Miracle-dz/UniFAR-FTUs huggingface dataset:
Proximate composition, mineral content and in vitro antioxidant activity of leaf and stem of Costus afer (Ginger lily)

Aim: This study was designed to determine the proximate composition and mineral content of Costus afer leaf and stem, as well as to identify the most active antioxidant fraction. Materials and Methods: The proximate composition and mineral analysis of C. afer leaf and stem were performed using the standard methods described by Pearson and Association of Official Analytical Chemist while the 1,1 diphenyl 2 picryl hydrazyl (DPPH), thiobarbituric acid reactive species (TBARS), lipid peroxidation (LPO), and total antioxidant capacity (TAC) assays were used to determine the in vitro antioxidant activity of aqueous, n-butanol, ethyl acetate and hexane fractions of C. afer leaf and stem. Results: Proximate analysis revealed that the carbohydrate content was highest in the leaf (55.83 ± 3.71%) and stem (50.38 ± 1.27%) while crude fat content was lowest in the leaf (1.83 ± 0.43%) and stem (1.75 ± 0.48%). The minerals detected in appreciable quantity in both the leaf and stem samples were calcium, magnesium, potassium, sodium, chromium, lead, manganese, nickel, and copper. Further study showed that the aqueous leaf fraction exhibited a significantly (P < 0.05) high DPPH scavenging activity (IC50 = 259.07 µg/ml) and TAC (7.95 ± 0.37 mg ascorbic acid equivalent/g) compared with the other test fractions while the aqueous stem fraction had the highest TBARS scavenging activity (IC50 = 0.37 µg/ml) and inhibition of LPO (IC50 = 41.15 µg/ml) compared with the other test fractions. Conclusion: The findings from this study indicate that C. afer could serve as a source of nutrient and minerals for animal nutrition and human metabolism. It also showed that the aqueous fractions of C. afer leaf and stem possess high antioxidant activity than the other fractions. In addition, this study may also explain the folkloric use of crude C. afer leaf or stem extracts in the treatment of oxidative stress associated diseases, including rheumatoid arthritis and hepatic disorder.
Verification of real-time WSA-ENLIL+Cone simulations of CME arrival-time at the CCMC from 2010 to 2016

The Wang-Sheeley-Arge (WSA)−ENLIL+Cone model is used extensively in space weather operations world-wide to model coronal mass ejection (CME) propagation. As such, it is important to assess its performance. We present validation results of the WSA−ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time by the CCMC space weather team. CCMC uses the WSA−ENLIL+Cone model to predict CME arrivals at NASA missions throughout the inner heliosphere. In this work we compare model predicted CME arrival-times to in situ interplanetary coronal mass ejection leading edge measurements at Solar TErrestrial RElations Observatory -Ahead (STEREO-A ), Solar TErrestrial RElations Observatory -Behind (STEREO-B ), and Earth (Wind and ACE ) for simulations completed between March 2010 and December 2016 (over 1,800 CMEs). We report hit, miss, false alarm, and correct rejection statistics for all three locations. For all predicted CME arrivals, the hit rate is 0.5, and the false alarm rate is 0.1. For the 273 events where the CME was predicted to arrive at Earth, STEREO-A , or STEREO-B , and was actually observed (hit event), the mean absolute arrival-time prediction error was 10.4 ± 0.9 h, with a tendency to early prediction error of −4.0 h. We show the dependence of the arrival-time error on CME input parameters. We also explore the impact of the multi-spacecraft observations used to initialize the model CME inputs by comparing model verification results before and after the STEREO-B communication loss (since September 2014) and STEREO-A sidelobe operations (August 2014–December 2015). There is an increase of 1.7 h in the CME arrival time error during single, or limited two-viewpoint periods, compared to the three-spacecraft viewpoint period. This trend would apply to a future space weather mission at L5 or L4 as another coronagraph viewpoint to reduce CME arrival time errors compared to a single L1 viewpoint.
Impact of unbalancedness and heteroscedasticity on classic parametric significance tests of two-way fixed-effects ANOVA tests

