Miracle-dz/UniFAR-FTUs huggingface dataset:
APPLE: Adaptive Planner Parameter Learning From Evaluative Feedback

Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however, fine-tuning of the system parameters by an expert is typically required before the system can navigate as expected. To alleviate this requirement, the recently-proposed Adaptive Planner Parameter Learning paradigm allows robots to learn how to dynamically adjust planner parameters using a teleoperated demonstration or corrective interventions from non-expert users. However, these interaction modalities require users to take full control of the moving robot, which requires the users to be familiar with robot teleoperation. As an alternative, we introduce apple, Adaptive Planner Parameter Learning from Evaluative Feedback (real-time, scalar-valued assessments of behavior), which represents a less-demanding modality of interaction. Simulated and physical experiments show apple can achieve better performance compared to the planner with static default parameters and even yield improvement over learned parameters from richer interaction modalities.
Making Sense of Being and Becoming Filipinos: An Indigenous Psychology Perspective

This qualitative study explored how 36 undergraduate students from the University of the Philippines who were born, raised and currently residing in the Philippines make sense of their "pagka-Pilipino" (being Filipinos). Using the method of ginabayang talakayan (indigenous facilitated discussions), it was found that notions of "being Filipino" were shaped by any of three factors: a sense of shared origins (pinagmulan); growing up in a similar cultural milieu (kinalakhan) and a shared consciousness (kamalayan). This may suggest uniformity in the participants' defmitions of "who the Filipino is" but analysis reveals that different people and groups tend to place different emphases on the three factors in their own attempts to come to terms with their identities as Filipinos. Hence, "Filipino" and "being Filipino" may evoke different meanings among different people.
Simultaneous Measurements of Mixing Fraction and Velocities of a Coaxial Jet in a Turbulent Channel Flow

Simultaneous velocity and concentration measurements of the isothermal mixing of a confined coaxial jet in air are performed in a small scale windtunnel at ambient conditions. The Reynolds number is 26000 based on the velocity difference of streams and on the diameter of the injection port. Instantaneous concentration measurements are imple- mented using planar laser induced fluorescence (PLIF) of acetone seeded injected flow. Simultaneous three-component velocity measurements are performed using stereo particle image velocimetry (PIV). The contribution provides details of the PLIF-PIV measurement setup and of the methodology to obtain instantaneous measurements of the local mixing fraction and velocity. Ensemble averaged velocities, concentrations, Reynolds stresses and turbulent mass fluxes are de- termined at four positions downstream of the nozzle using up to 3600 PLIF-PIV recordings. The convergence of the root-mean-square of velocity and concentration measurements is assessed in exemplary regions within the shear layers of the mixing zone. The recovered profiles of ensemble averaged velocity, Reynolds stresses and turbulent mass fluxes are compared to Reynolds averaged Navier-Stokes (RANS) and scalar transport simulations. Accompanying RANS cal- culation of the flow exhibit a strong underestimation of mixing and diffusion rates which can be attributed to the strong anisotropic behavior of this flow.
A quantitative cross‐comparison of container networking technologies for virtualized service infrastructures in local computing environments

Container networking is emerging as a game‐changer paradigm for the deployment of virtualized service infrastructures in a faster and reliable way. Nevertheless, small and medium enterprises (SMEs) are still skeptical to revise their business in this direction because of the absence of deep studies showing its effectiveness in real deployments leveraging local computing environments. To bridge this gap, this article presents a quantitative cross‐comparison of cutting‐edge technologies for container networking (including Docker as a container engine, Docker Swarm and Kubernetes as orchestrators, bare‐metal and OpenStack cloud as deployment platform), properly integrated to realize a virtualized service infrastructure within a commercial workstation. Initial experimental tests are conducted to identify the most suitable combination of technologies for high‐load environments, where many clients contact the virtualized service infrastructure to download files of large size. Obtained results demonstrate that the combination of Docker and Kubernetes generally ensures better performance on the bare‐metal deployment platform, thus emerging as mature and effective solutions to be used by SMEs. Finally, the behavior of the identified virtualized service infrastructure is also evaluated in a smart farm use case, where containers are in charge of processing images provided by mobile drones for monitoring purposes. In addition, in this case, the conducted study highlights the promising capability offered by container networking in real deployments, exploiting local computing environments.
A Combination of Lexicon and Machine Learning Approaches for Sentiment Analysis on Facebook

