Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Privileged Function Calls

We are approaching a world, where the CPU merely orchestrates a plethora of specialized devices such as accelerators, RDMA NICs, or non-volatile memory (NVM). Such devices operate by mapping their internal memory directly into an application’s address space for fast, low-latency access. With the latency of modern I/O devices low enough to make traditional system calls a performance bottleneck, kernel interaction has no place on the data path of microsecond-scale systems. However, kernel bypass prevents the OS from controlling and supervising access to the hardware. This paper tries to make a step back by bringing the OS to the critical path again, but with a reduced performance penalty. We pick up on previous ideas for reducing the cost of kernel interaction and propose the fastcall space , a new layer in the traditional OS architecture that hosts specialized and quickly accessible OS functions called fastcalls . Fastcalls can stay on the critical path of a microsecond-scale application because the invocation of fastcall-space functionality is up to 15 times faster than calling kernel functions from user space. We present and evaluate a prototype implementation of the fastcall framework and thereby show how much the overhead of calling into privileged mode can be reduced while using standard CPU features.
DO SHOCKS AFFECT POVERTY PERSISTENCE? EVIDENCE USING WELFARE TRAJECTORIES FROM NICARAGUA

hocks are often primarily associated with downward mobility or short-term movements in and out of poverty. However, households at the bottom of the welfare distribution are likely to face the most constraints to access insurance mechanisms. In this paper, we consider whether shocks directly affect poverty persistence. In order to analyze the impact of shocks on households’ welfare path over time, we define trajectories as the sequence of households’ position along the welfare distribution as time unfolds. Trajectories provide a consistent representation of households’ mobility when the first-order Markov assumption is violated. In a three-round Nicaraguan panel, we assess the role of shocks in driving two specific mobility patterns. We confirm that shocks contribute to downward mobility, but find novel and robust evidence that shocks trigger poverty persistence, preventing upward movement from the bottom of the distribution. This result points to large potential gains from social risk management policies targeting not only the vulnerable non-poor, but also and in priority the poor.
Latitudinal migration of sunspots based on the ESAI database

The latitudinal migration of sunspots toward the equator, which implies there is propagation of the toroidal magnetic flux wave at the base of the solar convection zone, is one of the crucial observational bases for the solar dynamo to generate a magnetic field by shearing of the pre-existing poloidal magnetic field through differential rotation. The Extended time series of Solar Activity Indices (ESAI) elongated the Greenwich observation record of sunspots by several decades in the past. In this study, ESAI’s yearly mean latitude of sunspots in the northern and southern hemispheres during the years 1854 to 1985 is utilized to statistically test whether hemispherical latitudinal migration of sunspots in a solar cycle is linear or nonlinear. It is found that a quadratic function is statistically significantly better at describing hemispherical latitudinal migration of sunspots in a solar cycle than a linear function. In addition, the latitude migration velocity of sunspots in a solar cycle decreases as the cycle progresses, providing a particular constraint for solar dynamo models. Indeed, the butterfly wing pattern with a faster latitudinal migration rate should present stronger solar activity with a shorter cycle period, and it is located at higher latitudinal position, giving evidence to support the Babcock-Leighton dynamo mechanism.
Approximation Methods for Two Classes of Singular Integral Equations

The dissertation consists of two parts. In the first part approximate methods for multidimensional weakly singular integral operators with operator-valued kernels are investigated. Convergence results and error estimates are given. There is considered an application of these methods to solving radiation transfer problems. Numerical results are presented, too. In the second part we consider a polynomial collocation method for the numerical solution of a singular integral equation over the interval. More precisely, the operator of our integral equation is supposed to be of the form aI + bμ−1SμI with S the Cauchy singular integral operator, with piecewise continuous coefficients a and b, and with a Jacobi weightμ. To the equation[aI + bμ−1SμI]u = f we apply a collocation method, where the collocation points are the Chebyshev nodes of the first kind and where the trial space is the space of polynomials multiplied by another Jacobi weight. For the stability and convergence of this collocation method in weightedL spaces, we derive necessary and sufficient conditions. Moreover, the extension of these results to an algebra generated by the sequences of the collocation method applied to the mentioned singular integral operators is discussed and the behaviour of the singular values of the discretized operators is investigated.
Flow reversals in two-dimensional thermal convection in tilted cells

