Miracle-dz/UniFAR-FTUs huggingface dataset:
Periodic Event-Triggered Control for Linear Systems

Event-triggered control (ETC) is a control strategy that is especially suited for applications where communication resources are scarce. By updating and communicating sensor and actuator data only when needed for stability or performance purposes, ETC is capable of reducing the amount of communications, while still retaining a satisfactory closed-loop performance. In this paper, an ETC strategy is proposed by striking a balance between conventional periodic sampled-data control and ETC, leading to so-called periodic event-triggered control (PETC). In PETC, the event-triggering condition is verified periodically and at every sampling time it is decided whether or not to compute and to transmit new measurements and new control signals. The periodic character of the triggering conditions leads to various implementation benefits, including a minimum inter-event time of (at least) the sampling interval of the event-triggering condition. The PETC strategies developed in this paper apply to both static state-feedback and dynamical output-based controllers, as well as to both centralized and decentralized (periodic) event-triggering conditions. To analyze the stability and the L2-gain properties of the resulting PETC systems, three different approaches will be presented based on 1) impulsive systems, 2) piecewise linear systems, and 3) perturbed linear systems. Moreover, the advantages and disadvantages of each of the three approaches will be discussed and the developed theory will be illustrated using a numerical example.
Studies on the potentials of the Opeki River Dam for water supply and irrigation

Effective management of existing water reservoirs has been identified as profitable strategy to solving the challenge of providing adequate water for domestic and irrigation uses. The Opeki River Dam is a medium size dam with a maximum active storage capacity of 2.1million cubic metre (mcm) and a guaranteed maximum all year round active storage of 1.78mcm. This in addition to supplying the 2.72million litre/day (ml/day) raw water to the treatment plant can also sustain a cultivable command area (CCA) of between 200ha of maize or melon or 175ha of tomato or a feasible combination of the three (3) crops. At present, the treatment plant capacity is inadequate for the intended population and needs urgent expansion to meet the future demand of 11.6ml/day and 17.6ml/day by year 2010 and 2025 respectively. Whereas the water supply is primary, the secondary objective, which is irrigation within the catchment area, can be substantially achieved if effective management of the reservoir capacity is focussed upon. However, in order to cultivate 50% of the CCA of the Eruwa Farm Settlement of 2500ha, an expansion of the reservoir to provide 12.0mcm will be necessary. The quality of the raw water was also found to be acceptable for crop production based on the FAO (1985) standard on irrigation water. Keywords : Opeki River, dam, water supply, irrigation Moor Journal of Agricultural Research Vol. 5(1) 2004: 74-81
Facial Asymmetry in Healthy North American Caucasians

Abstract This is a study of normal children, evaluating the degree of subtle asymmetry that can be expected in all of us. Asymmetry was found to be very common, but average differences between right and left measurements were mild (3mm or 3%), with the right side usually the largest. The most common and largest asymmetries were found in the upper third of the face.
Urban land use/land cover mapping with high-resolution SAR imagery by integrating support vector machines into object-based analysis

This paper investigates the capability of high-resolution SAR data for urban landuse/land-cover mapping by integrating support vector machines (SVMs) into object-based analysis. Five-date RADARSAT fine-beam C-HH SAR images with a pixel spacing of 6.25 meter were acquired over the rural-urban fringe of the Great Toronto Area (GTA) during May to August in 2002. First, the SAR images were segmented using multi-resolution segmentation algorithm and two segmentation levels were created. Next, a range of spectral, shape and texture features were selected and calculated for all image objects on both levels. The objects on the lower level then inherited features of their super objects. In this way, the objects on the lower level received detailed descriptions about their neighbours and contexts. Finally, SVM classifiers were used to classify the image objects on the lower level based on the selected features. For training the SVM, sample image objects on the lower level were used. One-against-one approach was chosen to apply SVM to multiclass classification of SAR images in this research. The results show that the proposed method can achieve a high accuracy for the classification of high-resolution SAR images over urban areas.
Characteristics of mesospheric optical emissions produced by lightning discharges

A new two-dimensional cylindrically symmetric electromagnetic model of the lightning-ionosphere interaction includes effects of both the lightning radiated electromagnetic pulses (EMP) and the quasi-electrostatic (QE) fields, thus allowing effective studies of lightning-ionosphere interactions on time scales ranging from several microseconds to tens of milliseconds. The temporal and spatial evolution of the electric field, lower ionospheric electron density, and optical emissions calculated with the new model are used to investigate theoretically the effects of the lightning return stroke current waveform (i.e., the current rise and fall timescales) and of the observational geometry on the optical signals observed with a photometer. For typical lightning discharges of ∼100 μs duration the ionospheric response is dominated by the EMP-induced heating leading to the highly transient and laterally expanding optical flashes known as elves. The optical signal characteristics are found to be highly sensitive to both the observational geometry and the current waveform. The onset delay with respect to the lightning discharge, the duration, and the peak magnitude of optical emissions are highly dependent on the elevation and azimuth angles of field of view of individual photometric pixels. The shape of the optical signal clearly reflects the source current waveform. For a waveshape with risetime of ∼50 μs or longer a double-pulse shape of the photometric signal is observed. For cloud to ground lightning discharges of ∼1 ms duration removing substantial amount of charge (i.e., ∼100 C from 10 km altitude), heating and ionization changes induced by the QE field lead to the mesospheric luminous glows with lateral extent < 100 km, referred to as sprites.
Generative Adversarial Network Based Acoustic Scene Training Set Augmentation and Selection Using SVM Hyper-Plane

Although it is typically expected that using a large amount of labeled training data would lead to improve performance in deep learning, it is generally difficult to obtain such DataBase (DB). In competitions such as the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge Task 1, participants are constrained to use a relatively small DB as a rule, which is similar to the aforementioned issue. To improve Acoustic Scene Classification (ASC) performance without employing additional DB, this paper proposes to use Generative Adversarial Networks (GAN) based method for generating additional training DB. