Miracle-dz/UniFAR-FTUs huggingface dataset:
Abstract WMP60: Automatic Embolic Signal Detection Using Adaptive Wavelet Packet Transform and Adaptive Neuro-Fuzzy Inference System

Introduction: Transcranial Doppler(TCD) can be used to detect emboli in cerebral circulation. To classify the measured TCD as an embolic signal (ES) or artifact is usually done by a well-trained physician. However, inter-rater reliability among those physicians is variable. Also,in countries where skilled physicians are scarce, an automatic ES detection system can be useful as a medical support system. Method: We propose a two-step algorithm based on adaptive Wavelet Packet Transform (WPT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) described as follows. First, the TCD signal is windowed using a 256-sample Gaussian window with 80% overlap. A 3-level WPT is used to transform each windowed TCD signal. The best basis algorithm is applied to find the best binary tree from which the entropy and normalized energy are calculated. The entropy and normalized energy are used as features to classify each windowed TCD signal as normal or abnormal. Second, the abnormal TCD windows are classified further as ES or artifact. Specifically, standard deviation of level 3 WPT coefficients from frequency index 1 to 6 are calculated and used as inputs of ANFIS where each standard deviation can be combined with others resulting in 64 rules. After training the ANFIS, ES and artifact can be differentiated. Results: Six hundred and sixty abnormal signals in vivo are used to evaluate the algorithm, i.e., 176 ESs collected during emboli monitoring from patients undergoing carotid angioplasty with stenting and from patients with patent foramen ovale, 106 artifacts from normal subjects, and 378 artifacts from stroke patients with carotid stenosis. The signals are divided into training and validation sets. The training set is used to train the algorithm and the validation set is used to evaluate the validity of the algorithm. Experimental results show that the algorithm can differentiate abnormal from normal signals efficiently with 100% accuracy. A sensitivity of 96.6% and specificity of 96.5% can be achieved from the validation set. Conclusions: The automatic embolic signal detection algorithm has been developed. The experimental results suggested that the algorithm could detect emboli and differentiate artifacts efficiently and could be used as a medical support system.
Optimal Column Layout for Hybrid Workloads

Data-intensive analytical applications need to support both efficient reads and writes. However, what is usually a good data layout for an update-heavy workload, is not well-suited for a read-mostly one and vice versa. Modern analytical data systems rely on columnar layouts and employ delta stores to inject new data and updates. We show that for hybrid workloads we can achieve close to one order of magnitude better performance by tailoring the column layout design to the data and query workload. Our approach navigates the possible design space of the physical layout: it organizes each column's data by determining the number of partitions, their corresponding sizes and ranges, and the amount of buffer space and how it is allocated. We frame these design decisions as an optimization problem that, given workload knowledge and performance requirements, provides an optimal physical layout for the workload at hand. To evaluate this work, we build an in-memory storage engine, Casper, and we show that it outperforms state-of-the-art data layouts of analytical systems for hybrid workloads. Casper delivers up to 2.32x higher throughput for update-intensive workloads and up to 2.14x higher throughput for hybrid workloads. We further show how to make data layout decisions robust to workload variation by carefully selecting the input of the optimization.
Dimorphism in human maxillary and madibular canines in establishment of gender

Sexual dimorphism refers to the differences in size, shape, color, etc. between males and females and is a useful tool to distinguish them, especially in forensic investigations and anthropological assessments. The canines are favoured as ideal teeth to study these differences in view of their durability in the oral cavity. The present study was performed on 100 dental casts of a South Indian population in the age group of 14-20 years in an attempt to assess the dimorphism of human permanent maxillary and mandibular canines and to evaluate the possibility of dimorphism of the canines being used as a valid tool in the forensic and legal identification of an individual. The mesiodistal and buccolingual measurements were subjected to statistical analysis using the t test to determine whether significant differences exist between tooth sizes in males and females. The present study revealed that the mean values of the buccolingual and mesiodistal dimensions of the mandibular left canine (33) were greater in females than in males and the mean values of the mesiodistal dimensions of the mandibular right canine (43) in females were greater than that in males in the given sample. The finding could be attributable to evolution resulting in a reduction in sexual dimorphism, causing an overlap of tooth dimensions in modern males and females.
CHAPTER 2 A DISCRETE DOPAMINERGIC PROJECTION FROM THE INCERTOHYPOTHALAMIC A 13 CELL GROUP TO THE DORSOLATERAL PERIAQUEDUCTAL GRAY

Several findings have indicated an involvement of dopamine in panic and defensive behaviors. The dorsolateral column of the periaqueductal gray (dlPAG) is crucially involved in the expression of panic attacks in humans and defensive behaviors, also referred to as panic-like behaviors, in animals. Although the dlPAG is known to receive a specific innervation of dopaminergic fibers and abundantly expresses dopamine receptors, the origin of this dopaminergic input is largely unknown. This study aimed at mapping the dopaminergic projections to the dlPAG in order to provide further insight into the panic-like related behavior circuitry of the dlPAG. For this purpose, the retrograde tracer cholera toxin subunit b (CTb) was injected into the dlPAG of male Wistar rats and double immunofluorescence for CTb and tyrosine hydroxylase (TH), the rate-limiting enzyme in the synthesis of dopamine, was performed. Neurons labeled for both CTb and TH were counted in different dopaminergic cell groups. The findings indicate that the dopaminergic nerve terminals present in the dlPAG originate from multiple dopamine-containing cell groups in the hypothalamus and mesencephalon. Interestingly, the A13 cell group is the main source of dopaminergic afferents to the dlPAG and contains at least 45% of the total number of CTb/TH-positive neurons. Anterograde tracing with biotinylated dextran amine (BDA) combined with double immunofluorescence for BDA and TH confirmed the projections from the A13 cell group to the dlPAG. The remainder of the dopamine-positive terminals present in the dlPAG was found to originate from the extended A10 cell group and the A11 group. The A13 cell group is known to send dopaminergic efferents to several other brain regions implicated in defensive behavior, including the central amygdala and ventromedial hypothalamus. Therefore, although direct behavioral evidence is lacking, our finding that the A13 cell group is also the main source of dopaminergic input to the dlPAG suggests that dopamine might contribute to the regulation of dlPAG-mediated defensive behaviors. DOPAMINERGIC PROJECTIONS TO THE DLPAG
Benthos: Trophic modelling of the Ross Sea

Although the Ross Sea benthos has been extensively tudied from the late 19 th century to presentday (e.g., Borchgrevink 1901; Littlepage & Pearse 1 967; Bullivant & Dearborn 1967; Bullivant 1967a, b; Dayton & Oliver 1977; Lipps et al. 1979; Battershill 1989; review by Starmans et al. 1999; Gambi & Bussotti 1999; Barry et al. 2003; Reh m et al. 2006), large-scale estimates of benthic biomass in the Ross Sea is limited. Samplin g methods and lack of calibration of numbers to biomass are at the base of difficulties in arriv ing at consolidated megaand macrobenthos biomass data.
On Using SpecAugment for End-to-End Speech Translation

This work investigates a simple data augmentation technique, SpecAugment, for end-to-end speech translation. SpecAugment is a low-cost implementation method applied directly to the audio input features and it consists of masking blocks of frequency channels, and/or time steps. We apply SpecAugment on end-to-end speech translation tasks and achieve up to +2.2% BLEU on LibriSpeech Audiobooks En→Fr and +1.2% on IWSLT TED-talks En→De by alleviating overfitting to some extent. We also examine the effectiveness of the method in a variety of data scenarios and show that the method also leads to significant improvements in various data conditions irrespective of the amount of training data.
1 Laws of class L and Self Decomposable Laws

The risk neutral process is modeled by a four parameter self similar process of independent increments with a self decomposable law for its unit time distribution. Six di¤erent processes in this general class are theoretically formulated and empirically investigated. We show that all 6 models are capable of adequately synthesizing European option prices across the spectrum of strikes and maturities at a point of time. Considerations of parameter stability over time suggest a preference for two of these models. Currently, there are several option pricing models with six to ten free parameters that deliver a comparable level of performance in synthesizing option prices. The dimension reduction attained here should prove useful in studying the variation over time of option prices.
Is the Chinese Stock Market Really Efficient