The influence of tilt on flow reversals in two-dimensional thermal convection in rectangular cells with two typical aspect ratios, $\unicode[STIX]{x1D6E4}=\text{width/height}=1$ and 2, are investigated by means of direct numerical simulations. For $\unicode[STIX]{x1D6E4}=1$ , tilt tends to suppress flow reversals. However, it is found that flow reversals characterized by two main rolls are promoted by tilt for $\unicode[STIX]{x1D6E4}=2$ , which are even observed for some cases of small Prandtl numbers ( $Pr$ ) and large tilt angles ( $\unicode[STIX]{x1D6FD}$ ). Different from level cases where the four corner rolls all have opportunities to grow and trigger a flow reversal, the reversals in an anticlockwise tilted cell with $\unicode[STIX]{x1D6E4}=2$ are always led by the growth of the bottom-right or the top-left corner roll. Tilt is favourable for the growth of the bottom-right or the top-left corner roll and thus for breaking the balance between the two main rolls and triggering a flow reversal. The mode decomposition analysis shows that the appearance of the intermediate single-roll mode is crucial for reversals, and flow reversals in a tilted cell with $\unicode[STIX]{x1D6E4}=2$ can be viewed as a mode competition process between single-roll mode and horizontally adjacent double-roll mode. They can only occur in a limited range of $\unicode[STIX]{x1D6FD}$ where the two modes have comparable strength. Furthermore, the Nusselt numbers at the hot plate $Nu_{h}$ and at the cold plate $Nu_{c}$ behave differently during a flow reversal for $\unicode[STIX]{x1D6E4}=2$ due to the preference of single corner roll growth.
Oracle P-values and variable screening

: The concept of P-value was proposed by Fisher to measure inconsistency of data with a speciﬁed null hypothesis, and it plays a central role in statistical inference. For classical linear regression analysis, it is a standard procedure to calculate P-values for regression coeﬃcients based on least squares estimator (LSE) to determine their signiﬁcance. However, for high dimensional data when the number of predictors exceeds the sample size, ordinary least squares are no longer proper and there is not a valid deﬁnition for P-values based on LSE. It is also challenging to deﬁne sensible P-values for other high dimensional regression methods such as penalization and resampling methods. In this paper, we introduce a new concept called oracle P-value to generalize traditional P-values based on LSE to high dimensional sparse regression models. Then we propose several estimation procedures to approximate oracle P-values for real data analysis. We show that the oracle P-value framework is useful for developing new and powerful tools to enhance high dimensional data analysis, including variable ranking, variable selection, and screening procedures with false discovery rate (FDR) control. Numerical examples are then presented to demonstrate performance of the proposed methods.
Characteristics of Synoptic Conditions for Heavy Snowfall in Western to Northeastern Japan Analyzed by the 5-km Regional Climate Ensemble Experiments

