculus, where we use Stark’s model for name creation to explore the difficult aspect of dynamic key generation. The categorical construction of logical relations for monadic types (by Goubault-Larrecq et al.) then allows us to derive logical relations over the category Set I . Although Set I is a perfectly adequate model of dynamic key generation, it lacks in some aspects when we study relations between programs in the metalanguage. This leads to an interesting exploration of what should be the proper category to consider. We show that, to define logical relations in the cryptographic metalanguage, a better choice of category is Set I
Combination of TMS and fMRI reveals a specific pattern of reorganization in M1 in patients after complete SCI

Purpose: After a spinal cord injury (SCI) which was complete deafferentation of the body representation caudal to the lesion height results in drastic changes in the cortical representation. The underlaying processes are incompletely understood. Methods: We investigated cortical representation sites of upper limb muscles using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) in five patients suffering from thoracic complete SCI and one with an incomplete SCI in the height of L1. Results: In comparison to healthy controls fMRI demonstrated a displacement of elbow movement representations in the precentral gyrus in patients with complete SCI into the direction of the deafferented cortical thoracic representation. Changes increased with time after the incidence of SCI. TMS revealed reduced excitability and prolonged silent periods for muscles more distant to the deafferented area. Conclusions: Whereas fMRI demonstrated changes in representation sites adjacent to the deafferented area, TMS excitability changes were also observed more distant to the deafferented area and silent periods were prolonged in comparison to healthy controls. TMS changes might depend on both: the distance to the deafferented area and the time of persistence of deafferentation.
Bank Corporate Loan Pricing Following the Subprime Crisis

The massive losses that banks incurred with the meltdown of the subprime mortgage market have raised concerns about their ability to continue lending to corporations. We investigate these concerns. We find that firms paid higher loan spreads during the subprime crisis. Importantly, the increase in loan spreads was higher for firms that borrowed from banks that incurred larger losses. These results hold after we control for firm-, bank-, and loan-specific factors, and account for endogeneity of bank losses. These findings, together with our evidence that borrowers took out smaller loans during the crisis when they borrowed from banks that incurred larger losses, lend support to the concerns about bank lending following their subprime losses. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. , Oxford University Press.
Online Classification via Self-Organizing Space Partitioning

The authors study online supervised learning under the empirical zero-one loss and introduce a novel classification algorithm with strong theoretical guarantees. The proposed method is a highly dynamical self-organizing decision tree structure, which adaptively partitions the feature space into small regions and combines (takes the union of) the local simple classification models specialized in those regions. The authors' approach sequentially and directly minimizes the cumulative loss by jointly learning the optimal feature space partitioning and the corresponding individual partition-region classifiers. They mitigate overtraining issues by using basic linear classifiers at each region while providing a superior modeling power through hierarchical and data adaptive models. The computational complexity of the introduced algorithm scales linearly with the dimensionality of the feature space and the depth of the tree. Their algorithm can be applied to any streaming data without requiring a training phase or a priori information, hence processing data on-the-fly and then discarding it. Therefore, the introduced algorithm is especially suitable for the applications requiring sequential data processing at large scales/high rates. The authors present a comprehensive experimental study in stationary and nonstationary environments. In these experiments, their algorithm is compared with the state-of-the-art methods over the well-known benchmark datasets and shown to be computationally highly superior. The proposed algorithm significantly outperforms the competing methods in the stationary settings and demonstrates remarkable adaptation capabilities to nonstationarity in the presence of drifting concepts and abrupt/sudden concept changes.
Commercializing Basic Science as a Competitor or Complement of Academic Accomplishment? The Case of Max Planck Directors

This paper studies the invention disclosure, patenting, licensing and publication activities of directors of Max Planck Institutes from 1975 to 2004. Max Planck directors represent a highly selected sample of German academic researchers over a broad range of disciplines. The paper analyzes whether inventing Max Planck directors are more or less prolific authors than their noninventing peers, whether there are differences in the effects of patented versus non-patented technologies, and how the licensing and commercialization of inventions relates to the publication output of inventors.
A Systematic Review of the Prevalence of Schizophrenia

