Miracle-dz/UniFAR-FTUs huggingface dataset:
Bulk and homoepitaxial films of III-V nitride semiconductors : Optical studies

It is well accepted that point and extended defects play an important role on the properties of III-V nitride semiconductors. It is also essential to understand the defect formation mechanisms to obtain the necessary material control to reach the full realization of the technological potential of the wide bandgap material system. Due to the nature of the centers involved a detailed and extensive investigation must be performed in controlled samples. In this work, results obtained using a combination of defect-sensitive techniques employed to detect, identify, and verify the role of defects and impurities in the structural, optical, and electronic properties of thick freestanding GaN, bulk AlN, and homoepitaxial layers are reviewed. The sharpness and line-shapes of XRD and Raman scattering lines of AlN and GaN were employed as figures of merit to evaluate the crystalline quality and homogeneity of the bulk and epitaxial layers. Detailed luminescence studies of AlN and GaN, grown by different techniques, and homoepitaxial films show evidence of the pervasive nature of some defects.
2 Aspect Extraction with Sememe Attentions ( AESA )

Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts. To enhance the performance on aspect extraction, leveraging lexical semantic resources is a possible solution to such challenge. In this paper, we present an unsupervised neural framework that leverages sememes to enhance lexical semantics. The overall framework is analogous to an autoenoder which reconstructs sentence representations and learns aspects by latent variables. Two models that form sentence representations are proposed by exploiting sememes via (1) a hierarchical attention; (2) a context-enhanced attention. Experiments on two real-world datasets demonstrate the validity and the effectiveness of our models, which significantly outperforms existing baselines.
A An Innovative Clustering Method for MANET Based on Cluster Convergence

Mobile Ad hoc Networks have special properties and one of the most significant of those properties is nodes mobility which in part plays a considerable role in network’s different parameters. Many efforts were made to make an infrastructure for these networks. In these infrastructures or clusters, a main node called clusterhead has a vital role in keeping the structure, routing and enhancing the efficiency of the network. In this article, an innovative clustering algorithm in mobile ad hoc networks is presented which has emphasis on a parameter that represents the convergence of the cluster with predict mobility. In this method, three parameters ,Relative speed, Ndm and battery power are used to calculate the primary weight of nodes, then a convergence coefficient can be achieved using the strenght of received signals of neighbors and predicting the nodes mobility . This value will be multiplied to the primary weight of the nodes afterwards, so that the final weight can be computed. In simulation, the proposed algorithm has been compared with WCA, MOBIC and the Lowest_ID algorithm. The results of simulation reveal that the proposed algorithm achieves the goals.
Adversarial Optimization for Dictionary Attacks on Speaker Verification

In this paper, we assess vulnerability of speaker veriﬁcation systems to dictionary attacks. We seek master voices, i.e., adversarial utterances optimized to match against a large number of users by pure chance. First, we perform menagerie analysis to identify utterances which intrinsically hold this property. Then, we propose an adversarial optimization approach for generating master voices synthetically. Our experiments show that, even in the most secure conﬁguration, on average, a master voice can match approx. 20% of females and 10% of males without any knowledge about the population. We demonstrate that dictionary attacks should be considered as a feasible threat model for sensitive and high-stakes deployments of speaker veriﬁcation.
Gestalt-based Contour Weights Improve Scene Categorization by CNNs

Humans can accurately recognize natural scenes from line drawings, consisting solely of contour-based shape cues. Deep learning strategies for this complex task, however, have thus far been applied directly to photographs, exploiting all the cues available in colour images at the pixel level. Here we report the results of fine tuning off-the-shelf pre-trained Convolutional Neural Networks (CNNs) to perform scene classification given only contour information as input. To do so we exploit the Iverson-Zucker logical/linear framework to obtain line drawings from popular scene categorization databases, including an artist’s scene database and MIT67. We demonstrate a high level of performance despite the absence of colour, texture and shading information. We also show that the inclusion of medial-axis based contour salience weights leads to a further boost, adding useful information that does not appear to be exploited when CNNs are trained to use contours alone.
Code Generation Strategies from AADL Architectural Descriptions Targeting the High Integrity Domain