Geographical distributions of heavy snowfall, especially in the Pacific Ocean side of Japan, have not been previously elucidated due to low occurrence frequency of heavy snowfall and limited number of snow observation points. This study investigates the characteristics of synoptic conditions for heavy daily snowfall from western to northeastern Japan in the present climate, analyzing high-resolution regional climate ensemble experiments with 5-km grid spacing. The Japanese 55-year Reanalysis (JRA-55) and the 10-ensemble members of the database for Policy Decision making for Future climate change (d4PDF) historical experiments are applied to the lateral boundary conditions of the regional climate model. Dynamical downscaling using d4PDF (d4PDF-DS) enabled us to evaluate much heavier snowfall events than those simulated by dynamical downscaling using JRA-55 (JRA55-DS). Over the Sea of Japan side, heavy snowfall occurs due to cold air outbreaks, while over the Pacific Ocean side, heavy snowfall is brought about by extratropical cyclones passing along the Pacific Ocean coast. A comparison Corresponding author: Hiroaki Kawase, Meteorological Research Institute, Japan Meteorological Agency, 1-1, Nagamine, Tsukuba, Ibaraki 305-0052, Japan E-mail: hkawase@mri-jma.go. jp J-stage Advance Published Date: 30 January 2018 ©The Author(s) 2018. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/license/by/4.0). Journal of the Meteorological Society of Japan Vol. 96, No. 2 162
Measurement of the Proton Recoil Spectrum in Neutron Beta Decay with the Spectrometer aSPECT: Study of Systematic Effects

Free neutron decay, n→ peνe, is the simplest nuclear beta decay, well described as a purely left-handed, vector minus axial-vector interaction within the framework of the Standard Model (SM) of elementary particles and fields. Due to its highly precise theoretical description, neutron beta decay data can be used to test certain extensions to the SM. Possible extensions require, e.g., new symmetry concepts like left-right symmetry, new particles, leptoquarks, supersymmetry, or the like. Precision measurements of observables in neutron beta decay address important open questions of particle physics and cosmology, and are generally complementary to direct searches for new physics beyond the SM in high-energy physics. In this doctoral thesis, a measurement of the proton recoil spectrum with the neutron decay spectrometer aSPECT is described. From the proton spectrum the antineutrinoelectron angular correlation coefficient a can be derived. In our first beam time at the Forschungs-Neutronenquelle Heinz Maier-Leibnitz in Munich, Germany (2005-2006), background instabilities due to particle trapping and the electronic noise level of the proton detector prevented us from presenting a new value for a. In the latest beam time at the Institut Laue-Langevin (ILL) in Grenoble, France (2007-2008), the trapped particle background has been reduced sufficiently and the electronic noise problem has essentially been solved. For the first time, a silicon drift detector was used. As a result of the data analysis, we identified and fixed a problem in the detector electronics which caused a significant systematic error. The target figure of the latest beam time was a new value for a with a total relative error well below the present literature value of 4%. A statistical accuracy of about 1.4% was reached, but we could only set upper limits on the correction of the problem in the detector electronics, which are too high to determine a meaningful result. The present doctoral thesis focused on the investigation of several different systematic effects. With the knowledge of the systematic effects gained in this thesis, we are now able to improve the aSPECT spectrometer to perform a 1% measurement of a in a further beam time at the ILL.
Meta-Learning with Adaptive Layerwise Metric and Subspace

Recent advances in meta-learning demonstrate that deep representations combined with the gradient descent method have sufficient capacity to approximate any learning algorithm. A promising approach is the model-agnostic meta-learning (MAML) which embeds gradient descent into the meta-learner. It optimizes for the initial parameters of the learner to warm-start the gradient descent updates, such that new tasks can be solved using a small number of examples. In this paper we elaborate the gradient-based meta-learning, developing two new schemes. First, we present a feedforward neural network, referred to as T-net, where the linear transformation between two adjacent layers is decomposed as T W such that W is learned by task-specific learners and the transformation T, which is shared across tasks, is meta-learned to speed up the convergence of gradient updates for task-specific learners. Second, we present MT-net where gradient updates in the T-net are guided by a binary mask M that is meta-learned, restricting the updates to be performed in a subspace. Empirical results demonstrate that our method is less sensitive to the choice of initial learning rates than existing meta-learning methods, and achieves the state-of-the-art or comparable performance on few-shot classification and regression tasks.
Unexpected side effects: A cautionary note on challenges of persistent self-transcendence