Groenewold et al (2004a) documented that the Chinese stock market is inefficient. In this paper, we revisit the efficiency problem of the Chinese stock market using time-series model based trading rules. Our paper distinguishes itself from previous studies in several aspects. First, while previous studies concentrate on the viability of linear forecasting techniques, we evaluate the profitability of the forecasts of the self-exciting threshold autoregressive model (SETAR), and compare it with the conventional linear AR and MA trading rules. Second, the finding of market inefficiency in earlier studies mainly rest on the statistical significance of the autocorrelation or regression coefficients. In contrast, this paper directly examines the profitability of various trading rules. Third, our sample covers an extensive period of 1991-2010. Sub-sample analysis shows that positive returns mainly concentrate in the pre-SOE reform period, suggesting that China’s stock market has become more efficient after the reform.
Scene Recognition using Bag-of-Words

Scene recognition is an important upcoming real world problem which finds application in the fields of robotics(autonomous systems), surveillance(wearable camera footage, evidence photographs, etc), and personal assistance devices(Google Glass). There has been a steady progress in the field and this report explains the developments made in this field leading to the current state-of-the-art approach, documenting the results obatined from our implementation with standard benchmarks and classifiers. Keywords—scene recognition. sift, object recognition, k-means, bag-of-words, visual words, object filters
O ct 2 01 9 COMBINATORICS OF QUADRATIC SPACES OVER FINITE FIELDS

We study various combinatorial properties of quadratic spaces (Fnq , Q) over finite fields Fq where Q(x) = x1 + · · · + x 2 n and q is an odd prime power. First, we construct a new isometric invariant of combinatorial type on (Fnq , Q) and then investigate possible quadratic subspaces of (Fnq , Q). Second, we investigate the poset structure of nondegenerate subspaces of (F n q , Q) under the inclusion order. The number of these subspaces behaves like the q-binomial coefficient and we study its combinatorial properties.
2 APPROXIMATION ALGORITHMS FOR BIN PACKING : A SURVEY

The classical one-dimensional bin packing problem has long served as a proving ground for new approaches to the analysis of approximation algorithms. In the early 1970's it was one of the rst combinatorial optimization problems for which the idea of worst-case performance guarantees was investigated. It was also in this domain that the idea of proving lower bounds on the performance of online algorithms was rst developed, and it is here that the probabilistic analysis of approximation algorithms has truly owered. The chapter surveys the literature on worstcase and average-case behavior of approximation algorithms for one-dimensional bin packing, using each type of analysis to put the other in perspective.
Characterization of breast tissues by diffusion weighted MR imaging

Diffusion weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information on in vivo diffusion of water molecules in tissues which is referred as apparent diffusion coefficient (ADC). The advantage of DWI is the intrinsic tissue contrast it provides because of its sensitivity to the changes in cellular environment. Malignant breast tissues are characterized by low ADC while benign breast tissues and normal breast tissues of volunteers show high ADC values. Addition of DWI as an adjunct to the contrast-enhanced MRI of breast lesions has been shown to improve the specificity and the diagnostic accuracy. Despite significant technical improvements in DWI, small lesions are sometimes difficult to visualize on ADC maps due to low spatial resolution and it is often difficult to differentiate non-mass like lesions using ADC values. Further, ADC values have the potential to delineate necrotic portions of the tumor from the viable tumor regions. Studies have shown that DWI has the potential to predict early response of tumors to therapeutic intervention in breast tumors. In recent years, numerous applications of DWI in breast tissue evaluation have been documented; however, there is a need for standardized methods for acquisition and processing of diffusion data for effective clinical applications.
BINAURAL PSYCHOACOUSTIC MODEL : IMPLEMENTATION AND EVALUATION

Objective sound quality evaluation methods and audio compression tools require appropriate models of human hearing system, that would predict the audibility or annoyance of a given signal component. In this paper, the implementation and evaluation of a psychoacoustic model is described. The proposed model comprises all the fundamental stages of human auditory pathway: outer/middle ear model, active nonlinear cochlear model, model of the inner hair cell and auditory nerve complex and also the binaural processing stage.
Simulation Modeling of Virtual Machine Based Services in Multi-Cloud Shared Access Centers

