Miracle-dz/UniFAR-FTUs huggingface dataset:
Phonon thermal conduction in graphene: Role of Umklapp and edge roughness scattering

We investigated theoretically the phonon thermal conductivity of single-layer graphene. The phonon dispersion for all polarizations and crystallographic directions in graphene lattice was obtained using the valenceforce field method. The three-phonon Umklapp processes were treated exactly using an accurate phonon dispersion and Brillouin zone, and accounting for all phonon relaxation channels allowed by the momentum and energy conservation laws. The uniqueness of graphene was reflected in the two-dimensional phonon density of states and restrictions on the phonon Umklapp scattering phase-space. The phonon scattering on defects and graphene edges has also been included in the model. The calculations were performed for the Gruneisen parameter, which was determined from the ab initio theory as a function of the phonon wave vector and polarization branch, and for a range of values from experiments. It was found that the near roomtemperature thermal conductivity of single-layer graphene, calculated with a realistic Gruneisen parameter, is in the range 2000‐5000 W /mK depending on the flake width, defect concentration and roughness of the edges. Owing to the long phonon mean free path the graphene edges produce strong effect on thermal conductivity even at room temperature. The obtained results are in good agreement with the recent measurements of the thermal conductivity of suspended graphene.
Construction of a dengue virus type 4 reporter replicon and analysis of temperature-sensitive mutations in non-structural proteins 3 and 5

Replicon systems have been useful to study mechanisms of translation and replication of flavivirus RNAs. In this study, we constructed a dengue virus 4 replicon encoding a Renilla luciferase (Rluc) reporter, and six single-residue substitution mutants were generated: L128F and S158P in the non-structural protein (NS) 3 protease domain gene, and N96I, N390A, K437R and M805I in the NS5 gene. The effects of these substitutions on viral RNA translation and/or replication were examined by measuring Rluc activities in wild-type and mutant replicon RNA-transfected Vero cells incubated at 35, 37 and 39 °C. Our results show that none of the mutations affected translation of replicon RNAs; however, L128F and S158P of NS3 at 39 °C, and N96I of NS5 at 37 and 39 °C, presented temperature-sensitive (ts) phenotypes for replication. Furthermore, using in vitro methyltransferase assays, we identified that the N96I mutation in NS5 exhibited a ts phenotype for N7-methylation, but not for 2′-O-methylation.
Software Engineering in Public Health: Opportunities and Challenges

Based on the state of the art of health information systems (HIS) and the recent advancement in information and communication technology (ICT), we discuss the possible future HIS and their potential to transform the global health services. We also show why with all of the enabling technologies rapidly changing so many aspects of our daily life and environment, the actual impact of HIS on healthcare is much more limited than we think. We discuss the main reasons from the software engineering perspective and identify the challenges in the design and implementation of HIS to realize their perceived potentials. In the discussion of the causes of the challenges, we propose to apply model-driven software engineering methodologies to the design and analysis of future HIS, with the support of formal modeling and verification techniques and tools.
Deep brain stimulation for Parkinson's disease: Surgical technique and perioperative management

Deep brain stimulation (DBS) is a widely accepted therapy for medically refractory Parkinson's disease (PD). Both globus pallidus internus (GPi) and subthalamic nucleus (STN) stimulation are safe and effective in improving the symptoms of PD and reducing dyskinesias. STN DBS is the most commonly performed surgery for PD as compared to GPi DBS. Ventral intermediate nucleus (Vim) DBS is infrequently used as an alternative for tremor predominant PD patients. Patient selection is critical in achieving good outcomes. Differential diagnosis should be emphasized as well as neurological and nonneurological comorbidities. Good response to a levodopa challenge is an important predictor of favorable long‐term outcomes. The DBS surgery is typically performed in an awake patient and involves stereotactic frame application, CT/MRI imaging, anatomical targeting, physiological confirmation, and implantation of the DBS lead and pulse generator. Anatomical targeting consists of direct visualization of the target in MR images, formula‐derived coordinates based on the anterior and posterior commissures, and reformatted anatomical stereotactic atlases. Physiological verification is achieved most commonly via microelectrode recording followed by implantation of the DBS lead and intraoperative test stimulation to assess benefits and side effects. The various aspects of DBS surgery will be presented. © 2006 Movement Disorder Society
Emulator-oriented tiny processors for unreliable post-silicon devices: A case study