The increase of user-generated content (UGC) on the Internet has led previous studies to propose various sentiment analysis approaches to understand public opinion. The primary goal is to enhance engagement through social media by analyzing various feedback. Sentiment analysis is performed based on two approaches i.e. machine learning and lexicon-based. Since approaches based on machine learning require costly preparation of training dataset and the approaches based on lexicon produce unsatisfactory performance, in this paper, both approaches are combined to perform sentiment analysis on Facebook comments. The importance of a lexicon-based approach to automatically construct the labeled data for machine learning sentiment classification is discussed in this paper. Experiments performed using the Universiti Utara Malaysia (UUM) Facebook posts show that using the combined lexicon-based and machine learning approach on two classifiers i.e. Naïve Bayes and Support Vector Machine outperform the single approaches to produce more accurate sentiment classifications.
This document is a preprint version of the following article: Title: Evidence of Convective Instability in Congested Traffic Flow: A Systematic Empirical and Theoretical Investigation

An extended open system such as traffic flow is said to be convectively unstable if perturbations of the stationary state grow but propagate in only one direction, so they eventually leave the system. By means of data analysis, simulations, and analytical calculations, we give evidence that this concept is relevant for instabilities of congested traffic flow. We analyze detector data from several hundred traffic jams and propose estimates for the linear growth rate, the wavelength, the propagation velocity, and the severity of the associated bottleneck that can be evaluated semi-automatically. Scatter plots of these quantities reveal systematic dependencies. On the theoretical side, we derive, for a wide class of microscopic and macroscopic traffic models, analytical criteria for convective and absolute linear instabilities. Based on the relative positions of the stability limits in the fundamental diagram, we divide these models into five stability classes which uniquely determine the set of possible elementary spatiotemporal patterns in open systems with a bottleneck. Only two classes, both dominated by convective instabilities, are compatible wiqth observations. By means of approximate solutions of convectively unstable systems with sustained localized noise, we show that the observed spatiotemporal phenomena can also be described analytically. The parameters of the analytical expressions can be inferred from observations, and also (analytically) derived from the model equations.
Advanced modeling with Dirichlet and Gaussian processes

As a modeling tool, Gaussian processes are particularly attractive for nonparametric regression with Gaussian noise where one can truly carry out exact process level computations due to normal-normal conjugacy (notwithstanding the O(n) computational complexity). However, they can be utilized more broadly in models which embeds, often as a latent component through parameter expansion, a Gaussian process regression with Gaussian noise. Two particularly important models are discussed below.
Propene epoxidation with in-site H2O2 produced by H2/O2 non-equilibrium plasma

A novel in-site H2O2 strategy is proposed for the liquid-phase epoxidation of propene catalyzed by titanium silicalite (TS-1). Said in-site H2O2 strategy is based on the direct synthesis of H2O2 from H2 and O2 under atmospheric pressure via nonequilibrium plasma reactions. Methanol is used to absorb H2O2 from the dielectric barrier discharge (DBD) reactor. Methanol also serves as the desired solvent for the liquid-phase epoxidation reactor. The efficiency of the integrated process for in-site synthesis of H2O2 reached 69% selectivity, and 62% yield with a nonexplosive H2/O2 mixture when the DBD reactor worked at an input power of 3.5 W (energy consumption 12.4 kWh/kg H2O2). The liquid-phase epoxidation of propene using the in-site H2O2 successfully proceeded over an 18 h time course under the conditions of 50 °C and 3.0 MPa, more than 92% H2O2 conversion and more than 93% PO selectivity were obtained. The two-reactor integrated process worked smoothly during continuous operation, no performance decay was observed for both the DBD reactor and the epoxidation reactor. These facts mean that our in-site H2O2 strategy for PO synthesis is promising. © 2007 American Institute of Chemical Engineers AIChE J, 2007
Reuse Intention of Internet Primary Bank with IT Convergence: An Extended Technology Acceptance Model Study

