Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF PACLOBUTRAZOL ON mRNA ACCUMULATION OF ent-kaurene oxidase AND GA20-oxidase GENES AND PLANT HEIGHT OF Jatropha curcas L.

Jatropha curcas L. (Jatropha) is a perennial plant that has received attention as a source of biodiesel production. However, the Jatropha plants exhibit unfavorable heights. Reducing the plant heights will improve seeds yield and easiness for harvesting. This study aimed to investigate the effect of paclobutrazol (PCB) on Jatropha seedlings (21 days after germination). The plant growth and mRNA accumulation of ent-kaurene oxidase (JcKO) and GA20-oxidase (JcGA20ox) genes were investigated. Height of the PCB treated plants was significantly shorter until the end of the experiment during 17-60 DAT (day after treated). The stem diameters of the treated plants were obviously and significantly thicker than those of the control during 7-19, 2553 DAT. RT-PCR analysis was performed to determined mRNA accumulations of JcKO and JcGA20ox of the plant collected from 10, 17, and 22 DAT. The results showed that the mRNA accumulations of JcKO from the treated plants were not significantly different from those of the control at 17 and 22 DAT. The mRNA accumulations of JcGA20ox of the treated plant were not significantly different from those of the control at 10, 17 and 22 DAT.
Generalization Guarantee of SGD for Pairwise Learning

Recently, there is a growing interest in studying pairwise learning since it includes many important machine learning tasks as speciﬁc examples, e.g., metric learning, AUC maximization and ranking. While stochastic gradient descent (SGD) is an efﬁcient method, there is a lacking study on its generalization behavior for pairwise learning. In this paper, we present a systematic study on the generalization analysis of SGD for pairwise learning to understand the balance between generalization and optimization. We develop a novel high-probability generalization bound for uniformly-stable algorithms to incorporate the variance information for better generalization, based on which we establish the ﬁrst nonsmooth learning algorithm to achieve almost optimal high-probability and dimension-independent excess risk bounds with O ( n ) gradient computations. We consider both convex and nonconvex pairwise learning problems. Our stability analysis for convex problems shows how the interpolation can help generalization. We establish a uniform convergence of gradients, and apply it to derive the ﬁrst excess risk bounds on population gradients for nonconvex pairwise learning. Finally, we extend our stability analysis to pairwise learning with gradient-dominated problems.
Efficient Sequential Switching Hybrid Modulation Techniques for Cascaded Multilevel Inverters

This paper presents four different sequential switching hybrid modulation strategies and compared for cascaded multilevel inverters. Hybrid modulation strategies represent combinations of fundamental frequency modulation and multilevel sinusoidal modulation strategies and designed for performance of the well-known alternative phase opposition disposition, phase shifted carrier, carrier based space vector modulation and single carrier sinusoidal modulations. The main characteristic of these modulations are the reduction of switching losses with good harmonic performance, balanced power loss dissipation among the devices with in a cell and among the series connected cells. Multilevel sinusoidal modulation and its base modulator design are implemented on a Microcontroller. The proposed modulations can be easily extended to three phase and higher level inverters, operates with same physical structure of the power module. The feasibility of these hybrid modulations are verified through spectral analysis, power loss analysis, simulation and experimental results.
U.S. Federal Agency Interests and Key Considerations for New Approach Methodologies for Nanomaterials

Engineered nanomaterials (ENMs) come in a wide array of shapes, sizes, surface coatings, and compositions, and often possess novel or enhanced properties compared to larger sized particles of the same elemental composition. To ensure the safe commercialization of products containing ENMs, it is important to thoroughly understand their potential risks. Given that ENMs can be created in an almost infinite number of variations, it is not feasible to conduct in vivo testing on each type of ENM. Instead, new approach methodologies (NAMs) such as in vitro or in chemico test methods may be needed, given their capacity for higher throughput testing, lower cost, and ability to provide information on toxicological mechanisms. However, the different behaviors of ENMs compared to dissolved chemicals may challenge safety testing of ENMs using NAMs. In this study, member agencies within the Interagency Coordinating Committee on the Validation of Alternative Methods were queried about what types of ENMs are of agency interest and whether there is agency-specific guidance for ENM toxicity testing. To support the ability of NAMs to provide robust results in ENM testing, two key issues in the usage of NAMs, namely dosimetry and interference/bias controls, are thoroughly discussed.
Lin2-Xor Lemma and Log-size Linkable Threshold Ring Signature