Background Understanding the prevalence of schizophrenia has important implications for both health service planning and risk factor epidemiology. The aims of this review are to systematically identify and collate studies describing the prevalence of schizophrenia, to summarize the findings of these studies, and to explore selected factors that may influence prevalence estimates. Methods and Findings Studies with original data related to the prevalence of schizophrenia (published 1965–2002) were identified via searching electronic databases, reviewing citations, and writing to authors. These studies were divided into “core” studies, “migrant” studies, and studies based on “other special groups. ” Between- and within-study filters were applied in order to identify discrete prevalence estimates. Cumulative plots of prevalence estimates were made and the distributions described when the underlying estimates were sorted according to prevalence type (point, period, lifetime, and lifetime morbid risk). Based on combined prevalence estimates, the influence of selected key variables was examined (sex, urbanicity, migrant status, country economic index, and study quality). A total of 1,721 prevalence estimates from 188 studies were identified. These estimates were drawn from 46 countries, and were based on an estimated 154,140 potentially overlapping prevalent cases. We identified 132 core studies, 15 migrant studies, and 41 studies based on other special groups. The median values per 1,000 persons (10%–90% quantiles) for the distributions for point, period, lifetime, and lifetime morbid risk were 4.6 (1.9–10.0), 3.3 (1.3–8.2), 4.0 (1.6–12.1), and 7.2 (3.1–27.1), respectively. Based on combined prevalence estimates, we found no significant difference (a) between males and females, or (b) between urban, rural, and mixed sites. The prevalence of schizophrenia in migrants was higher compared to native-born individuals: the migrant-to-native-born ratio median (10%–90% quantile) was 1.8 (0.9–6.4). When sites were grouped by economic status, prevalence estimates from “least developed” countries were significantly lower than those from both “emerging” and “developed” sites (p = 0.04). Studies that scored higher on a quality score had significantly higher prevalence estimates (p = 0.02). Conclusions There is a wealth of data about the prevalence of schizophrenia. These gradients, and the variability found in prevalence estimate distributions, can provide direction for future hypothesis-driven research.
Phosphoproteomic Analysis of the Developing Mouse Brain*S

Proper development of the mammalian brain requires the precise integration of numerous temporally and spatially regulated stimuli. Many of these signals transduce their cues via the reversible phosphorylation of downstream effector molecules. Neuronal stimuli acting in concert have the potential of generating enormous arrays of regulatory phosphoproteins. Toward the global profiling of phosphoproteins in the developing brain, we report here the use of a mass spectrometry-based methodology permitting the first proteomic-scale phosphorylation site analysis of primary animal tissue, identifying over 500 protein phosphorylation sites in the developing mouse brain.
Two-dimensional device simulation of polysilicon thin film transistors

Electrical characteristics of polysilicon thin-film transistors (poly-Si TFTs) are strongly affected by density of trap states in the energy bandgap. Based on the single exponential deep state density, two-dimensional simulation is used to construct an n-channel poly-Si TFT and simulate its DC and AC characteristics. Subthreshold swing, threshold voltage and gate capacitances are included. Compact models are derived that can describe the unique behaviors of the aforementioned parameters based on the observation of simulation results.
Directional constant beamwidth beamforming by second order cone programming constraints

A directional constant beamwidth (DCB) beamforming algorithm is introduced, which keeps the beamwidth unchanged when the mainlobe is steered to a large range of angles. The algorithm is based on the optimization theory that minimizing the sidelobe level subjects to the minimum mean square error between the designed mainlobe under all directions and the corresponding reference mainlobe. The introduced algorithm can be written in the form of second order cone programming (SOCP) and can be solved by some certain matlab toolboxes. It is shown by simulation that the new algorithm can form the directional constant beamwidth beampattern effectively under the designed mainlobe direction from -60° to +60°.
Sound Synthesis of the Geomungo Using Digital Waveguide Modeling