: Distributed Real-time Embedded (DRE) systems are increasingly used in critical domains such as avionics, vehicle and industrial control as well as in medical systems. They must be designed carefully and have to provide safety properties because a failure could mean loss of life. For these reasons, it is recommended to automatically generate a significant part of the code from the models describing the critical aspects. In our approach, we automatically generate two kinds of code from architectural models. The first one plugs the user functional code in the middleware, the second one provides a significant part of the middleware functions. Both rely on a hand coded written middleware that provides the minimal facilities to plug the generated code and to resolve portability issues. In this paper, we present our code generator and the middleware designed to generate High Integrity (HI) systems. We demonstrate via several use-cases how we succeeded in meeting the requirements of DRE systems (small memory footprint, no dead-code, etc.).
Combination of DexmeDetomiDine plus Remifentanil foR seDation DuRing awake CmaC ViDeolaRyngosCopy-assisteD nasotRaCheal intubation

Aim: evaluation of the effects of dexmedetomidine plus remifentanil on sedation rate, intubation and hemodynamic changes in patients scheduled for awake intubation using C-maC videolaryngoscope. Methods: Sixty patients with anticipated difficult intubation and scheduled for elective maxilla-facial surgery with a plan for awake intubation. patients were randomly divided in two groups. patients in remifentanil group received 10mcg/kg/hr remifentanil i.v. and in remifentanildexmedetomidine group 1mcg/kg/hr dexmedetomidine i.v. plus 8mcg/kg/hr remifentanil i.v. awake nasal intubation was performed using C-maC videolaryngoscope. Ramsay sedation scale, intubation score, adverse events and patient satisfaction score were recorded. oxygen arterial blood saturation, systolic and diastolic arterial pressure, heart rate and bispectral monitor index were assessed at five-time points: 2min before drug administration (T1), 10min after drug infusion (T2), pre-intubation (T3), at intubation (T4) and 2 min after intubation. Results: SPO2 was significantly higher in Group RD compared to Group R at T4 (P<0.05) and T5 (P < 0.01). SAP at T2, T3, T4, T5 (P<0.01), DAP at T5 (P<0.01) and BIS at T2 (P<0.001), T4 (P<0.05) and T5 (P<0.05) were significantly higher in Group R compared to Group RD. No statistically significant differences were observed in Ramsay sedation scale (P = 0.319), vocal cords movement (P = 0.096), cough (P = 0.351), intubation comfort (P = 0,318), adverse events, recall incidences (P = 0,584), patient satisfaction score (P = 0,538) and heart rate (P = 0.082) between groups. Conclusion: Combination of dexmedetomidine with remifentanil should be considered to achieve good intubating conditions, without hemodynamic changes and increased risk of desaturation than remifentanil alone for awake C-maC video-assisted intubation.
INITIATION, PROGRESSION, AND CONDITIONING OF THE SHORT-FRONT VOWEL SHIFT IN AUSTRALIA

This paper investigates the initiation, progression, and conditioning of the short-front vowel shift in Australian English as observed in a sociolinguistic corpus capturing 40 years in real time (from the 1970s to today). Acoustic analyses of over 10,000 tokens reveal that the lowering and retraction of KIT, DRESS and TRAP was preceded by movement in BATH. This suggests that the short-front vowel shift was structurally triggered by BATH moving away from a canonical low position and providing room for TRAP retraction, mirroring the triggering event for similar shifts in other English dialects. We also find that while pre-obstruent TRAP lowers over time, pre-nasal TRAP maintains a high position, resulting in a splitnasal system. Additionally, variance in vowel categories appears to decrease as changes crystalise, suggesting that greater within-category variability is a precursor to vocalic movement. These findings bear on the short-front vowel shift as a worldwide phenomenon in English.
Preparation of Nanoliposomes by Size Exclusion Chromatography to Entrap Soluble Antigens from Leishmania Donovani