Due to the rapid spread of IT and services, most banking services are turning to boost a finance industry. Fintech, a combination of finance and technology, has created a new financial transaction model that is differentiated from existing financial transaction methods by combining innovative ideas and advanced technologies. The development of this innovative technology industry has especially influenced mobile technology, and furthermore, it has promoted the growth of the financial industry. In Korea, the internet primary bank was combined with new financial services with fintech IT convergence in 2017. The internet primary bank combined with these advanced technologies have made a big difference in Korea's financial market. It was also surprising to see the number of consumers in Korea. But, really important is to maintain a consistent customer reuse intention. This study investigated factors affecting the intention to continuously use it for consumers using Kakao Bank, which shows a higher status of subscription retention than K-Bank. This study aims to analyze the factors affecting the actual reuse intention through the experience of using internet primary bank. For this study, the main variables of the expanded technology acceptance model were modified and applied to establish a research model for variables affecting the intention to reuse. This research can help for marketing by checking the impact variables on the internet primary bank reuse intention. And we would like to draw implications for successful stabilization of internet primary banking in South Korea.
Increased awareness of the complexity of the link between insecure attachment and development and maintenance of MUS and long-term illness

In this article, we explore the role of attachment in the development of medically unexplained symptoms (MUS) and response to physical illness. We review the evidence that attachment insecurity is common in people with different forms of MUS and certain long-term physical conditions. We discuss a possible developmental model for understanding how MUS develop. We conclude with discussion of potential therapies and implications for services.
Efficient Splitting Characteristic Method for Solving Multi-component Aerosol Spatial Transports in Atmospheric Environment

In this work, we develop a splitting characteristic method for solving multi-component atmospheric aerosol spatial dynamics, which can efficiently evaluate aerosol spatial transports by using large time step sizes. The method can compute the multicomponent aerosol distributions in high-dimensional domains with large ranges of concentrations and for different aerosol types. Numerical tests show the computational efficiency of the proposed method. An actual simulation focusing on the sulfate pollution is taken in the domain with a varying wind field. We also simulate multi-component aerosol transports in a large area in the southeast of America. The developed algorithm can be applied for the large scale predictions of multi-component aerosols in multi-regions and multi-levels in atmospheric environment. Keywords–Atmospheric aerosol transport; Multi-component; Splitting; Characteristic method; Efficiency; High accuracy.
A Hybrid Model-Based and Data-Driven Approach for Cloud Removal in Satellite Imagery Using Multi-Scale Distortion-Aware Networks

Cloud layer contamination is a common problem in optical remote sensing images. Cloud removal from remote sensing images has attracted increasing attention in recent years. To this end, we propose a multi-scale distortion-aware network for cloud removal from remote sensing images. A novel Cloud Aware and Feature Extraction (CAFE) module is developed by incorporating the physical model of cloud distortion considering atmosphere light, cloud reflectance light and cloud transmission. These distortion factors in CAFE module are encoded into trainabile parameters for feature extraction from contaminated images. The network is trained in an end-to-end manner with cloud contaminated images and ground truth data. Finally, experimentl results on the Remote sensing Image Cloud rEmoving (RICE) dataset demonstrate the effectiveness of the proposed approach.
ANALYSIS OF TAPER WIDTH VARIATIONS IN LINEAR RELUCTANCE MACHINE