This paper presents a sound synthesis method for the geomungo, a Korean traditional pluckedstring instrument, based on physical modeling. Commuted waveguide synthesis method is used as a basic synthesis algorithm in this work. Recorded geomungo tones are analyzed by estimating frequency responses and fundamental frequency curves. A synthesis model proposed here consists of a fractional delay allpass filter, a loss filter which combines a ripple filter and an one-pole filter, and a digital delay line. Calibration of parameters in the model is done by minimizing the difference between the magnitude response of the designed filter and estimated losses of harmonic partials. An time-varying model which has Lagrange interpolation FIR filter as a fractional delay filter, time-varying loss filter and delay line, and a gain factor to preserve energy is proposed and evaluated to generate geomungo tones which have fluctuating pitches. Gain control using a sinusoidal function in the time-varying loss filter is also discussed. In addition, a hybrid-modal synthesis model which synthesizes the signal with the resonators at low frequencies and with the digital waveguide model at high frequencies is proposed. Finally, real-time sound synthesis application is implemented based on The Synthesis toolkit(STK). The former shows that physical modeling based on digital waveguide theory is an effective method to synthesize geomungo sounds.
The First Sequenced Carnivore Genome Shows Complex Host-Endogenous Retrovirus Relationships

Host-retrovirus interactions influence the genomic landscape and have contributed substantially to mammalian genome evolution. To gain further insights, we analyzed a female boxer (Canis familiaris) genome for complexity and integration pattern of canine endogenous retroviruses (CfERV). Intriguingly, the first such in-depth analysis of a carnivore species identified 407 CfERV proviruses that represent only 0.15% of the dog genome. In comparison, the same detection criteria identified about six times more HERV proviruses in the human genome that has been estimated to contain a total of 8% retroviral DNA including solitary LTRs. These observed differences in man and dog are likely due to different mechanisms to purge, restrict and protect their genomes against retroviruses. A novel group of gammaretrovirus-like CfERV with high similarity to HERV-Fc1 was found to have potential for active retrotransposition and possibly lateral transmissions between dog and human as a result of close interactions during at least 10.000 years. The CfERV integration landscape showed a non-uniform intra- and inter-chromosomal distribution. Like in other species, different densities of ERVs were observed. Some chromosomal regions were essentially devoid of CfERVs whereas other regions had large numbers of integrations in agreement with distinct selective pressures at different loci. Most CfERVs were integrated in antisense orientation within 100 kb from annotated protein-coding genes. This integration pattern provides evidence for selection against CfERVs in sense orientation relative to chromosomal genes. In conclusion, this ERV analysis of the first carnivorous species supports the notion that different mammals interact distinctively with endogenous retroviruses and suggests that retroviral lateral transmissions between dog and human may have occurred.
A new approach for parallel region growing algorithm in image segmentation using MATLAB on GPU architecture

Image segmentation is the process of dividing a digital image into multiple segments or clusters. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful for analysis. Image segmentation is typically used to separate the region of interest from the input image. So we have different region of interest for different applications. None of the algorithm will satisfy this global application to give better result from the computational point of view. So Segmentation is one of the challenging issues in Digital Image Processing. Many general-purpose algorithms have been developed for image segmentation in which Region Growing is one of them. This paper presents a comparison between serial execution of the Region growing algorithm and Parallel execution of it on CUDA platform (provided by nvidia) integration with MATLAB. Finally we are able to solve the computational complexity and improve the real-time performance of Region Growing using GPU computing. MATLAB is used as a platform for serial execution and CUDA is used as a platform for parallel execution.
Stitched Panoramas from Toy Airborne Video Cameras

Effective panoramic photographs are taken from vantage points that are high. High vantage points have recently become easier to reach as the cost of quadrotor helicopters has dropped to nearly disposable levels. Although cameras carried by such aircraft weigh only a few grams, their low-quality video can be converted into panoramas of high quality and high resolution. Also, the small size of these aircraft vastly reduces the risks inherent to flight.
Nonlinear system identification using adaptive Chebyshev neural networks