In this paper we introduce a novel method of constructing a linkable threshold ring signature without a trusted setup in a group where the decisional Diffie-Hellman problem is hard and no bilinear pairings exist. Our ring signature is logarithmic in anonymity set size and linear in signer threshold, its verification complexity is quasilinear. A range of the recently proposed setup-free logarithmic size signatures is based on the commitment-tozero proving system by Groth and Kohlweiss or on the Bulletproofs inner-product compression method by Bünz et al. In contrast, we construct our signature from scratch using the Lin2-Xor and lemma-Lin2-Selector lemmas that we formulate and prove herein. The Lin2-Xor lemma itself provides a novel 2-round public coin OR-proof protocol, whereas the Lin2-Selector lemma generalizes it to an n-round public coin proof of membership. Consequently, we construct an n-round special honest verifier zero-knowledge proof of membership and instantiate it in the form of a general-purpose setup-free linkable threshold ring signature in the random oracle model. Also, we show the signature is anonymous, has witness-extended emulation, is unforgeable and non-frameable.
Comparison of two types of synchronization of unidirectionally coupled external-cavity semiconductor lasers

We study numerically the synchronization regimes of optical-feedback-induced chaos in unidirectionally coupled semiconductor lasers. Depending on the operating conditions, the receiver laser intensity, IR(t), synchronizes with the intensity of the optically injected field, IT(t-tc), where tc is the flight time from the transmitter laser to the receiver laser, or synchronizes with IT(t-tc+t), where t is the is the external-cavity round-trip time. In the latter case, the intensity produced by the receiver anticipates the injected intensity IT(t-tc) by an anticipation time equal to t. We find that synchronization with a lag time tc-t is much more sensitive to frequency detuning and noise than synchronization with a lag time tc. Moreover, we also find that though synchronization with a lag time tc can be obtained using either an external-cavity or an open-loop receiver, the synchronization quality is always better when an external-cavity receiver is used.
Regional Timber Bridge Girder Reliability: Structural Health Monitoring and Reliability Strategies

Many short span timber beam bridges in regional New South Wales are of unknown reliability, have high traffic loadings and were designed according to codes, many of which have since been superseded. Because asset managers are delaying their maintenance for fiscal reasons, a high proportion of these bridges are structurally degraded and potentially unsafe when excessively loaded. In regional areas, a prioritised maintenance program can be a cost effective alternative to bridge replacement. Such older bridges will require continuous monitoring by low cost methods to assess the temporal probability of their failure. This paper examines the potential for measuring the mid-span deflections of girders caused by high traffic loads to obtain continually updated indicators of the structural health of girders. The mid-span deflection data of a case study bridge were continuously measured using a laser based measuring system, recently developed by the first author. An analysis of the deflection data is used to obtain a reliability index and the probability of bridge failure. Reliability indicators such as these can be used, in conjunction with continuous deflection monitoring, to prioritise cost effective maintenance of older timber bridges in regional New South Wales.
Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs

Piano transcription systems are typically optimized to estimate pitch activity at each frame of audio. They are often followed by carefully designed heuristics and post-processing algorithms to estimate note events from the frame-level predictions. Recent methods have also framed piano transcription as a multi-task learning problem, where the activation of different stages of a note event are estimated independently. These practices are not well aligned with the desired outcome of the task, which is the speciﬁcation of note intervals as holistic events, rather than the aggregation of disjoint observations. In this work, we propose a novel formulation of piano transcription, which is optimized to directly predict note events. Our method is based on Semi-Markov Conditional Random Fields (semi-CRF), which produce scores for intervals rather than individual frames. When formulating piano transcription in this way, we eliminate the need to rely on disjoint frame-level estimates for different stages of a note event. We conduct experiments on the MAESTRO dataset and demonstrate that the proposed model surpasses the current state-of-the-art for piano transcription. Our results suggest that the semi-CRF output layer, while still quadratic in complexity, is a simple, fast and well-performing solution for event-based prediction, and may lead to similar success in other areas which currently rely on frame-level estimates.
Mycena jingyinga, Mycena luguensis, and Mycena venus: three new species of bioluminescent fungi from Taiwan

Three new species of bioluminescent fungi Mycena jingyinga, M. luguensis, and M. venus are proposed based on phylogenetic and morphological analyses. Of the three new species, the luminescence only admits from the mycelia instead of the fruiting bodies. The DNA sequences of RNA polymerase II second largest subunit (rpb2) and nuc rDNA ITS1‐5.8S‐ITS2 (ITS) were used for phylogenetic analyses. The three new species are characterized by small size, white or brown, adnate to subdecurrent lamellae, pubescent stipe with fibrils at bases, four-spored basidia, amyloid spores, cheilocystidia with various shapes, and pileipellis hyphae with short, simple, or diverticulate excrescences. These characteristics are congruent with those of Mycena section Fragilipedes. The three new species bring the total number of bioluminescent fungi to 108 and 15 species in the world and Taiwan, respectively.
On Missingness Features in Machine Learning Models for Critical Care: Observational Study (Preprint)