Classic parametric statistical tests, like the analysis of variance (ANOVA), are powerful tools used for comparing population means. These tests produce accurate results provided the data satisfies underlying assumptions such as homoscedasticity and balancedness, otherwise biased results are obtained. However, these assumptions are rarely satisfied in real-life. Alternative procedures must be explored. This thesis aims at investigating the impact of heteroscedasticity and unbalancedness on effect sizes in two-way fixed-effects ANOVA models. A real-life dataset, from which three different samples were simulated was used to investigate the changes in effect sizes under the influence of unequal variances and unbalancedness. The parametric bootstrap approach was proposed in case of unequal variances and non-normality. The results obtained indicated that heteroscedasticity significantly inflates effect sizes while unbalancedness has non-significant impact on effect sizes in two-way ANOVA models. However, the impact worsens when the data is both unbalanced and heteroscedastic.
Speeding Up BigClam Implementation on SNAP [ Experiment and Analysis ]

We perform a detailed analysis of the C++ implementation of the Cluster Affiliation Model for Big Networks (BigClam) on the Stanford Network Analysis Project (SNAP). BigClam is a popular graph mining algorithm that is capable of finding overlapping communities in networks containing millions of nodes. Our analysis shows a key stage of the algorithm — determining if a node belongs to a community — dominates the runtime of the implementation, yet the computation is not parallelized. We show that by parallelizing computations across multiple threads using OpenMP we can speed up the algorithm by 5.3 times when solving large networks for communities, while preserving the integrity of the program and the result. PVLDB Reference Format: C.H. Bryan Liu, Benjamin Paul Chamberlain. Speeding Up BigClam Implementation on SNAP [Experiment and Analysis]. PVLDB, 11 (3): xxxx-yyyy, 2017. DOI: https://doi.org/TBD
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification Supplementary Materials

for Person Re-identification Supplementary Materials Dangwei Li, Xiaotang Chen, Zhang Zhang, Kaiqi Huang CRIPAC & NLPR, CASIA University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology {dangwei.li, xtchen, zzhang, kaiqi.huang}@nlpr.ia. ac.cn 1. Market1501 dataset To further understand the results on Market1501 [8], we show mean Average Precision (mAP) and Rank-1 identification rate between camera pairs in Figure 1 and Figure 2. Compared to the BOW methods, the proposed method improves mean mAP and Rank-1 identification rate between camera pairs by 35.09% and 40.01% respectively. In addition, we show some searching results with different query images in Figure 3. The dataset is challenging and the returned images have very similar appearances and some pedestrians have large backgrounds and occlusions. For the query image in first row of Figure 3, even though the query person has large occlusions and some groundtruth images have large backgrounds, our proposed method can still return the right results. This shows the effectiveness of our proposed method. 2. CUHK03 dataset CUHK03 [3] is one of the largest person re-identification datasets. It provides two types of pedestrian bounding boxes, including detected and manually annotated. In this paragraph, we show the overall Cumulated Matching Characteristics (CMC) on both detected and labeled datasets in Figure 4. For the GateSCNN [5] in Figure 4(a), we use the singe-query results to approximate the single-shot results. The DGD [6] is trained using multiple datasets. In this paper, we use the results trained on single CUHK03 dataset for fair comparison. Compared with the state-of-the-art approaches, our proposed method improves the results by a large margin. 3. MARS dataset MARS [7] is currently largest sequence-based person re-identification dataset. In this paragraph, experimental results of CMC curves under single query and multiple query are shown in Figure 5. We show the results of our proposed method using
Compact CPW-fed UWB MIMO antenna with a novel modified Minkowski fractal defected ground structure (DGS) for high isolation and triple band-notch characteristic

Abstract A compact printed ultra-wideband (UWB) multiple input multiple output (MIMO) antenna with coplanar waveguide feed for portable UWB applications is presented. The radiator and ground plane of the antenna are etched with an overall size of 26.75 mm × 41.5 mm. The UWB MIMO antenna consists of two planar identical monopole antenna elements and a novel modified Minkowski fractal defect is introduced in the ground plane for enhancing the isolation of the MIMO system. The proposed antenna has an impedance bandwidth (for │S11│ < −10 dB) ranging from 3.1 to 11.5 GHz (VSWR ≤ 2) and an isolation of more than 15 dB is achieved between the adjacent planar monopoles. The band rejection phenomenon in wireless local area network band is achieved by etching rotated C-shaped slots from the respective patches. Also, two identical rectangular slots are etched on the respective ground planes which enhances the lower UWB bandwidth and cause interference suppression at Wi-Max and C-band (3.3–3.7 and 3.7–4.2 GHz), respectively. A further improvement in isolation from 14.71 to 21.81 dB is achieved by the addition of parallel rectangular strips at the back side of the antenna. Acceptable agreement is obtained between the simulated and measured antenna performance parameters.
ABC-NS: a new computational inference method applied to parameter estimation and model selection in structural dynamics

