Miracle-dz/UniFAR-FTUs huggingface dataset:
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes

We investigate the problem of multiple time series forecasting, with the objective to improve multiple-step-ahead predictions. We propose a multi-task Gaussian process framework to simultaneously model batches of individuals with a common mean function and a specific covariance structure. This common mean is defined as a Gaussian process for which the hyper-posterior distribution is tractable. Therefore an EM algorithm can be derived for simultaneous hyper-parameters optimisation and hyper-posterior computation. Unlike previous approaches in the literature, we account for uncertainty and handle uncommon grids of observations while maintaining explicit formulations, by modelling the mean process in a non-parametric probabilistic framework. We also provide predictive formulas integrating this common mean process. This approach greatly improves the predictive performance far from observations, where information shared across individuals provides a relevant prior mean. Our overall algorithm is called MAGMA (standing for Multi tAsk Gaussian processes with common MeAn), and publicly available as a R package. The quality of the mean process estimation, predictive performances, and comparisons to alternatives are assessed in various simulated scenarios and on real datasets.
Perceptual consequences of centre – surround antagonism in visual motion processing

The experiment consisted of 12 blocks of 40 trials each, resulting in 120 trials per experimental condition. Blocks were administered in random order. The responding hand was changed halfway through the experiment, and the sequence of hand usage was counterbalanced across subjects. One subject was excluded from further analysis because of difficulty in detecting the matching symbols. The electrode positions shown schematically in Fig. 1 are specified in a previous report 30. The EEG was recorded with a sampling rate of 250 Hz and a bandpass of d.c. to 50 Hz. Individual trials were discarded on the basis of blink or electromyogram (EMG) artefacts in the scalp channels exceeding 75 mV, or when lateral eye movements monitored in the horizontal electro-oculogram (EOG) deviated more than 11 mV (18) from fixation. These stringent criteria resulted in a mean rejection rate of 30% of the trials. In order to analyse the SSVEP, artefact-free EEG epochs were averaged separately for each experimental condition and algebraically re-referenced to averaged mastoids by subtracting one-half of the averaged signal recorded from the mastoid opposite the reference mastoid from the averaged signals at each scalp site. The averaging epochs extended from 500 ms before to 2,500 ms after the time point when all streams were synchronized (that is, from 26 to 3,026 ms after flicker onset). SSVEP amplitudes were extracted by means of complex demodulation of the averaged waveforms 15,19,25. To avoid including the visual evoked response to flicker onset in the SSVEP measurements, the first 500 ms of each epoch were excluded from analysis. Thus, mean SSVEP amplitudes were calculated over the interval 526 to 3,026 ms after flicker onset. For testing the significance of SSVEP amplitude changes, the posterior electrode site that exhibited the largest overall attention effect (comparing attended versus ignored positions) was selected for each subject. These amplitude values were subjected to paired t-tests between conditions, and corrected for multiple comparisons by the Bonferroni– Dunn criterion. As a reliability check, the averaged amplitudes across three standard electrode locations (PO3/4, PO7/8 and O1/2) were also subjected to paired t-tests for the attended versus ignored SSVEPs at 8.69 Hz and 20.27 Hz under conditions of attention to separated locations. Only button-presses occurring between 250 and 1,000 ms after target-pair onset were accepted as correct detections. False alarms for the adjacent condition were defined as button presses occurring in response to a target presented at only …
Efficient Algorithms for Stochastic Ride-Pooling Assignment with Mixed Fleets