BACKGROUND Missing data in electronic health records is inevitable and considered to be nonrandom. Several studies have found that features indicating missing patterns (missingness) encode useful information about a patient’s health and advocate for their inclusion in clinical prediction models. But their effectiveness has not been comprehensively evaluated. OBJECTIVE The goal of the research is to study the effect of including informative missingness features in machine learning models for various clinically relevant outcomes and explore robustness of these features across patient subgroups and task settings. METHODS A total of 48,336 electronic health records from the 2012 and 2019 PhysioNet Challenges were used, and mortality, length of stay, and sepsis outcomes were chosen. The latter dataset was multicenter, allowing external validation. Gated recurrent units were used to learn sequential patterns in the data and classify or predict labels of interest. Models were evaluated on various criteria and across population subgroups evaluating discriminative ability and calibration. RESULTS Generally improved model performance in retrospective tasks was observed on including missingness features. Extent of improvement depended on the outcome of interest (area under the curve of the receiver operating characteristic [AUROC] improved from 1.2% to 7.7%) and even patient subgroup. However, missingness features did not display utility in a simulated prospective setting, being outperformed (0.9% difference in AUROC) by the model relying only on pathological features. This was despite leading to earlier detection of disease (true positives), since including these features led to a concomitant rise in false positive detections. CONCLUSIONS This study comprehensively evaluated effectiveness of missingness features on machine learning models. A detailed understanding of how these features affect model performance may lead to their informed use in clinical settings especially for administrative tasks like length of stay prediction where they present the greatest benefit. While missingness features, representative of health care processes, vary greatly due to intra- and interhospital factors, they may still be used in prediction models for clinically relevant outcomes. However, their use in prospective models producing frequent predictions needs to be explored further.
Deposit Money Banks ’ Lending Rate and Small Scale Firms Performance in Nigeria

The research work investigated the impact of Deposit Money Banks’ Lending Rate on Small and Medium Firms Output in Nigeria spanning for the period 2002-2014. The objectives of the study were to ascertain the impact of bank lending rate, inflation rate and exchange rate on small and medium scale firms output in Nigeria. Ex-post Facto research design was adopted while Ordinary Least Square estimation techniques was used. It was discovered that bank lending rate has negative significant impact on Small and Medium Scale firms Output; Exchange rate has significant positive impact on Small and Medium Scale Industry Output while inflation rate has positive and insignificant impact on Small and Medium scale industry output in Nigeria. The study recommended that appropriate policies be put in place to drive down bank lending rate to stimulate economic growth and enhance small and medium scale industry output in Nigeria and to further support the SMIs output, there is need for government to pursue policies to drive down the lending rate to the barest minimum so as to stimulate economic growth in Nigeria.
Low-cost visible light spectral imaging

Spectral imaging is an emerging technology for measuring spectral power distributions (SPDs) of electromagnetic radiation over a two-dimensional spatial domain. Within the visible light wavelength domain, spectral imaging measures light and colour with greater accuracy than digital photography. However, high cost limits its accessibility. Accordingly, a low-cost method was developed using commercially-available hardware – primarily a DSLR camera and a set of narrow bandpass filters. The quantity of filters was minimised to a total of seven, set by the dimensionality of SPDs, the spectral sensitivities of eyes and cameras, and commercial availability. Camera spectral sensitivity was measured using this same filter set, a colour chart, a spectrophotometer, and noon daylight modelled as CIE D65. The RAW photo format was used to access unprocessed sensor data. Independent SPD measurements from each colour channel were fused as a sensitivity-weighted average for efficient and continuous interpolation between colour channels with a high signal-to-noise ratio. Images were reconstructed from SPDs with standard observer functions. The method was demonstrated with a Canon 650D DSLR camera, a set of Thorlabs one-inch narrow bandpass filters, an X-Rite ColorChecker chart, and a Spectro 1 spectrophotometer. Accuracy was validated by quantitative comparison against ground truth SPD measurements and qualitative assessment of reconstructed images. The total filter cost was $715, plus $405 to measure camera sensitivity .
Towards Sharper Generalization Bounds for Structured Prediction

In this paper, we investigate the generalization performance of structured prediction learning and obtain state-of-the-art generalization bounds. Our analysis is based on factor graph decomposition of structured prediction algorithms, and we present novel margin guarantees from three different perspectives: Lipschitz continuity, smoothness, and space capacity condition. In the Lipschitz continuity scenario, we improve the square-root dependency on the label set cardinality of existing bounds to a logarithmic dependence. In the smoothness scenario, we provide generalization bounds that are not only a logarithmic dependency on the label set cardinality but a faster convergence rate of order O ( 1 n ) on the sample size n . In the space capacity scenario, we obtain bounds that do not depend on the label set cardinality and have faster convergence rates than O ( 1 √ n ) . In each scenario, applications are provided to suggest that these conditions are easy to be satisﬁed.
A Horizontally Polarized Omnidirectional Antenna Using Stacked Curve Dipoles for DTV Reception

