Miracle-dz/UniFAR-FTUs huggingface dataset:
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates

Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The Bayesian framework provides a principled way of uncertainty estimation but is often not scalable to modern deep neural nets (DNNs) that have a large number of parameters. Non-Bayesian methods are simple to implement but often conflate different sources of uncertainties and require huge computing resources. We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective. The core of our method is to view DNN transformations as state evolution of a stochastic dynamical system and introduce a Brownian motion term for capturing epistemic uncertainty. Based on this perspective, we propose a neural stochastic differential equation model (SDE-Net) which consists of (1) a drift net that controls the system to fit the predictive function; and (2) a diffusion net that captures epistemic uncertainty. We theoretically analyze the existence and uniqueness of the solution to SDE-Net. Our experiments demonstrate that the SDE-Net model can outperform existing uncertainty estimation methods across a series of tasks where uncertainty plays a fundamental role.
Analgesic potential of dichloromethane leaf extracts of Eucalyptus globulus (Labill) and Senna didymobotrya (Fresenius) in mice models

Introduction: Pain is managed using conventional drugs like paracetamol, aspirin and diclofenac among others. Synthetic drugs have many side effects. This study aimed at evaluating the analgesic potential of the dichloromethane leaf extracts of Eucalyptus globulus and Senna didymobotrya in mice. Methods: The dichloromethane leaf extracts of E. globulus and S. didymobotrya were subjected to quantitative phytochemical analysis using gas chromatography-mass spectrophotometry (GC-MS). In vivo analgesic evaluation comprised of nine groups of animals (Swiss albino mice): normal, positive, negative control and six experimental groups that received 25, 50, 100, 150, 200 and 250 mg/kg body weight of each plant extract intraperitoneally. Thirty minutes later, they were injected with 0.01 mL of 2.5% formalin. The animals in positive control group were administered diclofenac (15 mg/kg) and formalin, the normal control mice received 3% dimethyl sulfoxide (DMSO) in normal saline, while the negative group received DMSO in normal saline and formalin. All the doses were administered intraperitoneally. The duration of shaking and licking of the injected paw was scored and analyzed. Results: The analysis revealed that E. globulus contained alpha-pinenes, endo-fenchol, α-eudesmol, myrcene, camphene, alpha-phellandrene, limonene, and camphor while S. dymobotrya possessed camphene, alpha-phellandrene, limonene, and camphor. In the late phase, E. globulus at the doses of 25, 50, 100, 150, 200 and 250 mg/kg reduced the paw licking time by 34.03%, 60.79%, 84.33 %, 90.65%, 94.49%, 98.52%, respectively while S. didymobotrya extract reduced the paw licking time by 26.48%, 32.96%, 87.04%, 91.27%, 93.40%, 90.97%, and 96.82%, respectively. Conclusion: The results of this study validate and support the traditional uses of these plants as analgesics.
Influences of Commuter Personality and Preferences on Travel Intention in Developing Countries: A case of Bangkok

Travel intention can directly or indirectly affects travel choices as well as personal attributes such as car ownership and income that important for future transportation planning of developing countries. This study hypothesized that the real travel intention is influenced by travelers’ personality and preference which are free from constraints. Car preference and two types of personality, environmental concern and frugal lifestyle, were selected as the important factors influencing travel intention of travelers especially in developing countries. High level of car preference induced commuters to choose faster travel modes, car and mass transits. Eco-friendly commuters had high intention to travel by environmental friendly modes. Conventional public transports are preferable modes of commuters who have higher level of frugal lifestyle. The intention to refrain from car use is higher for the commuters who have both eco-friendly and frugal personality. Besides, personality and preference also affected attitudes to travel of the commuters.
Leaper