This paper addresses the problem of performance improvement of multi-cloud centers for shared access to virtual resources. Statistical study of Azure workload data was performed in Jupyter Notebooks using SciPy, StatsModels and Facebook Prophet libraries. It should be noted relatively high underutilization of the virtual resources of the Azure cloud platform. As a result of experiments the best way to simulate end-user request stream is to use the empirical cumulative distribution functions and probability mass functions calculated from the sample data. To produce high quality forecasts for time series data Prophet framework for Python and SARIMAX prediction model of StatsModels library were used. The model based on Prophet framework showed the best prediction accuracy by the Mean Absolute Error metric. Prophet framework can be used to forecast number of created and deleted virtual machines in ‘Interactive mode’, which in turn could be used in the implementation of the multi-cloud shared access center simulator. Keywords—simulation modeling, Azure, DaaS, statistical analysis
QuAVONet : Answering Questions on the SQuAD Dataset with QANet and Answer Verifier

SQuAD has been a driving force behind the challenge of Machine Comprehension in recent years. In this paper, I set to first recreate a popular model, QANet, from SQuAD 1.1, before combining it with Answer Verifier from U-Net, what I refer to as QuAVONet. The best results achieved, however, was a QANet-lite with 4 attention heads and 96 hidden dimension size for an EM of 58.157 and F1 of 61.39 on the hidden test set. QuAVONet, however, only achieves EM of 57.532 and nearly identical F1 of 61.389 on the test dataset.
Shooting deaths of unarmed racial minorities: Understanding the role of racial stereotypes on decisions to shoot.

The shootings of Amadou Diallo, Michael Brown, and other unarmed African Americans by police officers propelled social psychological research regarding the influence of racial stereotypes on decisions to shoot. Using the full-cycle model as a frame, we contextualize the shootings of unarmed minorities and the empirical research on how racial stereotypes affect perception and behavior. Focusing on the “shooter bias” phenomenon, we detail what social psychological research knows about decisions to shoot in nonpolice samples and what needs further empirical research. We highlight the complexity and dynamic nature of shooting decisions and the need for more translational research with police officers to fully understand the potential effect. Next, we examine empirical interventions that aim to reduce the applicability of activated stereotypes on shooting behavior. Finally, we finish with a discussion of the practical implications that this research has for police, training, and community relations.
Lawrence Berkeley National Laboratory Recent Work Title Spatiotemporal Consistency of Four Gross Primary Production Products and Solar-Induced Chlorophyll Fluorescence in Response to Climate Extremes Across CONUS

Large spatial‐scale effects of climate extremes on gross primary production (GPP), the largest terrestrial carbon flux, are highly uncertain even as these extremes increase in frequency and extent. Here we report the impacts of spring warming and summer drought in 2012 on GPP across the contiguous United States (CONUS) using estimates from four GPP models: Vegetation Photosynthesis Model (VPM), MOD17A2H V006, Carnegie‐Ames‐Stanford Approach, and Simple Biosphere/Carnegie‐Ames‐Stanford Approach. VPM simulations are driven by Moderate Resolution Imaging Spectroradiometer, North American Regional Reanalysis climate data, and C3 and C4 cropland maps from the United States Department of Agriculture Cropland Data Layer data set. Across 25 eddy covariance flux tower sites, GPP estimates from VPM (GPPVPM) showed better accuracy in terms of cross‐site variability and interannual variability (R2 = 0.84 and 0.46, respectively) when compared to MOD17 GPP. We further assessed the spatial and temporal (seasonal) consistency between GPP products and the Global Ozone Monitoring Experiment‐2 solar‐induced chlorophyll fluorescence over CONUS during 2008–2014. The results suggested that GPPVPM agrees best with solar‐induced chlorophyll fluorescence across space and time, capturing seasonal dynamics and interannual variations. Anomaly analyses showed that increased GPP during the spring compensated for the reduced GPP during the summer, resulting in near‐neutral changes in annual GPP for the CONUS. This study demonstrates the importance of assessing the impacts of different types and timing of climate extremes on GPP and the need to improve light use efficiency models by incorporating C3 and C4 plant functional types.
Structural Flexibility of Peripheral Loops and Extended C-Term Domain of Short Length Substrate Binding Protein from Rhodothermus marinus