A new adaptive Chebyshev neural networks (ACNN) algorithm for the purpose of complex nonlinear system identification was proposed. In the proposed algorithm, the activation function of hidden units was defined by Chebyshev polynomials in the neural networks. The efficient algorithm for complex nonlinear system identification was constructed, which integrated Chebyshev neural networks with adaptive learning strategy to improve the identification accuracy and convergence rate. Furthermore, the networks algorithm was improved so that the applications becomed extensive. Then the ACNN directly learned dynamic characters of nonlinear system and identified it. The simulation results show that the ACNN algorithm have much less computation and high accuracy in the problem of complex nonlinear system identification.
Distributed Load Balancing Algorithm in Wireless Networks Distributed Load Balancing Algorithm in Wireless Networks

Title of thesis: DISTRIBUTED LOAD BALANCING ALGORITHM IN WIRELESS NETWORKS Alireza Sheikhattar, Master of Science, 2014 Thesis directed by: Dr. Mehdi Kalantari Department of Electrical and Computer Engineering As communication networks scale up in size, complexity and demand, effective distribution of the traffic load throughout the network is a matter of great importance. Load balancing will enhance the network throughput and enables us to utilize both communication and energy resources more evenly through an efficient redistribution of traffic load across the network. This thesis provides an algorithm for balancing the traffic load in a general network setting. Unlike most of state-of-the-art algorithms in load balancing context, the proposed method is fully distributed, eliminating the need to collect information at a central node and thereby improving network reliability. The effective distribution of load is realized through solving a convex optimization problem where the p-norm of network load is minimized subject to network physical constraints. The optimization solution relies on the Alternating Direction Method of Multipliers (ADMM), which is a powerful tool for solving distributed convex optimization problems. A three-step ADMM-based iterative scheme is derived from suitably reformulated form of p-norm problem. The distributed implementation of the proposed algorithm is further elaborated by introducing a projection step and an initialization setup. The projection step involves an inner-loop iterative scheme to solve linear subproblems. In a distributed setting, each iteration step requires communication among all neighboring nodes. Due to high energy consumption of node-to-node communication, it is most appealing to devise a fast and computationally efficient iterative scheme which can converge to optimal solution within a desired accuracy by using as few iteration steps as possible. A fast convergence iterative scheme is presented which shows superior convergence performance compared to conventional methods. Inspired by fast propagation of waves in physical media, this iterative scheme is derived from partial differential equations for propagation of electrical voltages and currents in a transmission line. To perform these iterations, all nodes should have access to an acceleration parameter which relies on the network topology. The initialization stage is developed in order to overcome the last challenging obstacle toward achieving a fully distributed algorithm. DISTRIBUTED LOAD BALANCING ALGORITHM IN WIRELESS NETWORKS
Avoiding Whitewashing in Unstructured Peer-to-Peer Resource Sharing Network

In peer-to-peer file sharing network, it is hard to distinguish between a legitimate newcomer and a whitewasher. This makes whitewashing a big problem in peer-to-peer networks. Although the problem of whitewashing can be solved using permanent identities, it may take away the right of anonymity for users. In this paper, we a have proposed a novel algorithm to avoid this problem when network uses free temporary identities. In this algorithm, the initial reputation is adjusted according to the level of whitewashing in the network.
A New Scheme on Privacy-Preserving Distributed Decision-Tree Mining

Privacy-preserving data mining is discovering accurate patterns and rules without precise access to the original data. This paper focuses on privacy-preserving research in the situation of distributed decision-tree mining, and presents a decision-tree mining algorithm based on homomorphic encryption technology, which can get accurate mining effect in the premise of no sharing of private information among mining participators. Theoretical analysis and experiment results show that this algorithm can provide good capability of privacy-preserving, accuracy and efficiency.