Although various post-silicon devices have been invested in recent years, they still have a major issue of reliability. Because circuit area is an essential factor of reliability, especially for such unreliable post-silicon devices, it is desired to build small circuits which can reuse as many today's application programs as possible even if the performance is not very high. This paper presents the very first work to study novel, efficient techniques of emulating wider-bit guest processors (e.g., 32-bit) on a narrower-bit host processor (e.g., 8-bit) with very limited hardware resources while mitigating performance degradation. We propose three types of emulator-oriented tiny processors varying in available hardware resources and reliability enhancement approaches. Quantitative evaluation and discussions are done for comparing those three processors. We believe that this work will will be a good help of making breakthrough for further development of new device technologies and computers based on them.
A Chaotic Cryptosystem for Images Based on Henon and Arnold Cat Map

The rapid evolution of imaging and communication technologies has transformed images into a widespread data type. Different types of data, such as personal medical information, official correspondence, or governmental and military documents, are saved and transmitted in the form of images over public networks. Hence, a fast and secure cryptosystem is needed for high-resolution images. In this paper, a novel encryption scheme is presented for securing images based on Arnold cat and Henon chaotic maps. The scheme uses Arnold cat map for bit- and pixel-level permutations on plain and secret images, while Henon map creates secret images and specific parameters for the permutations. Both the encryption and decryption processes are explained, formulated, and graphically presented. The results of security analysis of five different images demonstrate the strength of the proposed cryptosystem against statistical, brute force and differential attacks. The evaluated running time for both encryption and decryption processes guarantee that the cryptosystem can work effectively in real-time applications.
Performance bounds of compressed sensing recovery algorithms for sparse noisy signals

Recently, the performance bounds of the compressed sensing (CS) recovery algorithms have been investigated in the noisy setting. However, most of the papers only focus on the noisy measurement model where the signal is noiseless and the noise enters after the CS operation. The noisy signal model where both the signal and the compressed measurements are contaminated by the different noises is not considered. This paper works on the noisy signal model and provides the performance bounds for the following popular recovery algorithms: thresholding and orthogonal matching pursuit (OMP), Dantzig selector (DS) and basis pursuit denoising (BPDN). The performance of the recovery algorithms is quantified as the ℓ2 distance between the reconstructed signal and the true noisy signal. Next, the impacts of the noise are analyzed on the basis of the quantified performance. The analysis results show that the effective way to restrain the impact of the noise is to choose the measurement matrix with low correlation between the columns or the rows. Finally, the theoretical bounds are verified with numerical simulations by calculating the mean-squared-error for the different noise variances. The simulation results show that OMP owns the better performance than the other three recovery algorithms under the noisy signal model.
An Optical Flow-Based Approach to Robust Face Recognition Under Expression Variations

Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize faces with expressions under the restriction of one single training sample per class. A constrained optical flow algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation, has been developed for face recognition from expressional face images. In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intraperson optical flow and the synthesized face image in a probabilistic framework. Our experimental results show that the proposed system improves the accuracy of face recognition from expressional face images.
Static and Dynamic Structural Correlations in Graphs

Real-life graphs not only contain nodes and edges, but also have events taking place, e.g., product sales in social networks. Among different events, some exhibit strong correlations with the network structure, while others do not. Such structural correlations will shed light on viral influence existing in the corresponding network. Unfortunately, the traditional association mining concept is not applicable in graphs because it only works on homogeneous data sets like transactions and baskets. We propose a novel measure for assessing such structural correlations in heterogeneous graph data sets with events. The measure applies hitting time to aggregate the proximity among nodes that have the same event. To calculate the correlation scores for many events in a large network, we develop a scalable framework, called gScore, using sampling and approximation. By comparing to the situation where events are randomly distributed in the same network, our method is able to discover events that are highly correlated with the graph structure. We test gScore's effectiveness by synthetic events on the DBLP coauthor network and report interesting correlation results in a social network extracted from TaoBao.com, the largest online shopping network in China. Scalability of gScore is tested on the Twitter network. Since an event is essentially a temporal phenomenon, we also propose a dynamic measure, which reveals structural correlations at specific time steps and can be used for discovering detailed evolutionary patterns.
Cleavage Entropy as Quantitative Measure of Protease Specificity

A purely information theory-guided approach to quantitatively characterize protease specificity is established. We calculate an entropy value for each protease subpocket based on sequences of cleaved substrates extracted from the MEROPS database. We compare our results with known subpocket specificity profiles for individual proteases and protease groups (e.g. serine proteases, metallo proteases) and reflect them quantitatively. Summation of subpocket-wise cleavage entropy contributions yields a measure for overall protease substrate specificity. This total cleavage entropy allows ranking of different proteases with respect to their specificity, separating unspecific digestive enzymes showing high total cleavage entropy from specific proteases involved in signaling cascades. The development of a quantitative cleavage entropy score allows an unbiased comparison of subpocket-wise and overall protease specificity. Thus, it enables assessment of relative importance of physicochemical and structural descriptors in protease recognition. We present an exemplary application of cleavage entropy in tracing substrate specificity in protease evolution. This highlights the wide range of substrate promiscuity within homologue proteases and hence the heavy impact of a limited number of mutations on individual substrate specificity.
Seamless And Always-on Security in a Bring-Your-Own-Application World