The inference of dynamical systems is a challenging issue, particularly when the dynamics include complex phenomena such as the existence of bifurcations and/or chaos. In this situation, the likelihood function formulated based on time-series data may be complex with several local minima and as a result not suitable for parameter inference. In the most challenging scenarios, the likelihood function may not be available in an analytical form, so a standard statistical inference is impossible to carry out. To overcome this problem, the inclusion of new features/invariants less sensitive to small variations from either the time or frequency domains seems to be potentially a very useful way to make Bayesian inference. The use of approximate Bayesian computation (ABC) or likelihood-free algorithms is an appropriate option as they offer the flexibility to use different metrics for parameter inference. However, most variants of the ABC algorithm are inefficient due to the low acceptance rate. In this contribution, a new ABC algorithm based on an ellipsoidal nested sampling technique is proposed to overcome this issue. It will be shown that the new algorithm performs perfectly well and maintains a relatively high acceptance rate through the iterative inference process. In addition to parameter estimation, the new algorithm allows one to deal with the model selection issue. To demonstrate its efficiency and robustness, a numerical example is presented.
Central venous catheter-related infections in hematology and oncology: 2012 updated guidelines on diagnosis, management and prevention by the Infectious Diseases Working Party of the German Society of Hematology and Medical Oncology.

BACKGROUND Cancer patients are at increased risk for central venous catheter-related infections (CRIs). Thus, a comprehensive, practical and evidence-based guideline on CRI in patients with malignancies is warranted. PATIENTS AND METHODS A panel of experts by the Infectious Diseases Working Party (AGIHO) of the German Society of Hematology and Medical Oncology (DGHO) has developed a guideline on CRI in cancer patients. Literature searches of the PubMed, Medline and Cochrane databases were carried out and consensus discussions were held. RESULTS Recommendations on diagnosis, management and prevention of CRI in cancer patients are made, and the strength of the recommendation and the level of evidence are presented. CONCLUSION This guideline is an evidence-based approach to the diagnosis, management and prevention of CRI in cancer patients.
Multi-Label Classification of Patient Notes: Case Study on ICD Code Assignment

The automatic coding of clinical documentation according to diagnosis codes is a useful task in the Electronic Health Record, but a challenging one due to the large number of codes and the length of patient notes. We investigate four models for assigning multiple ICD codes to discharge summaries, and experiment with data from the MIMIC II and III clinical datasets. We present Hierarchical Attention-bidirectional Gated Recurrent Unit (HA-GRU), a hierarchical approach to tag a document by identifying the sentences relevant for each label. HA-GRU achieves state-of-the art re-sults. Furthermore, the learned sentence-level attention layer highlights the model decision process, allows for easier error analysis, and suggests future directions for improvement.
General fractional-order anomalous diffusion with non-singular power-law kernel

In this paper, we investigate general fractional derivatives with a non-singular power-law kernel. The anomalous diffusion models with non-singular power-law kernel are discussed in detail. The results are efficient for modelling the anomalous behaviors within the frameworks of the Riemann-Liouville and Liouville-Caputo general fractional derivatives.
Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation Supplementary Material