Linear motors are being used in different application with a huge popularity in the use of transport industry. With the invention of maglev trains and other high speed trains, linear motors are being used for the translation and braking applications for these systems. However, a huge drawback for the linear motor design is the cogging force, low thrust values, and voltage ripples. This paper aims to study the force analysis with change in taper/teeth width of the motor stator and mover to understand the best teeth ratio to obtain a high flux density and a high thrust. The analysis is conducted through JMAG software and it is found that the optimum teeth ratio for both the stator and mover gives an increase of 94.4% increases compared to the 0.5 mm stator and mover width.
Impact of monoclonal antibody treatment on hospitalization and mortality among non-hospitalized adults with SARS-CoV-2 infection

Background: Monoclonal antibody (mAb) treatment may prevent complications of COVID-19. We sought to quantify the impact of bamlanivimab monotherapy on hospitalizations and mortality, as well as Emergency Department (ED) visits without hospitalization, among outpatients at high risk of COVID-19 complications. Methods: We compared patients receiving mAb to patients who met criteria but did not receive mAb from December 2020 through March 2021. The study population selection used propensity scores to match 1:1 by likelihood to receive mAb. The primary outcome was hospitalization or all-cause mortality within 28 days; the secondary outcome was hospitalization or ED visit without hospitalization within 28 days. Odds ratios (OR) calculation used logistic regression modeling including propensity score and mAb receipt predictors. Results: The study population included 234 patients receiving mAb and 234 matched comparator patients not receiving mAb. Patients receiving mAb were less likely to experience hospitalization or mortality (OR 0.31, 95% confidence interval [95%CI] 0.17-0.56, p=0.00001) and hospitalization or ED visit without hospitalization (OR 0.50, 95%CI 0.43-0.83, p=0.007). The impact of mAb was more pronounced in prevention of hospitalization (among all age groups, OR 0.35, 95%CI 0.19-0.66, p=0.001) than mortality or ED visit without hospitalization, and most strongly associated with patients age 65 years and older (primary outcome OR 0.28, 95%CI 0.14-0.56, p=0.0003). Conclusions: Bamlanivimab monotherapy was associated with reduction in the composite outcome of hospitalizations and mortality in patients with mild-moderate COVID-19. The benefit may be strongest in preventing hospitalization in patients ages 65 years or older.
NTRK oncogenic fusions are exclusively associated with the serrated neoplasia pathway in the colorectum and begin to occur in sessile serrated lesions

Neurotrophic tropomyosin receptor kinase (NTRK) gene fusions are emerging tissue‐agnostic drug targets in malignancies including colorectal carcinomas (CRCs), but their detailed landscape in the context of various colorectal carcinogenesis pathways remains to be investigated. In this study, pan‐tropomyosin receptor kinase (TRK) protein expression was assessed by immunohistochemistry (IHC) in retrospectively collected colorectal epithelial tumor tissues, including 441 CRCs [133 microsatellite instability‐high (MSI‐high) and 308 microsatellite stable (MSS)] and 595 premalignant colorectal lesions (330 serrated lesions and 265 conventional adenomas). TRK‐positive cases were then subjected to next‐generation sequencing and/or fluorescence in situ hybridization to confirm NTRK rearrangements. TRK IHC positivity was not observed in any of the MSS CRCs, conventional adenomas, traditional serrated adenomas, or hyperplastic polyps, whereas TRK positivity was observed in 11 of 58 (19%) MLH1‐methylated MSI‐high CRCs, 4 of 23 (17%) sessile serrated lesions with dysplasia (SSLDs), and 5 of 132 (4%) sessile serrated lesions (SSLs). The 11 TRK‐positive MSI‐high CRCs commonly harbored CpG island methylator phenotype‐high (CIMP‐high), MLH1 methylation, BRAF/KRAS wild‐type, and NTRK1 or NTRK3 fusion (TPM3–NTRK1, TPR–NTRK1, LMNA–NTRK1, SFPQ–NTRK1, ETV6–NTRK3, or EML4–NTRK3). Both NTRK1 or NTRK3 rearrangement and BRAF/KRAS wild‐type were detected in all nine TRK‐positive SSL(D)s, seven of which demonstrated MSS and/or CIMP‐low. TRK expression was selectively observed in distorted serrated crypts within SSLs and was occasionally localized at the base of serrated crypts. NTRK fusions were detected only in SSLs of patients aged ≥50 years, whereas BRAF mutation was found in younger age‐onset SSLs. In conclusion, NTRK‐rearranged colorectal tumors develop exclusively through the serrated neoplasia pathway and can be initiated from non‐dysplastic SSLs without BRAF/KRAS mutations prior to full occurrence of MSI‐high/CIMP‐high. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
How well can we guess the outcome of measurements of non-commuting observables?