Ride-pooling, which accommodates multiple passenger requests in a single trip, has the potential to substantially enhance the throughput of mobility-on-demand (MoD) systems. This paper investigates MoD systems that operate mixed fleets composed of “basic supply” and “augmented supply” vehicles. When the basic supply is insufficient to satisfy demand, augmented supply vehicles can be repositioned to serve rides at a higher operational cost. We formulate the joint vehicle repositioning and ride-pooling assignment problem as a two-stage stochastic integer program, where repositioning augmented supply vehicles precedes the realization of ride requests. Sequential ride-pooling assignments aim to maximize total utility or profit on a shareability graph: a hypergraph representing the matching compatibility between available vehicles and pending requests. Two approximation algorithms for midcapacity and high-capacity vehicles are proposed in this paper; the respective approximation ratios are [Formula: see text] and [Formula: see text], where p is the maximum vehicle capacity plus one. Our study evaluates the performance of these approximation algorithms using an MoD simulator, demonstrating that these algorithms can parallelize computations and achieve solutions with small optimality gaps (typically within 1%). These efficient algorithms pave the way for various multimodal and multiclass MoD applications. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the National Science Foundation [Grants CCF-2006778 and FW-HTF-P 2222806], the Ford Motor Company, and the Division of Civil, Mechanical, and Manufacturing Innovation [Grants CMMI-1854684, CMMI-1904575, and CMMI-1940766]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021. 0349 .
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Cardiac Signals.

The ubiquity and rate of collection of cardiac signals produce large, unlabelled datasets. Active learning (AL) can exploit such datasets by incorporating human annotators (oracles) to improve generalization performance. However, the over-reliance of existing algorithms on oracles continues to burden physicians. To minimize this burden, we propose SoCal, a consistency-based AL framework that dynamically determines whether to request a label from an oracle or to generate a pseudo-label instead. We show that our framework decreases the labelling burden while maintaining strong performance, even in the presence of a noisy oracle.
An interval algorithm for finding all solutions of nonlinear resistive circuits

An efficient algorithm is proposed for finding all solutions of nonlinear (not piecewise-linear) resistive circuits with mathematical certainty. This algorithm is based on interval analysis, the dual simplex method, and the contraction method. By numerical examples, it is shown that the proposed algorithm could find all solutions of systems of 500 /spl sim/ 700 nonlinear circuit equations in acceptable computation time.
Sparse phase retrieval of structured signals by Prony's method

The phase retrieval problem consists in the recovery of a complex‐valued signal from the magnitudes of its Fourier transform. Restricting ourselves to the case of sparse structured signals f, which can be represented as a linear combination of N arbitrary translations of a given generator function, we show that almost all f can be recovered from 𝒪 (N2) intensity measurements |ℱ[f](ω)| up to trivial ambiguities. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Field enhancement of a metal grating with nanocavities and its sensing applications

We theoretically investigate electric and magnetic field enhancements in nanocavities of all-metal metasurfaces, which can be controlled by varying two independent parameters of nanocavity width and length. Result shows that electric field enhancement is larger at resonance with nanocavity width 2 nm, while magnetic field enhancement is smaller. Additionally, operating as a sensor, its surface sensitivity reaches a value of 10 while the figure of merit is as high as 2.3. We conclude that a nanocavity inserted in a metal grating enlarges resonant wavelength shift in surface sensing applications, compared to similar structures without a nanocavity. This proposed structure has potential applications in molecular detection, refractive sensing, stem cell differentiation, and nonlinear effect based devices.
Maximizing Nash Social Welfare in 2-Value Instances: The Half-Integer Case

We consider the problem of maximizing the Nash social welfare when allocating a set G of indivisible goods to a set N of agents. We study instances, in which all agents have 2-value additive valuations: The value of a good g ∈ G for an agent i ∈ N is either 1 or s , where s is an odd multiple of 1 / 2 larger than one. We show that the problem is solvable in polynomial time. In [ACH + 22] it was shown that this problem is solvable in polynomial time if s is integral and is NP-hard whenever s = p / q , p ∈ N and q ∈ N are co-prime and p > q ≥ 3. For the latter situation, an approximation algorithm was also given. It obtains an approximation ratio of at most 1 . 0345. Moreover, the problem is APX-hard, with a lower bound of 1 . 000015 achieved at p / q = 5 / 4 . The case q = 2 and odd p was left open. In the case of integral s , the problem is separable in the sense that the optimal allocation of the heavy goods (= value s for some agent) is independent of the number of light goods (= value 1 for all agents). This leads to an algorithm that ﬁrst computes an optimal allocation of the heavy goods and then adds the light goods greedily. This separation no longer holds for s = 3 / 2 ; a simple example is given in the introduction. Thus an algorithm has to consider heavy and light goods together. This complicates matters considerably. Our algorithm is based on a collection of improvement rules that transfers any allocation into an optimal allocation and exploits a connection to matchings with parity constraints.
Microbial Interactions between Marine Microalgae and Fungi: From Chemical Ecology to Biotechnological Possible Applications