We leverage EPN as our SE(3)-equivariant backbone. The core element of EPN is the SPConv block, which consists of one SE(3) point convolution layer to learn features from spatial domain S, and one SE(3) group convolution operator to learn features within rotational group G. Batch normalization layers and leaky ReLU activations are inserted in between and after. We implement a 5-layer hierarchical convolutional network, with each layer containing two SPConv blocks. The first SPConv block is strided by a factor of 2 to down-sample input points gradually. We sample 1024 points from each input data, the points will be downsampled to 64 points after the last SPConv layers with each point containing features that are equivariant to the rotation group G. We set the output feature channel as 128. For the SE(3)-invariant canonical shape reconstruction branch, we implement the MLPZ as a simple 2-layer MLP to map the pooled SE(3)-invariant feature into 1024 ∗ 3, which corresponds to 1024 points in canonical space. The 2-layer MLP is followed by Sigmoid activation function to constrain the values. For the SE(3)-equivariant 6D pose estimation branch, MLPπ is realized by another 2-layer MLP with batch normalization and a leaky ReLU activation. MLPP is just a one-layer MLP. In practice, the residual translation prediction head is separated from the residual rotation one, where a 2-layer MLP is used instead.
Clinical Features and Outcome of Patients With IRAK-4 and MyD88 Deficiency

Autosomal recessive interleukin-1 receptor-associated kinase (IRAK)-4 and myeloid differentiation factor (MyD)88 deficiencies impair Toll-like receptor (TLR)- and interleukin-1 receptor-mediated immunity. We documented the clinical features and outcome of 48 patients with IRAK-4 deficiency and 12 patients with MyD88 deficiency, from 37 kindreds in 15 countries. The clinical features of IRAK-4 and MyD88 deficiency were indistinguishable. There were no severe viral, parasitic, and fungal diseases, and the range of bacterial infections was narrow. Noninvasive bacterial infections occurred in 52 patients, with a high incidence of infections of the upper respiratory tract and the skin, mostly caused by Pseudomonas aeruginosa and Staphylococcus aureus, respectively. The leading threat was invasive pneumococcal disease, documented in 41 patients (68%) and causing 72 documented invasive infections (52.2%). P. aeruginosa and Staph. aureus documented invasive infections also occurred (16.7% and 16%, respectively, in 13 and 13 patients, respectively). Systemic signs of inflammation were usually weak or delayed. The first invasive infection occurred before the age of 2 years in 53 (88.3%) and in the neonatal period in 19 (32.7%) patients. Multiple or recurrent invasive infections were observed in most survivors (n = 36/50, 72%). Clinical outcome was poor, with 24 deaths, in 10 cases during the first invasive episode and in 16 cases of invasive pneumococcal disease. However, no death and invasive infectious disease were reported in patients after the age of 8 years and 14 years, respectively. Antibiotic prophylaxis (n = 34), antipneumococcal vaccination (n = 31), and/or IgG infusion (n = 19), when instituted, had a beneficial impact on patients until the teenage years, with no seemingly detectable impact thereafter. IRAK-4 and MyD88 deficiencies predispose patients to recurrent life-threatening bacterial diseases, such as invasive pneumococcal disease in particular, in infancy and early childhood, with weak signs of inflammation. Patients and families should be informed of the risk of developing life-threatening infections; empiric antibacterial treatment and immediate medical consultation are strongly recommended in cases of suspected infection or moderate fever. Prophylactic measures in childhood are beneficial, until spontaneous improvement occurs in adolescence. Abbreviations: CRP = C-reactive protein, ELISA = enzyme-linked immunosorbent assay, IFN = interferon, IKBA = I&kgr;B&agr;, IL = interleukin, IL-1R = interleukin-1 receptor, InvBD = invasive bacterial disease, IRAK = interleukin-1 receptor-associated kinase, MyD = myeloid differentiation factor, NEMO = nuclear factor-kappaB essential modulator, NInvBD = noninvasive bacterial disease, TIR = Toll/IL-1R, TLR = Toll-like receptor, TNF = tumor necrosis factor.
Pool-Party: Exploiting Browser Resource Pools as Side-Channels for Web Tracking

—We identify a new class of side-channels in browsers that are not mitigated by current defenses. This class of side-channels, which we call “pool-party” attacks, allow sites to create covert channels by manipulating limited-but-unpartitioned resource pools in browsers. We identify pool-party attacks in all popular browsers, and show they are practical cross-site tracking techniques. In this paper we make the following contributions: ﬁrst, we describe pool-party side-channel attacks that exploit limits in application-layer resource pools in browsers. Second, we demonstrate that pool-party attacks are practical, and can be used to track users in all popular browsers; we also share open source implementations of the attack and evaluate them through a representative web crawl. Third, we show that in Gecko based-browsers (including the Tor Browser Bundle) pool-party attacks can also be used for cross-proﬁle tracking (e.g., linking user behavior across normal and private browsing sessions). Last, we discuss possible mitigations and defenses.
2 New fuzzy wavelet network for modeling and control: Part I (Modeling)