According to Heisenberg’s uncertainty relation, there is an ultimate limit to how precisely we may predict the outcome of position and momentum measurements on a quantum system. We show that this limit may be violated by an arbitrarily large factor if one aims, instead, to guess the unknown value of a past measurement. For experiments on a single quantum system, the precise assignment of past position and momentum measurement outcomes is accompanied by large uncertainty about their linear combinations, while we show that entanglement with an ancillary system permits accurate retrodiction of any such linear combination. Finally, we show that the outcomes of experiments that jointly measure multiple linear combinations of position and momentum observables by means of ancillary probe particles can also be guessed with no formal lower limit. We present quantitative results for projective measurements and for generalized measurements where all components are prepared in Gaussian states.
Small doubling, atomic structure and $\ell$-divisible set families

Let $\mathcal{F}\subset 2^{[n]}$ be a set family such that the intersection of any two members of $\mathcal{F}$ has size divisible by $\ell$. The famous Eventown theorem states that if $\ell=2$ then $|\mathcal{F}|\leq 2^{\lfloor n/2\rfloor}$, and this bound can be achieved by, e.g., an `atomic' construction, i.e. splitting the ground set into disjoint pairs and taking their arbitrary unions. Similarly, splitting the ground set into disjoint sets of size $\ell$ gives a family with pairwise intersections divisible by $\ell$ and size $2^{\lfloor n/\ell\rfloor}$. Yet, as was shown by Frankl and Odlyzko, these families are far from maximal. For infinitely many $\ell$, they constructed families $\mathcal{F}$ as above of size $2^{\Omega(n\log \ell/\ell)}$. On the other hand, if the intersection of any number of sets in $\mathcal{F}\subset 2^{[n]}$ has size divisible by $\ell$, then it is easy to show that $|\mathcal{F}|\leq 2^{\lfloor n/\ell\rfloor}$. In 1983 Frankl and Odlyzko conjectured that $|\mathcal{F}|\leq 2^{(1+o(1)) n/\ell}$ holds already if one only requires that for some $k=k(\ell)$ any $k$ distinct members of $\mathcal{F}$ have an intersection of size divisible by $\ell$. We completely resolve this old conjecture in a strong form, showing that $|\mathcal{F}|\leq 2^{\lfloor n/\ell\rfloor}+O(1)$ if $k$ is chosen appropriately, and the $O(1)$ error term is not needed if (and only if) $\ell \, | \, n$, and $n$ is sufficiently large. Moreover the only extremal configurations have `atomic' structure as above. Our main tool, which might be of independent interest, is a structure theorem for set systems with small 'doubling'.
Distributed learning in congested environments with partial information

How can non-communicating agents learn to share congested resources efficiently? This is a challenging task when the agents can access the same resource simultaneously (in contrast to multi-agent multi-armed bandit problems) and the resource valuations differ among agents. We present a fully distributed algorithm for learning to share in congested environments and prove that the agents' regret with respect to the optimal allocation is poly-logarithmic in the time horizon. Performance in the non-asymptotic regime is illustrated in numerical simulations. The distributed algorithm has applications in cloud computing and spectrum sharing. Keywords: Distributed learning, congestion games, poly-logarithmic regret.