Frequency-based cache replacement policies that work well on page-based database storage engines are no longer sufficient for the emerging LSM-tree (Log-Structure Merge-tree) based storage engines. Due to the append-only and copy-on-write techniques applied to accelerate writes, the state-of-the-art LSM-tree adopts mutable record blocks and issues frequent background operations (i.e., compaction, flush) to reorganize records in possibly every block. As a side-effect, such operations invalidate the corresponding entries in the cache for each involved record, causing sudden drops on the cache hit rates and spikes on access latency. Given the observation that existing methods cannot address this cache invalidation problem, we propose Leaper, a machine learning method to predict hot records in an LSM-tree storage engine and prefetch them into the cache without being disturbed by background operations. We implement Leaper in a state-of-the-art LSM-tree storage engine, X-Engine, as a light-weight plug-in. Evaluation results show that Leaper eliminates about 70% cache invalidations and 99% latency spikes with at most 0.95% overheads as measured in real-world workloads.
The properties of the clumpy torus and BLR in the polar-scattered Seyfert 1 galaxy ESO 323-G77 through X-ray absorption variability

GM thanks Pilar Esquej and Almudena Alonso-Herrero for useful discussions. This work is based on observations obtained with XMM–Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. This research has also made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. We made use of data obtained from the Chandra Data Archive and the Chandra Source Catalogue, and software provided by the Chandra X-ray Center (CXC) and of data obtained from the Swift observatory. We also made use of data from the Suzaku observatory, a collaborative mission between the space agencies of Japan (JAXA) and the USA (NASA). BAG, GM and MS thank the Spanish MINECO for support through the Spanish Plan Nacional de Astronomia y Astrofisica under grant AYA2010-21490-C02-02.
THE EFFECT OF MODEL CHOICE AND SAMPLE SIZE ON STATISTICAL TOLERANCE LIMITS by

Statistical tolerance limits are estimates of large (or small) quantiles of a distribution, quantities which are very sensitive to the shape of the tail of the distribution. The exact nature of this tail behavior cannot be ascertained from small samples, so statistical tolerance limits are frequently computed using a statistical model chosen on the basis of theoretical considerations or prior experience with similar populations. This report illustrates the effects of such choices on the computations.
Smaller, more accurate regression forests using tree alternating optimization

Regression forests, based on ensemble approaches such as bagging or boosting, have long been recognized as the leading off-the-shelf method for regression. However, forests rely on a greedy top-down procedure such as CART to learn each tree. We extend a recent algorithm for learning classiﬁcation trees, Tree Alternating Optimization (TAO) , to the regression case, and use it with bagging to construct regression forests of oblique trees, having hyperplane splits at the decision nodes. In a wide range of datasets, we show that the resulting forests exceed the accuracy of state-of-the-art algorithms such as random forests, AdaBoost or gradient boosting, often considerably, while yielding forests that have usually fewer and shallower trees and hence fewer parameters and faster inference overall. This result has an immense practical impact and advocates for the power of optimization in ensemble learning.
Seasonal variation of citrinin in traditionally brewed African beer

summer, 19.4°C and 7.1% for spring, and 16.3°C and 6.5% for winter. Samples were analysed for the mycotoxincitrinin using a commercial Ridascreen (citrinin) ELISA kit, validated for the purpose of the study. The recovery level achieved for citrinin in beer samples using the kit was 117 ± 15%. The interassay and intra-assay variations were 17.8 and 14.8% respectively. Standard curves prepared using traditional beer samples were similar to the standard curves prepared using the kit standards. The mean citrinin concentrations found in the various samples were 257.6 µg/kg, 99.0 and 185.7 µg/kg for the summer, spring and winter seasons respectively and the levels ranged between 35.6 µg/kg (min) and 924.2 µg/kg (max). Statistically significant differences (p ≤0.05) were found between the citrinin levels of the summer and spring samples and also between citrinin levels of the spring and winter samples (p ≤0.001). The relatively high levels of citrinin in selected samples may, with prolonged exposure, have adverse impacts on the health of consumers. Thus further studies into the origin and levels of this mycotoxin should be a priority.
Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural Dependency Graph