Self-transcendence is an ambiguous construct without consensual meaning, yet many claim that it relates to, or even causes, beneficial outcomes. Few discuss its potential deleterious side effects, choosing to focus primarily on positive effects. However, anything with sufficient potency to heal may have unintended side effects, especially when it leads beyond a transitory state to becoming an enduring trait, such as when self-transcendence (ST) becomes persistent self-transcendence (PST). With PST, evidence is overviewed here, along with two illustrative case reports, that people can suffer emotional difficulties, motivation changes, loss of self-reflexivity, anhedonia, dissociation, depersonalization, memory problems, and other psychological concerns. This is discussed in terms of the disruption of the sense of self, which ordinarily serves as an integrative center for the person that conveys a sense of agency. Possible neurobiological and sociocultural effects of PST are also discussed, with a focus on its role on narrative memory as the construction of self-concept. Evidence has also been accumulating on problematic side effects of meditation and mindfulness, techniques commonly presented as paths to PST. Given that PST is often presented as a pinnacle of human development and spiritual attainment, seekers of PST, psychologists, and other mental health professionals are urged to become informed about its possible side effects, and view this phenomenon in a more balanced way.
Causal Reasoning Across Agents and Objects

This work attempts to bridge the divide between accounts of causal reasoning with respect to agents and objects. We begin by examining the influence of animacy. In a collision-based context, we vary the animacy status of an object using 3D animations. By holding the fine-grained kinematics of the actual and counterfactual outcomes fixed across animate and inanimate conditions, we find that animacy itself has no effect on causal attribution judgments. Next, we test if causal judgments for animate and inanimate objects differ as a function of the counterfactuals they respectively afford in a disjunctive causal structure. Here, we find that the effect of perceived animacy on causal attribution is mediated by differences in counterfactual judgments. Finally, we introduce the known effect of prescriptive norm violations to this paradigm. Our results collectively highlight how normative expectations specify the counterfactual considerations that guide causal reasoning about both agents and objects.
On boundary value problem for equations with cubic nonlinearity and step-wise coefficient

The differential equation with cubic nonlinearity x′′ = −ax + bx3 is considered together with the boundary conditions x(−1) = x(1) = 0 . In the autonomous case, b = const > 0 , the exact number of solutions for the boundary value problem is given. For nonautonomous case, where b = β(t) is a step-wise function, the existence of additional solutions is detected. The reasons for such behaviour are revealed. The example considered in this paper is supplemented by a number of visualizations.
On-Orbit Validation of a Framework for Spacecraft-Initiated Communication Service Requests With NASA’s SCaN Testbed

—We design, analyze, and experimentally validate a framework for demand-based allocation of high-performance space communication service in which the user spacecraft itself initiates a request for service. Leveraging machine-to-machine communications, the automated process has potential to improve the responsiveness and efﬁciency of space network operations. We propose an augmented ground station architecture in which a hemispherical-pattern antenna allows for reception of service requests sent from any user spacecraft within view. A suite of ground-based automation software acts upon these direct-to-Earth requests and allocates access to high-performance service through a ground station or relay satellite in response to immediate user demand. A software-deﬁned radio transceiver, optimized for reception of weak signals from the helical antenna, is presented. Design and testing of signal processing equipment and a software framework to handle service requests is discussed. Preliminary results from on-orbit demonstrations with a testbed onboard the International Space Station are presented to verify feasibility of the concept.
Flow Measurements in a Short Takeoff, Vertical Landing Fountain: Splayed Jets

DOI: 10.2514/1.38296 The interaction of multiple jets with the ground is of great importance for the design and operation of short takeoff, vertical landing aircraft. The fountain upwash flow, generated by the impingement of two axisymmetric, compressible, turbulent jets onto a ground plane was studied using laser-based particle image velocimetry. Measurements were made with nozzle pressure ratios of between 1.05 and 4, nozzle height-to-diameter ratios of between 2.4 and 8.4, nozzle splay angles of between 15 deg, and a nozzle spacing-to-diameter ratio of seven. The effect of varying these parameters on the fountain velocity decay, spreading rate, and momentum flux ratio are discussed. In general, it was found that the inclusion of nozzle splay did not significantly alter the distribution of fountain momentum flux ratio relative to the equivalent parallel configuration.
A new accurate retrieval of bromine monoxide inside minor volcanic plumes from Sentinel-5 Precursor/TROPOMI