In this study, we investigated the ability of nanoliposomes preparation, as a nanoadjuvant, to entrap soluble Leismania donovani antigens (SLAs) and release in vitro. The parasite reactivation was carried out when inoculated into Rosewell park memorial institute media (RPMI) and incubated at 23 °C for 4 days. L. donovani promastigote inoculum (10 cell / ml) of 4 days was used to inoculate modified medium of Saline Neopeptone and Blood agar 9 (SNB 9) to produce promastigote mass. SLAs were extracted from the promastigotes ghost membrane after fourth passages of subculturing in SNB. The membrane pellet obtained was suspended in 5 mM Tris buffer (pH 7.6) and sonicated three times at 4 °C and entrapped in freshly prepared nanoliposomes. Lipids mixture of 4mM Phosphatidylcholine, 2.2 mM Cholesterol and 0.55 mM Phosphatidylethanolamine in a ratio of 7:2:1 were used to prepare nanoliposome. Physio-chemical characterizations of prepared nanoliposomes was performed by using Scanning Electron Microscope (SEM) , Atomic Force Microscope (AFM) and Zeta Potential assays to determine the size, morphology and charge. The efficiency of freshly prepared nanoliposoms to entrap SLAs was determined by measuring the nanoliposome efficiency entrapment (EE). The percentage of EE was 50 and 27.5 of SLAs entrapped nanoliposomes prepared by Sephadex G25 and Sephadex G75, respectively. Moreover, stability of SLAs entrapped nanoliposomes was examined at 4 and 37 °C as a storage temperature.
Structural Change of the Chicago Economy : A Temporal Inverse Analysis

Earlier study (Sonis and Hewings, 1998) proposed an alternative tool that can assist in exploiting trends and uncovering tendencies in individual sectors or groups of sectors within the context of an economy-wide system of accounts. In this paper, the methodology, Temporal Leontief Inverse Analysis, is applied to a set of annual input-output tables for the Chicago metropolitan economy during the period of 1980-97. The results are compared to the earlier analysis (Hewings et al., 1998, Okuyama et al., 2002a, and Okuyama et al. 2002b) to examine the method and to investigate further the structural changes of the Chicago economy.
Spatial and Temporal Variability of Snow Bulk Density and Seasonal Snow Densification Behavior in Finland

The spatial and temporal variability of snow bulk density and seasonal snow densification behavior in Finland was studied using comprehensive set of snow survey data. Mapping of the monthly snow bulk densities showed small differences, mainly indicating slightly denser snow covers in the open areas compared to the dense forest conditions. Open areas / forest openings had statistically different snow densities compared to forested conditions most often during the melt period. On average, Finnish snow has a bulk density of approximately 200 kgmduring the early winter, and bulk density increases to approximately 230 kgm in March and to more than 280 kgm in April. The average densification behavior of the Finnish snow cover locates the country on a transition zone between the maritime and taiga snow zones. Early winter snow bulk densities showed decreasing trends at some locations and/or biotypes during recent decades; during the melt period some increasing trends were seen. Trends seen in long time series of the snow densification behavior were mostly small. Some significant increasing trends in snow densification were observed in the long times series from Jokioinen, Konnevesi and Kittilä. Densification behavior among the biotypes did not differ significantly.
Measuring the interaction dimension of segregation ∗ Preliminary version submitted to the PET Conference 2015 ( Luxembourg )