In the part I of the paper, a fuzzy wavelet network is proposed to approximate arbitrary 20 nonlinear functions based on the theory of multiresolution analysis (MRA) of wavelet 21 transform and fuzzy concepts. The presented network combines TSK fuzzy models with 22 wavelet transform and ROLS learning algorithm while still preserve the property of linear23 ity in parameters. In order to reduce the number of fuzzy rules, fuzzy clustering is invoked. 24 In the clustering algorithm, those wavelets that are closer to each other in the sense of the 25 Euclidean norm are placed in a group and are used in the consequent part of a fuzzy rule. 26 Antecedent parts of the rules are Gaussian membership functions. Determination of the 27 deviation parameter is performed with the help of gold partition method. Here, mean of 28 each function is derived by averaging center of all wavelets that are related to that partic
Numerical Methods for Option Pricing under the Two-Factor Models

NUMERICAL METHODS FOR OPTION PRICING UNDER THE TWO-FACTOR MODELS by Jiacheng Cai Dr. Hongtao Yang, Examination Committee Chair Associate Professor of Mathematics University of Nevada, Las Vegas, USA Pricing options under multi-factor models are challenging and important problems for financial applications. In particular, the closed form solutions are not available for the American options and some European options, and the correlations between factors increase the complexity and difficulty for the formulations and implements of the numerical methods. In this dissertation, we first introduce a general transformation to decouple correlated stochastic processes governed by a system of stochastic differential equations. Then we apply the transformation to the popular two-factor models: the two-asset model, the stochastic volatility model, and the stochastic interest rate models. Based on our new formulations, we develop a mixed Monte Carlo method, a lattice method, and a finite volume-alternating direction implicit method for pricing the European and American options under these models. The proposed methods can be easily implemented and need less memory. Numerical results are also presented to validate our C++ programs and to examine our methods. It shows that our methods are very accurate and efficient.
Measurement of the absolute branching ratio of the $K^+ \rightarrow \pi^+\pi^-\pi^+(\gamma)$ decay with the KLOE detector

The absolute branching ratio of the $K^+ \rightarrow \pi^+\pi^-\pi^+(\gamma)$ decay, inclusive of final-state radiation, has been measured using $\sim$17 million tagged $K^+$ mesons collected with the KLOE detector at DA$\Phi$NE, the Frascati $\phi$-factory. The result is: \[ BR(K^+ \rightarrow \pi^+\pi^-\pi^+(\gamma)) = 0.05565 \pm 0. 00031_{stat} \pm 0. 00025_{syst} \] a factor $\simeq$ 5 more precise with respect to the previous result. This work completes the program of precision measurements of the dominant kaon branching ratios at KLOE.
The Galerkin analysis for the random periodic solution of semilinear stochastic evolution equations

The random periodic solution is a new concept to characterize the presence of random periodicity in the long run of some stochastic systems. On its first appearance in [22], the authors gave the definition of the random periodic solutions of random dynamical systems and showed the existence of such periodic solutions for a C perfect cocycle on a cylinder. This is followed by another seminal paper [9], where the authors not only defined the random periodic solutions for semiflows but also provided a general framework for its existence. Namely, instead of following the traditional geometric method of establishing the Poincaré mapping, a new analytical method for coupled infinite horizon forward-backward integral equations was introduced. This pioneering study boosts a series of work, including the existence of random periodic solutions to stochastic partial differential equations (SPDEs) [4], the existence of anticipating random periodic solutions [6, 7], periodic measures [8], etc.
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward

We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find the problem of spurious rewards and a phenomenon we call the "lazy agent'' problem, which arises due to partial observability. We address these problems by training individual agents with a novel value-decomposition network architecture, which learns to decompose the team value function into agent-wise value functions.