This paper presents an omnidirectional antenna using stacked curve dipoles radiating horizontal polarization for digital television (DTV) reception. The proposed antenna consists of stacked curve dipoles designed on brass sheet. The parametric study is carried out for various dimensions of curve dipoles to improve the impedance bandwidth. The results show good impedance matching for of less than −10 dB along the frequency range from 404 MHz to 871 MHz. The antenna gain is between −10 dBi and 0.67 dBi along the frequency range from 470 to 862 MHz.
Action-guided 3D Human Motion Prediction

The ability of forecasting future human motion is important for human-machine interaction systems to understand human behaviors and make interaction. In this work, we focus on developing models to predict future human motion from past observed video frames. Motivated by the observation that human motion is closely related to the action being performed, we propose to explore action context to guide motion prediction. Specifically, we construct an action-specific memory bank to store representative motion dynamics for each action category, and design a query-read process to retrieve some motion dynamics from the memory bank. The retrieved dynamics are consistent with the action depicted in the observed video frames and serve as a strong prior knowledge to guide motion prediction. We further formulate an action constraint loss to ensure the global semantic consistency of the predicted motion. Extensive experiments demonstrate the effectiveness of the proposed approach, and we achieve state-of-the-art performance on 3D human motion prediction.
An Empirical Analysis of the Impact of Financial Inclusion and Financial Development on Economic Growth of Nigeria from 1981-2017 Odeigah Ernest

This paper examines the impact of financial inclusion and financial Development on Economic growth of Nigeria, for the period 1981-2017. It uses the Ordinary least square technique and also incorporated the Johansen Cointegration test approach. The results show evidence of long and short run relationships between the variables: Real Gross Domestic Product Growth Rate (GRGD), broad Money ratio to GDP (MGDP), Credit to Private Sector ratio to GDP (CPSGDP), Deposits of Rural branches of Commercial banks (DRC) and Loans of Rural branches of Commercial banks (LRC). These findings suggest that achieving greater Financial Inclusion and financial development is a responsibility of all relevant stakeholders and involves collective effort. However, the onus is on the government to make the first step towards financial inclusion and development by establishing the necessary institutional framework, an effective regulatory framework and an environment necessary for successful implementation of any its strategy.
Deformable Butterfly: A Highly Structured and Sparse Linear Transform

We introduce a new kind of linear transform named Deformable Butterfly (DeBut) that generalizes the conventional butterfly matrices and can be adapted to various input-output dimensions. It inherits the fine-to-coarse-grained learnable hierarchy of traditional butterflies and when deployed to neural networks, the prominent structures and sparsity in a DeBut layer constitutes a new way for network compression. We apply DeBut as a drop-in replacement of standard fully connected and convolutional layers, and demonstrate its superiority in homogenizing a neural network and rendering it favorable properties such as light weight and low inference complexity, without compromising accuracy. The natural complexity-accuracy tradeoff arising from the myriad deformations of a DeBut layer also opens up new rooms for analytical and practical research. The codes and Appendix are publicly available at: https://github.com/ruilin0212/DeBut.
Defining Pavement Condition States to Quantify Road Quality for Designing of Pavement Maintenance Management System

Important factor in the Pavement Maintenance Management System (PMMS) is to quantify the quality of pavement. Earlier research is based on various parameters such as pavement condition index (PCI), mechanistic properties, and physical distress. Ultimate objective of Maintenance Management System (MMS) is to optimize the resources required to upgrading that utility. In none of the previous research; the relationship between pavement condition and corresponding maintenance cost has been considered in designing of MMS. In this study, 70 pavements are studied and an attempt is made to categories them in various condition states based on their repair cost. The research is useful in designing of cost effective MMS.
Automated 3D Wireframe Modeling of Indoor Structures from Point Clouds Using Constrained Least-Squares Adjustment for As-Built BIM

AbstractAutomated three-dimensional (3D) modeling of building interiors for an as-built building information model (BIM) incurs critical difficulties because of the complex design of indoor structures and a variety of clutter from scanned point clouds. This paper proposes a scheme for automated 3D geometric modeling of indoor structures, including detailed components such as windows and open doors. Moreover, to produce a regularized model, we imposed constrained least-squares adjustment according to an assumption made with respect to typical indoor structures: walls, ceiling, floor, doors, and windows are composed of straight lines, either parallel or orthogonal. Modeling proceeds in two main phases: wall modeling and window modeling. In the wall modeling phase, the point-cloud acquisitions are projected onto a 2D binary image and the wall boundary is traced out. The boundary is regularized by means of the constrained least-squares method, and, thereby, a 2D floor boundary map can be obtained that later p.