This paper studies the heterogeneity of social interaction profiles among individuals, defining the extent of the interaction dimension of segregation. An interaction profile assigns the probabilities that one individual has to interact with well defined social groups, and it can be inferred, for instance, from observation of social ties through networks data. Heterogeneity is minimal if everybody is associated with the same profile. It is maximal if everybody interacts with only one group. All the in-between configurations can be ordered on the bases of an intuitive principle: when two individuals compound their interaction profiles, the identity of the individuals becomes less informative about the groups she is more likely to interact with. This paper proposes a characterization of the class of indicators that are used to assess heterogeneity in interaction patters in a society, and studies one of them, the Gini-exposure index. One key advantage of this index is that overall heterogeneity can be decomposed into the segregation experienced by every individual with respect to other people in his own group (isolation) or in other groups (exposure). An preliminary empirical investigation of interaction patterns of natives and immigrants across Italian municipalities reveals connections and differences with other exposure measures.
Speech Emotion Recognition based on Voiced Emotion Unit

Speech emotion recognition (SER) system is becoming a very important tool for human-computer interaction. Previous studies in SER have been focused on utterance as one unit. They assumed that the emotional state is fixed during the utterance, although, the emotional state may change during the time even in one utterance. Therefore, using utterance as one unit is not suitable for this purpose especially for long utterances. The ultimate goal of this study is to find a novel emotion unit that can be used to improve SER accuracy. Therefore, different emotion units defined based on voiced segments are investigated. To find the optimal emotion unit, SER system based on support vector machine (SVM) classifier is used to evaluate each unit. The classification rate is used as a metric for the evaluation. To validate the proposed method, the Berlin database of emotional speech EMO-DB is used. The experimental results revealed that emotion unit that contains four voiced segments gives the highest recognition rate for SER. Moreover, the final emotional state of the whole utterance is determined by majority voting of emotional states of its units. It is found that the performance of the proposed method using voiced related emotion unit outperforms the conventional method using utterance as one
Snippet from the Black Scholes options pricing benchmark implemented using Intel MKL

Data movement between main memory and the CPU is a major bottleneck in parallel data-intensive applications. In response, researchers have proposed using compilers and intermediate representations (IRs) that apply optimizations such as loop fusion under existing high-level APIs such as NumPy and TensorFlow. Even though these techniques generally do not require changes to user applications, they require intrusive changes to the library itself: often, library developers must rewrite each function using a new IR. In this paper, we propose a new technique called split annotations (SAs) that enables key data movement optimizations over unmodified library functions. SAs only require developers to annotate functions and implement an API that specifies how to partition data in the library. The annotation and API describe how to enable cross-function data pipelining and parallelization, while respecting each function’s correctness constraints. We implement a parallel runtime for SAs in a system called Mozart. We show that Mozart can accelerate workloads in libraries such as Intel MKL and Pandas by up to 15×, with no library modifications. Mozart also provides performance gains competitive with solutions that require rewriting libraries, and can sometimes outperform these systems by up to 2× by leveraging existing hand-optimized code. ACM Reference Format: Shoumik Palkar and Matei Zaharia. 2019. Optimizing Data-Intensive Computations in Existing Libraries with Split Annotations. In ACM SIGOPS 27th Symposium on Operating Systems Principles (SOSP ’19), October 27–30, 2019, Huntsville, ON, Canada. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3341301.3359652
Collective Perception of Anthropic and Extractive Interventions in the Colombian Llanos

Increasingly, the developmental model of anthropic and extractive interventions is a global concern. Its impacts are challenging not only the precarious equilibrium of natural resources but also the one of local communities and identities. The case of the Colombian Llanos shows how the local culture of the Cultura Llanera (CL) is deep-rooted with natural resources, their use and their management. Throughout the use of a survey based on the Governance Analytical Framework (GAF), this paper presents and discusses shared problems and social norms. The collective perception of local groups shows that the CL, in particular traditional livestock practices in flooded savannahs, is a key element for the sustainable development of the region. Furthermore, it reveals that agricultural and extractive activities, primarily rice and oil, are considered the main threats to both the ecosystem and the protection of the CL.
An Efficient Protocol Framework Solution for Resource – Constraint Mobile Devices Allocation in Cloud Computing Environments