Chemical interactions have been shown to regulate several marine life processes, including selection of food sources, defense, behavior, predation, and mate recognition. These chemical communication signals have effects not only at the individual scale, but also at population and community levels. This review focuses on chemical interactions between marine fungi and microalgae, summarizing studies on compounds synthetized when they are cultured together. In the current study, we also highlight possible biotechnological outcomes of the synthetized metabolites, mainly for human health applications. In addition, we discuss applications for bio-flocculation and bioremediation. Finally, we point out the necessity of further investigating microalgae-fungi chemical interactions because it is a field still less explored compared to microalga–bacteria communication and, considering the promising results obtained until now, it is worthy of further research for scientific advancement in both ecology and biotechnology fields.
Developer-Centric Test Amplification The Interplay Between Automatic Generation and Human Exploration

Automatically generating test cases for software has been an active research topic for many years. While current tools can generate powerful regression or crash-reproducing test cases, these are often kept separately from the maintained test suite. In this paper, we leverage the developer's familiarity with test cases amplified from existing, manually written developer tests. Starting from issues reported by developers in previous studies, we investigate what aspects are important to design a developer-centric test amplification approach, that provides test cases that are taken over by developers into their test suite. We conduct 16 semi-structured interviews with software developers supported by our prototypical designs of a developer-centric test amplification approach and a corresponding test exploration tool. We extend the test amplification tool DSpot, generating test cases that are easier to understand. Our IntelliJ plugin TestCube empowers developers to explore amplified test cases from their familiar environment. From our interviews, we gather 52 observations that we summarize into 23 result categories and give two key recommendations on how future tool designers can make their tools better suited for developer-centric test amplification.
Application of supercritical carbon dioxide for the preparation of a piroxicam-beta-cyclodextrin inclusion compound.

PURPOSE Piroxicam is a poorly soluble NSAID, whose solubility is enhanced when included into beta-cyclodextrin. The preparation of a piroxicam-beta-cyclodextrin inclusion compound using supercritical CO2 was investigated. METHODS The solubility and the stability of piroxicam in supercritical CO2 were determined. Then, the influence of the temperature, the pressure and the time of exposure on the inclusion rate was studied. RESULTS The solubility of piroxicam varied over a wide range depending on the temperature and pressure (from 0.006 to 1.500 mg/g of CO2). The temperature and the time of exposure had a great influence on the inclusion yield, while pressure did not and a complete inclusion was achieved by keeping a physical mixture of piroxicam and beta-cyclodextrin (1:2.5 mol/mol) for 6 hours at 150 degrees C and 15 MPa of CO2. This complex was characterized by Differential Scanning Calorimetry, differential solubility and Fourier Transform Infrared Spectrometry. CONCLUSIONS Supercritical carbon dioxide may prove to be a novel useful complexation method of drugs into beta-cyclodextrin.
Application of Interpolation Image by using Bi-Cubic Algorithm

Image magnification is one of the branches in digital image processing that is often required in various applications such as in the field of medicine, multimedia, and in satellite imagery. As technology grows, more and more methods are used for image enlargement. In this study, the image enlargement process performs by using Bi-Cubic spline interpolation method, and the result of image try to compare between the original one and picture after enlargement.
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets

: Robot learning holds the promise of learning ﬂexible policies that gen-1 eralize across a wide range of scenarios. However, such generalization requires 2 sufﬁciently broad datasets of the particular task of interest, which is often pro-3 hibitively expensive to collect. On the other hand, there often exist large amounts 4 of past data from related tasks collected in other contexts. In this work, we study 5 how we can leverage such past data to broaden robot generalization in a number 6 of settings. This includes broadening the generalization of a new target task for 7 which we only have narrow data, and generalizing a task to a new target domain 8 from which we have little or no data. To this end, we collect a diverse ‘bridge’ 9 dataset of 4,700 human demonstrations of a robot performing 33 common kitchen 10 tasks across 3 toy kitchens and 3 toy sinks with varying lighting, robot positions, 11 and backgrounds. We train policies via task-conditioned behavior cloning directly 12 from RGB images on this data mixed with narrow data from the target task or do14 13 main. Our experiments ﬁnd that leveraging the diverse bridge dataset often leads 14 to a 2x improvement in success rate when evaluated on challenging scenarios that 15 demand generalization, even if the user’s domain is substantially different, both 16 visually and functionally, from the bridge data. These results suggest that reusing 17 diverse ofﬂine bridge datasets, including our open-source dataset, may pave the 18 way for broader robot generalization moving forward without requiring extensive 19 datasets to be re-collected for each new project. 20
Coronal Mass Ejections travel time

Abstract Coronal mass ejections (CMEs) are the main source of intense geomagnetic storms when they are earthward directed. Studying their travel time is a key-point to understand when the disturbance will be observed at Earth. In this work, we study the CME that originated the interplanetary disturbance observed on 2013/10/02. According to the observations, the CME that caused the interplanetary disturbance was ejected on 2013/09/29. We obtained the CME speed and estimate of the time of arrival at the Lagrangian Point L1 using the concept of expansion speed. We found that observed and estimated times of arrival of the shock differ between 2 and 23 hours depending on method used to estimate the radial speed.
Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies

We consider the problem of reinforcement learning when provided with (1) a baseline control policy and (2) a set of constraints that the controlled system must satisfy. The baseline policy can arise from a teacher agent, demonstration data or even a heuristic while the constraints might encode safety, fairness or other application-specific requirements. Importantly, the baseline policy may be sub-optimal for the task at hand, and is not guaranteed to satisfy the specified constraints. The key challenge therefore lies in effectively leveraging the baseline policy for faster learning, while still ensuring that the constraints are minimally violated. To reconcile these potentially competing aspects, we propose an iterative policy optimization algorithm that alternates between maximizing expected return on the task, minimizing distance to the baseline policy, and projecting the policy onto the constraint-satisfying set. We analyze the convergence of our algorithm theoretically and provide a finite-sample guarantee. In our empirical experiments on five different control tasks, our algorithm consistently outperforms several state-of-the-art methods, achieving 10 times fewer constraint violations and 40% higher reward on average.
Nonlinear reduced order model of high aspect ratio rectangular wing plate

This paper presents a Combined Modal Finite Element (CMFE) approach to develop a Nonlinear Reduced Order Model (NROM) in order to characterize the nonlinear properties of the wing plate model. The wing plate model is subjected to three types of loading cases. The first case considers a uniformly distributed loading on the whole wing plate model for describing the bending deflection; the second case considers a uniformly distributed loading on both leading and trailing edges with one of them of an opposite direction for describing the twisting deflection; the third case considers the loading on the leading edge for describing a combination of bending-twisting deflection. The accuracy of the results is represented in the form of mean error, the standard deviation of the error and the percentage of error. From the findings, the NROMs are able to predict the nonlinear deformations of the wing plate with a minimal computational time and reasonably good accuracy. The results also indicate the importance of the selection modes when conducting the analysis.
Prioritized training on points that are learnable, worth learning, and not yet learned