Substrate binding proteins (SBP) bind to specific ligands in the periplasmic region and bind to membrane proteins to participate in transport or signal transduction. Typical SBPs consist of two α/β domains and recognize the substrate by hinge motion between two domains. Conversely, short length Rhodothermus marinus SBP (named as RmSBP) exists around the methyl-accepting chemotaxis protein. We previously determined the crystal structure of RmSBP consisting of a single α/β domain, but the substrate recognition mechanism is still unclear. To better understand the short length RmSBP, we performed comparative structure analysis, computational substrate docking, and X-ray crystallographic study. RmSBP shares a high level of similarity in α/β domain with other SBP proteins, but it has a distinct topology in the C-term region. The substrate binding model suggested that conformational change in the peripheral region of RmSBP was required to recognize the substrate. We determined the crystal structures of RmSBP at pH 5.5, 6.0, and 7.5. RmSBP showed structural flexibility of the β1-α2 loop, β5-β6 loop, and extended C-term domain based on the electron density map and temperature B-factor analysis. These results provide information that will further the understanding of the function of the short length SBP.
BAND STOP AND BAND PASS FREQUENCY SELECTIVE SURFACE WITH MINIATURIZED ELEMENT IN LOW FREQUENCIES

In this paper, two single-band frequency selective surfaces (FSS) for low frequency stop and pass band with miniaturized element have been proposed. A square loop patch is constructed to realize single stop band responses. Similarly, a square with slot is constructed to realize a single pass band response at lower frequencies. Pass band is realized by slot while stop band is realized by a patch. Both pass band and stop band FSS structure having dimensions 25mm×25mm×1.605mm. FSS structures are designed and simulated using CST MWS V’16 with unit cell boundary along X and Y axis and floquet ports. Simulated results show that proposed FSS structures for both pass band and stop band are independent of angle of incidence and polarisation. Axial ratio bandwidth of the proposed FSS structure (<3dB) for bandwidth 1GHz. Equivalent circuits of the stop band and pass band FSS structures shows series and parallel combination of inductance and capacitance respectively.
Molecular mechanisms of cisplatin and its liposomally encapsulated form , Lipoplatin TM

__________________________________________________________________________________ *Correspondence, proofs and reprint requests to: Teni Boulikas, Ph.D., Regulon AE, Afxentiou 7, Alimos, Athens 17455, Greece Tel: +30-210-9858454; Fax: +30-210-9858453; E-mail: teni@regulon.org
Goodness of fit Model Dependent Approaches of Controlled Release Matrix Tablets of Zidovudine

Objectives: The objective of the study was to develop controlled release matrix tablet of zidovudine and to understand the release kinetics of drug by applying several mathematical model dependant and independent approaches. Various equations and models are developed for evaluating the drug release. Comparison of original and predicted release profile was most common way for selection of optimum formulation. Methods: In this study drug release profiles are characterized by using several parameters like percentage of drug released at 1 h and 12 h (R1h, R12h), dissolution efficiency at 2 h and 12 h (DE2h, DE12h) and pair wise procedures such as similarity factor (f1), difference factor (f2) and rescigno indices (ξ1, ξ2) for getting the optimum formulation. Six batches (C1 to C6) of different concentration of carbopol embedded controlled release matrix tablets of Zidovudine were evaluated. Further the criteria for selection of appropriate model was based on goodness of fit (R2, adj-R2), sum square residual (SSR), F value and Akaike Information Criterion (AIC). Results & Conclusion: Formulation C5 showed highest values of DE2h, DE12h (19.45%, 57.63%) with acceptance criteria of f2 (51.63), f1 (9.91), ξ1 (0.063) and ξ2 (0.066). Further, drug release from optimum batch C5 was explained by the Higuchi model, due to highest value of R2 (0.992), adj-R2 (0.991) with lowest value of SSR (62.22), F (5.65) and AIC (53.56) data. Moreover a simple mathematical equation was applied to determine the deviation of area under curve (AUC) between predicted and observed dissolution data. On an average of 13.4% percent deviation of AUC was observed in optimum batch.