Bromine monoxide ( BrO ) is a key radical in the atmosphere, influencing the chemical state of the atmosphere, most notably the abundance of ozone. The main effect of BrO onto tropospheric ozone concentrations occurs in bromine release events in polar regions, salt pans and volcanic plumes. Ozone depletion caused by halogen release has been observed and modeled for such conditions, in particular inside volcanic plumes. Furthermore, the molar bromine to sulphur ratio in volcanic plumes is a proxy for the magmatic composition of a volcano and potentially an eruption forecast parameter. The integrated 5 column of BrO in the atmosphere, which in turn serves as an estimate for the bromine content, can be detected simultaneously with SO 2 via spectroscopic measurements using the Differential Optical Absorption Spectroscopy (DOAS). Thus, a direct derivation of the BrO / SO 2 ratio can be performed from a single measurement. Satellite spectroscopic observations offer the potential to observe and monitor volcanic bromine release globally. The detection of BrO in volcanic plumes is limited by the precision and sensitivity of the retrieval, which so far only allowed for 10 the detection of BrO during major eruptions, leading to a potential sampling bias when looking at the BrO / SO 2 ratio. The The TROPospheric Monitoring Instrument (TROPOMI
Spatial and temporal variability of mode-1 and mode-2 internal solitary waves from MODIS/TERRA sun glint off the Amazon shelf.

The Amazon shelf is a key region for intense internal tides (ITs) and nonlinear internal solitary waves (ISWs) generation associated with them. The region shows well-marked seasonal variability (boreal Summer/fall ASOND vs spring MAMJJ) of the circulation and stratiﬁcation, which can both induce changes in the ISWs physical characteristics. The description of the seasonal and neap-spring tidal variability of the ISWs off the Amazon shelf is performed for the
Performance above all ? energy consumption vs. performance for machine listening, a study on dcase task 4 baseline

In machine listening there is a tendency to resort to models with a growing number of parameters raising thus concerns about the practical viability of these due to their energy consumption. Reporting energy consumption of the models could be a first step to raise awareness on this matter. Yet, estimating the energy consumption across different conditions (hyper-parameters, GPU types etc.) poses some challenges in terms of biases and fairness of the comparison between different models and works. In this paper we perform an extensive study using the DCASE task 4 baseline system and monitor energy consumption and training time for different GPU types and batch sizes. The goal is to identify which aspects can have an impact on the estimation of the energy consumption and should be normalized for a fair comparison across systems. Additionally, we propose an analysis of the relationship be-tween the energy consumption and the sound event detection performance that calls into question our current way to evaluate systems.
A Proposed Approach for Predicting Liver Disease A Proposed Approach for Predicting Liver Disease

: One of the main challenges is to exploit recent technologies in a way that is able to preserve human life. Liver disease is one of the most influencing and largest organs of the human body, which has a great impact on human life, according to the massive number of deaths of this disease. So, it is important to predict liver disease with the maximum possible accuracy, as the current problem is the weak accuracy of predicting liver disease and not predicting the severity of the liver disease. Thus, through this paper, the aim behind our proposed work is to enhance the performance of predicting liver disease, predicting the severity of liver disease, and then building a recommender system that recommends the appropriate medical pieces of advice according to the patient's condition using machine learning algorithms and tools like a GridsearchCV tool. Indian liver patient's dataset (ILPD) and the hepatitis C virus (HCV) dataset are our training datasets. Hence, the proposed solution enhanced the prediction accuracy of liver disease by 80% and 77 % for extra tree and KNN algorithms when using ILPD datasets. And when using the HCV dataset, the accuracy is achieved by the Gradient boosting algorithm and Logistic Regression by 96% for predicting liver disease, disease severity, and patient recommendation system model.