Understanding relationships between feature variables is one important way humans use to make decisions. However, state-of-the-art deep learning studies either focus on task-agnostic statistical dependency learning or do not model explicit feature dependencies during prediction. We propose a deep neural network framework, dGAP , to learn neural dependency Graph and optimize structure-Aware target Prediction simultaneously. dGAP trains towards a structure self-supervision loss and a target prediction loss jointly. Our method leads to an interpretable model that can disentangle sparse feature relationships, informing the user how relevant dependencies impact the target task. We empirically evaluate dGAP on multiple simulated and real datasets. dGAP is not only accurate, but can also recover the correct dependency structure.
Generating Programmatic Referring Expressions via Program Synthesis

Incorporating symbolic reasoning into machine learning algorithms is a promising approach to improve performance on learning tasks that require logical reasoning. We study the problem of generating a programmatic variant of referring expressions that we call referring relational programs. In particular, given a symbolic representation of an image and a target object in that image, the goal is to generate a relational program that uniquely identiﬁes the target object in terms of its attributes and its relations to other objects in the image. We propose a neurosymbolic program synthesis algorithm that combines a policy neural network with enumerative search to generate such relational programs. The policy neural network employs a program interpreter that provides immediate feedback on the consequences of the decisions made by the policy, and also takes into account the uncertainty in the symbolic representation of the image. We evaluate our algorithm on challenging benchmarks based on the CLEVR dataset, and demonstrate that our approach significantly outperforms several baselines.
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games

Efﬁciently computing equilibria for multiplayer games is still an open challenge in computational game theory. This paper focuses on computing Team-Maxmin Equilibria (TMEs), which is an important solution concept for zero-sum multiplayer games where players in a team having the same utility function play against an adversary independently. Existing algorithms are inefﬁcient to compute TMEs in large games, especially when the strategy space is too large to be represented due to limited memory. In two-player games, the Incremental Strategy Generation (ISG) algorithm is an efﬁcient approach to avoid enumerating all pure strategies. However, the study of ISG for computing TMEs is completely unexplored. To ﬁll this gap, we ﬁrst study the properties of ISG for multiplayer games, showing that ISG converges to a Nash Equilibrium (NE) but may not converge to a TME. Second, we design an ISG variant for TMEs (ISGT) by exploiting that a TME is an NE maximizing the team’s utility and show that ISGT converges to a TME and the impossibility of relaxing conditions in ISGT. Third, to further improve the scalability, we design an ISGT variant (CISGT) by using the strategy space for computing an equilibrium that is close to a TME but is easier to be computed as the initial strategy space of ISGT. Finally, extensive experimental results show that CISGT is orders of magnitude faster than ISGT and the state-of-the-art algorithm to compute TMEs in large games.
On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach

: Soil water content (SWC) performs an important role in many areas including agriculture, drought cases, usage of water resources, hydrology, crop diseases and aerology. However, the measurement of the SWC over large terrains with standard computational techniques is very hard. In order to overcome this situation, remote sensing tools are preferred, which can produce much more successful results in less time than standard calculation techniques. Among all remote sensing tools, synthetic aperture radar (SAR) has a significant impact on determining SWC over large terrains. The main objective of this study is to predict SWC on a yearly basis over the vegetation-covered terrains with the aid of different machine learning techniques and SAR based Radarsat-2 data, which obtained in 2015 and 2016 years.The proposed system consists of several stages, respectively. In the feature extraction stage, the backscatter coefficients of different polarizations and the parameters obtained from different models of decomposition (Freeman-Durden and H/A/ α ) were combined and nine polarimetric features were formed for each sample point. In the next stage, support vector regression (SVR), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) were employed for the prediction of SWC. In the last stage, a machine learning based feature selection was implemented to the obtained feature vectors for determining optimal feature sets. Finally, a feature set with 6 parameters was determined as most optimal feature set over the SWC prediction and a slightly better performance was observed thanks to this feature set compared to the other results
AN EXPLORATIVE STUDY FOR THERAPIST TREATMENT FIDELITY IN AN ASYNCHRONOUS WEB-BASED TREATMENT