This paper describes a usable security experiment with xmail, a prototype that enhances Gmail's webmail client security with end-to-end encryption making user content unreadable by the service provider. The prototype follows a seamless and always-on approach requiring minimal changes to the Gmail user interface and task flow. Many studies have pointed out problems with email security usability, but they usually take an adversarial context as reference. The focus of this work is on peacetime information disclosure with service providers when adopting third party web applications. This context is a remarkable characteristic in the trending Bring-Your-Own-Application phenomenon and raises many privacy and confidentiality concerns. Although users could successful benefit from the security approach and protect their messages without much extra work, many long-term adoption obstacles were detected in informal user observations.
Association of DCDC2 Polymorphisms with Normal Variations in Reading Abilities in a Chinese Population

The doublecortin domain-containing 2 (DCDC2) gene, which is located on chromosome 6p22.1, has been widely suggested to be a candidate gene for dyslexia, but its role in typical reading development over time remains to be clarified. In the present study, we explored the role of DCDC2 in contributing to the individual differences in reading development from ages 6 to 11 years by analysing data from 284 unrelated children who were participating in the Chinese Longitudinal Study of Reading Development (CLSRD). The associations of eight single nucleotide polymorphisms (SNPs) in DCDC2 with the latent intercept and slope of children’s reading scores were examined in the first step. There was significant support for an association of rs807724 with the intercept for the reading comprehension measure of reading fluency, and the minor “G” allele was associated with poor reading performance. Next, we further tested the rs807724 SNP in association with the reading ability at each tested time and revealed that, in addition to significant associations with the two main reading measures (reading fluency and Chinese character reading) over multiple testing occasions, this SNP also showed associations with reading-related cognitive skills, including morphological production, orthographic judgment and phonological processing skills (rapid number naming, phoneme deletion, and tone detection). This study provides support for DCDC2 as a risk gene for reading disability and suggests that this gene is also operative for typical reading development in the Han population.
Modeling International Trends in Energy Efficiency and Carbon Emissions

This study uses a stochastic production frontier to model energy efficiency trends, in 85 countries over a 37 year period. No structure is imposed on technological change over time, although differences in technology level across the countries are modelled as a stochastic function of explanatory variables. These variables are selected by a literature survey and a theoretical model of energy-efficient technology choice. An improvement in a country’s energy efficiency is measured as a reduction in energy intensity, while holding constant that economy’s mix of inputs and outputs. All other things remaining constant, the country using the least energy per unit output is on the global best-practice frontier. The model is used to derive decompositions of energy intensity and carbon emissions. It also examines whether there is a convergence across countries. The study shows that energy efficiency rises with increasing general total factor productivity. Energy efficiency is also higher in countries with undervalued currencies. Higher fossil fuel reserves are associated with lower energy efficiency. Energy efficiency converges over time across countries. Technological change was the most important factor counteracting the effect of economic growth in increasing global energy- use increase and carbon emissions.
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems

In this paper, we describe our approach of enabling an interactive dialogue system to recognize user emotion and sentiment in real-time. These modules allow otherwise conventional dialogue systems to have “empathy” and answer to the user while being aware of their emotion and intent. Emotion recognition from speech previously consists of feature engineering and machine learning where the ﬁrst stage causes delay in decoding time. We describe a CNN model to extract emotion from raw speech input without feature engineering. This approach even achieves an impressive average of 65.7% accuracy on six emotion categories, a 4.5% improvement when compared to the conventional feature based SVM classiﬁcation. A separate, CNN-based sentiment analysis module recognizes sentiments from speech recognition results, with 82.5 F-measure on human-machine dialogues when trained with out-of-domain data.
Current-sensor-based predictive current control of a synchronous reluctance motor fed by a six-switch three-phase inverter

In this study, we propose a current-sensor-based predictive current control (CSBPCC) for six-switch three-phase (SSTP) inverter-fed synchronous reluctance motor (SynRM) drives. Generally speaking, two current sensors are used for detection of a- and b-phase stator currents of a SynRM drive system, respectively. In each sampling interval, the stator current will be read once rather than twice. After that, the past current variation can be saved and classified according to different switching states generated by the inverter. Next, the sampled stator current and the previously saved current variations are used to predict the future stator current without calculating the back-EMF or any motor's parameter. The next switching state can be chosen by minimizing a cost function to control the inverter directly in the next sampling interval. For comparison purposes, a traditional hysteresis current control (HCC) and the proposed predictive current control are adopted to test their current tracking performance under different operations through some simulations.
Modeling and Validating Train System Applications Using Statemate and Live Sequence Charts