We introduce Goldilocks Selection, a technique for faster model training which selects a sequence of training points that are"just right". We propose an information-theoretic acquisition function -- the reducible validation loss -- and compute it with a small proxy model -- GoldiProx -- to efficiently choose training points that maximize information about a validation set. We show that the"hard"(e.g. high loss) points usually selected in the optimization literature are typically noisy, while the"easy"(e.g. low noise) samples often prioritized for curriculum learning confer less information. Further, points with uncertain labels, typically targeted by active learning, tend to be less relevant to the task. In contrast, Goldilocks Selection chooses points that are"just right"and empirically outperforms the above approaches. Moreover, the selected sequence can transfer to other architectures; practitioners can share and reuse it without the need to recreate it.
Local equivalence of some maximally symmetric $(2,3,5)$-distributions III: An-Nurowski surface rolling on a plane

We consider the maximally symmetric $(2,3,5)$-distribution given by the An-Nurowski circle twistor bundle over the product of an An-Nurowski surface and the plane. This circle twistor distribution encodes the configuration space of an An-Nurowski surface rolling without slipping or twisting on the plane. We calculate the vector fields associated to this maximally symmetric $(2,3,5)$-distribution that define a split $\frak{g}_2$ Lie algebra and by projection we obtain an action of $SL(3,\mathbb{R})$ on the submanifold $\mathbb{R}^2\times \mathbb{S}^1$ (the configuration space without a surface).
Renormalization group improvement of the effective potential in massive phi**4 theory: Next-next-next-to-leading logarithm resummation

The renormalization group method is applied to the three-loop effective potential of the massive φ4 theory in the MS scheme in order to obtain the next-next-next-to leading logarithm resummation. For this, we use already known four-loop renormalization group functions and calculate perturbatively evolutions of the parameters (λ, m2, φ and, Λ) along the running scale within the accuracy of the three-loop order. We also comment on the structure of five-loop effective potential using the renormalization group equation for the effective potential and the existing five-loop renormalization group functions. PACS number(s): 11.10. Hi Typeset using REVTEX ∗Electronic address: chung@ctpred.mit. edu †Electronic address: bkchung@nms.kyunghee. ac.kr
A low phase noise oscillator based on substrate integrated coaxial line technology

In this article, a miniaturized resonator based on substrate integrated coaxial line technology (SICL) is presented. The SICL resonator consists of a hairpin half-wavelength open-ended coaxial line element and two symmetrical parallel coaxial feed lines. The gap between the feed lines and the hairpin element can be adjusted properly to obtain a steep phase change, which can result in a higher group delay peak and achieve a superior phase noise performance. Based on the proposed SICL resonator, an X-band oscillator has been designed and demonstrated by using low-cost printed circuit boards (PCBs). Great consistency between the measured and simulated results is verified. According to the measured results, the oscillation frequency of the SICL oscillator is 10.0625 GHz with −1.81 dBm RF output power, and the phase noise is −138.6 dBc/Hz@1 MHz. The statistic figure of merit (FOM) is −206.1 dBc/Hz@1 MHz.
Functional Genomics of Biotic and Abiotic Stresses in Phaseolus vulgaris

Common bean is the most important legume for human consumption in the world, being a crop extremely diverse in cultivation methods, uses, range of environments in which it is adapted, morphological variety, among others. Besides its high demand and produc‐ tion, this crop is threatened by a series of biotic and abiotic adversities during its life cy‐ cle, which leads to losses in yield of up to 100%. In this chapter, we explored the main constraints that affect common bean and the ways this plant reaches tolerance or resist‐ ance to them, highlighting studies at the molecular level that enabled to understand the mechanisms by which common bean perceives, responds, and adapts to a stress condi‐ tion. Special focus has been given to the most recent findings in the understanding of the mechanisms underlying drought tolerance and anthracnose resistance. Thereby, we re‐ viewed some genetic and functional genomic studies concerning the genes and pathways involved in each case. Furthermore, we outline important genetic resources of Phaseolus vulgaris, as well as the technologies and methods used toward these findings.