English) . 3 Abstract (Dutch) . 4Dutch) . 4 Preface . 5 Index . 7 Introduction . 9 Online treatments . 9 Advantages and disadvantages of online treatments . 9 Alcohol consumption in the Netherlands . 10 A web-based treatment . 11 Treatment Components and therapeutic skills . 12 Therapist treatment fidelity . 14 Direction of Research . 16 Research Questions . 17 Method . 18 Participants and Data . 18 Treatment Content . 19 Concept of analysis . 22 Results . 27 Quantitative data . 28 Techniques of motivational interviewing . 28 Therapeutic Alliance . 33 Reflection . 35 Giving information . 37 Other techniques . 39 Questions . 40 Connection . 41 R.V. Roskam Cross your T’s and dot your I’s 8 Discussion . 43 Therapist treatment fidelity . 43 Therapeutic skills . 45 The therapeutic function of codes . 49 The connection . 49 Research limitations . 49 Suggestions for future research . 51 Suggestions for improvement and implementation . 53 Conclusion . 55 Conflict of interest . 56 References . 57 About the Author . 65 R.V. Roskam Cross your T’s and dot your I’s
LAGRANGIAN INTERSECTION OF THE GAUSS IMAGES OF ISOPARAMETRIC HYPERSURFACES (A PRELIMINARY REPORT ON JOINT WORK WITH H. IRIYEH, H. MA AND R. MIYAOKA)

It is a quite interesting problem in geometry to study Lagrangian submanifolds L of various Kähler manifolds (M,ω, g, J). From about 2005, I am working on Lagrangian submanifolds of complex hyperquadrics obtained as the Gauss images of isoparametric hypersurfaces jointly with Professor Hui Ma of Tsinghua University in Beijing. I am reporting on progress of our joint work at this meeting every time. In this note we shall mention recent results on Lagrangian intersection of the Gauss images of isoparametric hypersurfaces in my new joint work with Hiroshi Iriyeh, Hui Ma and Reiko Miyaoka.
AND8453-Loop Control Design of an ac-dc Adapter Using the NCP1250

The NCP1250 controller lends itself very well to the design of small to moderate output power offline converters. Despite its ease of implementation and the limited number of active pins, the control loop design cannot be overlooked, especially if high volumes are at stake. To further ease the work of the design engineer, ON Semiconductor has developed on an automated design tool based on Microsoft Excel®. Rather than being a simple spreadsheet, the software plots the various ac responses based on the hardware component values and suggests a compensation scheme based on the popular TL431. Follow the steps. A Compact Adapter The NCP1250 includes everything needed to build a compact and robust ac−dc converter including Over Voltage Protection (OVP), Over Temperature Protection (OTP) and finally, input line Over Power Protection (OPP). Operating from a 65 kHz switching frequency, the adapter presented in Figure 1 delivers 19 V with a nominal output current capability of 3 A continuous. Please note the absence of the front−end rectifying section, purposely ignored for the sake of simplicity.
Investigation of plant flavonoids as potential dengue protease inhibitors

Introduction: Dengue infection is one of the worldwide problems with an increasing rate. Dengue virus (DENV) belonging to Flaviviridae family carries a positive single strand RNA, consists of four serotypes, gives rise to dengue shock syndrome, undifferentiated fever (DF), and dengue hemorrhagic fever. Unfortunately, there is no licensed drug available for various serotypes and the vaccine candidates are still under investigation. Flavonoids are a group of important phytochemicals of many plants used against various viruses. Therefore, the present study was designed to screen the subset of plant flavonoids against the DENV NS2/NS3 protease of 3L6P and 2VBC of serotype 1 and serotype 4 respectively by in silico docking. Methods: Flavonoids were subjected to docking study using AutoDock Vina. The lead molecule was further investigated for its dengue viral protease inhibition. Further the cytotoxicity of lead molecule was tested by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Absorption, distribution, metabolism, excretion and toxicity (ADMET) study was performed using OSIRIS software. Results: The flavonoids amentoflavone, quercetin-3-O-β-D-glucopyranoside, avicularin, reynoutrin, silymarin and scutallarein showed potential interactions in both serotype 1 and serotype 4 and hence these flavonoids can be served as potential lead candidates to stop viral replication. Amentoflavone, the lead molecule, showed potential in-vitro protease inhibition activity and also the concentration was below the minimal effective level for the in-vitro cytotoxicity test conducted. Conclusion: This finding has laid the groundwork for eventual identification of these flavonoids in developing potential inhibitor candidates for further development by in-vitro extraction and chemical modification methods.
Generalized eigenfunctions and complete semiseparable solutions for Stokes flow in spheroidal coordinates