1 now at OSC – OFFIS Systems & Consulting, Oldenburg, Germany, bohn@o-s-c.de 2 This research was partially supported by the German Research Council (DFG) under grant number DA 206/7-2 within the priority program “Integration of Specification Techniques with Engineering Applications”. ABSTRACT The European CENELEC norm now requires train system applications with critical safety integrity levels to be developed using formal methods, in particular “supporting various forms of analysis to check for different correctness properties”. In doing so, the CENELEC standard reflects the increasing need for advanced validation techniques in developing in particular also on board train system applications, which increasingly involve both complex and safety critical control units. This paper describes a methodology for developing train system applications based on powerful extensions of the Statemate modeling tool from I-Logix Inc. The extension come in three dimensions:
Input Sparsity Time Low-rank Approximation via Ridge Leverage Score Sampling

We present a new algorithm for finding a near optimal low-rank approximation of a matrix $A$ in $O(nnz(A))$ time. Our method is based on a recursive sampling scheme for computing a representative subset of $A$'s columns, which is then used to find a low-rank approximation. This approach differs substantially from prior $O(nnz(A))$ time algorithms, which are all based on fast Johnson-Lindenstrauss random projections. It matches the guarantees of these methods while offering a number of advantages. Not only are sampling algorithms faster for sparse and structured data, but they can also be applied in settings where random projections cannot. For example, we give new single-pass streaming algorithms for the column subset selection and projection-cost preserving sample problems. Our method has also been used to give the fastest algorithms for provably approximating kernel matrices [MM16].
Expected Transmission Energy Routing Metric for Wireless Mesh Senor Network

mesh is a network topology that achieves high throughput and stable intercommunication. With great potential, it's expected to be the key architecture of the future network. Wireless sensor networks are an active research area with numerous workshops and conferences arranged each year. This paper designs a new routing metric for wireless mesh sensor networks on the study of traditional metrics. Results from simulation experiments reveal that the new metric algorithm improves the energy balance of the whole network and extends the lifetime of wireless mesh sensor network.
Zinc(II) and Nickel(II) Benzoate Complexes from the Use of 1-methyl-4,5-diphenylimidazole

Two new complexes, [Zn(O2CPh)2(L)2]·2MeOH (1 ·2MeOH) and [Ni2(O2CPh))4(L)2]·2MeCN (2 ·2MeCN), have been synthesized and characterized by X-ray analysis in the course of an ongoing investigation of the MII/X−/L [MII = Co, Ni, Cu, Zn; X− = Cl−, Br−, I−, NCS−, NO3 −, N3 −, PhCO2 −; L = 1-methyl-4,5-diphenylimidazole] reaction system, aiming at understanding and assessing the relative strength and the way in which the intermolecular interactions control the supramolecular organization of these compounds. In the mononuclear complex 1 ·2MeOH, the benzoate ion acts as a monodentate ligand resulting in a distorted tetrahedral N2O2 coordination environment. Complex 2 ·2MeCN exhibits a dinuclear paddle-wheel structure; each NiII has a square pyramidal NiNO4 chromophore with four benzoate oxygens in the basal plane and the pyridine-type nitrogen atom of one ligand L at the apex. The structure of 1 ·2MeOH is stabilized by intramolecular π-π interactions between aromatic rings of adjacent 4,5-diphenylimidazole moieties; it is a feature also evidenced in similar compounds of the type [MX2L2].
A Fuzzy Logic Approach to Computer Software Source Code Authorship Analysis

Software source code authorship analysis has become an important area in recent years with promising applications in both the legal sector (such as proof of ownership and software forensics) and the education sector (such as plagiarism detection and assessing style). Authorship analysis encompasses the sub-areas of author discrimination, author characterization, and similarity detection (also referred to as plagiarism detection). While a large number of metrics have been proposed for this task, many borrowed or adapted from the area of computational linguistics, there is a difficulty with capturing certain types of information in terms of quantitative measurement. Here it is proposed that existing numerical metrics should be supplemented with fuzzy-logic linguistic variables to capture more subjective elements of authorship, such as the degree to which comments match the actual source code’s behavior. These variables avoid the need for complex and subjective rules, replacing these with an expert’s judgement. Fuzzy-logic models may also help to overcome problems with small data sets for calibrating such models. Using authorship discrimination as a test case, the utility of objective and fuzzy measures, singularly and in combination, is assessed as well as the consistency of the measures between counters.