In cloud computing, data owners host their data on cloud servers, and users (data consumers) can access the data from the cloud servers. An excellent remote data authentication method should be able to collect information for statistical analysis, such as validation results. With the rapid development of network technology and cloud computing, more and more organizations and users outsource their data into the cloud server. In order to protect data privacy, the sensitive data have to be encrypted, which increases the heavy computational overhead and brings great challenges to resourceconstraint devices. In the proposed scheme, several algorithms are designed to maintain and lookup CBF, while a pruning algorithm is used to delete the repeated items for saving the space. It ensures that the same relevance scores are encrypted into different bits, which can resist the statistical analyses on the cipher text of the relevance scores. Moreover, since the cryptosystem supports the addition of cipher text without the knowledge of the private key, the major computing work in ranking could be moved from user side to the cloud server side. Therefore, the proposed scheme has huge potentials in resource-constraint mobile devices such as 5G mobile terminals. Security analyses prove that the proposed scheme can prevent the information leakage. In the platform, an estimation module has been embedded to predict the future loads of the system, and then, two schedulers are considered to schedule the expected and unpredicted loads, respectively. The proposed scheduler applies the column generation technique to handle the integer linear/quadratic programming optimization problem. Also, the cut-and solve-based algorithm and the call back method are proposed to reduce the complexity and computation time. Finally, numerical and experimental results are presented to validate our findings. To ensure cloud reliability and availability, a fault tolerance strategy should be developed and implemented. Most of the early fault tolerant strategies focused on using only one method to tolerate faults. This paper presents an adaptive framework to cope with the problem of fault tolerance in cloud computing environments. The framework employs both replication and check-pointing methods in order to obtain a reliable platform for carrying out customer requests. The results of the experiments show that the proposed framework improves the performance of the cloud in terms of throughput, overheads, monetary cost, and availability.
Divergent allocation strategies between annuals and perennials in the field

Within a species, geographic populations often diverge as they adapt to their local environments. Such divergence can be an early step in speciation, especially when it results in reproductive isolation and a reduction of gene flow. In the wildflower Mimulus guttatus, local adaptation is observed with variation in life history strategies. Annual plants flower early and invest very little in vegetative growth, whereas perennials spend more time growing vegetatively before reproducing. My study investigates the genetic basis of these alternative strategies in the field using an F4 cross between parent populations representative of the two ecotypes. I grew 24 replicates of each inbred parent and 537 F4 offspring. Phenotypic observations taken in the field assessed allocation to vegetative versus reproductive growth, as well as overall fitness. DNA collected in the field, extracted in the lab, and sequenced at the University of Rochester will be combined with phenotypic data for Quantitative Trait Loci (QTL) mapping. This will identify regions of the genome that explain phenotypic variation, and comparisons of the QTL for multiple traits will indicate the role of linkage or pleiotropy in the evolution of M. guttatus. Current results come from correlations in the phenotypic data. They show that while some traits that were correlated in parent populations are broken up by the rounds of recombination in the F4 population (e.g. stolon number and flower number); other traits remain linked (e.g. flowering date and senescence). These results suggest that evolution may act on combinations of traits as populations become adapted to their environments.
Sensorless fuzzy sliding-mode control of the double-star induction motor using a sliding-mode observer

The paper presents a sensorless fuzzy sliding-mode speed control (FSMC) decoupled by a vector control applied to a double-star induction motor (DSIM) using a sliding mode observer (SMO) fed by two PWM voltage source inverters. The fuzzy sliding-mode control is a hybrid control technique that links two control strategies, the sliding mode and fuzzy logic, whose sign function of the discontinuous part is replaced by a fuzzy inference system to overcome the drawbacks of the chattering phenomenon. For the control, the major problem is the need for using a mechanical sensor. Which imposes an additional cost and increases the complexity of the assembly. For this reason, we propose a sliding mode observer of a high robustness to few parametric uncertainties by providing considerable correction gains. The control + observer set is simulated by the Matlab / Simulink software and the simulation results show that its performance is robust and the chattering problem is solved.