The stream function y/ for axisymmetric Stokes flow satisfies the wellknown equation E4y/ = 0. In spheroidal coordinates the equation E2y/ = 0 admits separable solutions in the form of products of Gegenbauer functions of the first and second kind, and the general solution is then represented as a series expansion in terms of these eigenfunctions. Unfortunately, this property of separability is not preserved when one seeks solutions of the equation E4i// = 0. The nonseparability of Ea >// = 0 in spheroidal coordinates has impeded considerably the development of theoretical models involving particle-fluid interactions around spheroidal objects. In the present work the complete solution for ^ in spheroidal coordinates is obtained as follows. First, the generalized 0-eigenspace of the operator E2 is investigated and a complete set of generalized eigenfunctions is given in closed form, in terms of products of Gegenbauer functions with mixed order. The general Stokes stream function is then represented as the sum of two functions: one from the 0-eigenspace and one from the generalized 0-eigenspace of the operator E . A rearrangement of the complete expansion, in such a way that the angular-type dependence enters through the Gegenbauer functions of successive order, leads to some kind of semiseparable solutions, which are given in terms of full series expansions. The proper solution subspace that provides velocity and vorticity fields, which are regular on the axis, is given explicitly. Finally, it is shown how these simple and generalized eigenfunctions reduce to the corresponding spherical eigenfunctions as the focal distance of the spheroidal system tends to zero, in which case the separability is regained. The usefulness of the method is demonstrated by solving the problem of the flow in a fluid cell contained between two confocal spheroidal surfaces with Kuwabara-type boundary conditions. Received March 18, 1992. 1991 Mathematics Subject Classification. Primary 76D07; Secondary 33A50, 33A70, 35C10, 35D99, 35J30.
BRAIN TUMOR SEGMENTATION USING COVOLUTIONAL NEURAL NETWORK IN MRI IMAGES

Brain tumor segmentation methodology is based on Convolutional Neural Networks (CNN), by exploring into small 3x3 kernels. The employment of small kernels permits coming up with a deeper architecture, besides having a positive impact against over fitting, given the less variety of masses within the network and also investigating on the utilization of intensity normalization as a pre-processing step, which is not common in Convolution Neural Network based segmentation methods, and well-tried in conjunction with information augmentation to be intolerably in effect for neoplasm segmentation in magnetic resonance imaging pictures
A Novel Algorithm for Removal of Herringbone Artifact in Brain MR Images Using FFT and Canny Edge Detector

: Magnetic Resonance Images of Brain often contain herringbone artifact in the form of stripes spread in either frequency or phase encoding directions throughout the image. These artifacts create a problem in image enhancement and segmentation process and they have to be removed for accurate segmentation of region of interest. This paper presents a novel algorithm to remove herringbone artifact using frequency domain technique which preserves image details and sharpness of edges. The algorithm is developed using Fast Fourier Transform and Canny Edge detector. Filter is created using Canny Edge detector to pick the bright spots created by herringbone artifact. Frequency spectrum of artifact image is multiplied with this filter to zero out the frequencies of artifact. Final image is restored by taking the inverse Fast Fourier Transform. Accuracy of the image restoration is jeopardized at minimal degree in the case where pixels are falsely picked up as artifact. The quality of the processed image is evaluated using signal to noise ratio and energy loss metrics. The results shows that there is a greater improvement in signal to noise ratio and negligible energy loss in the processed image and suggests that the novel algorithm presented in this paper is suitable in processing and removing herringbone artifact in brain MR images.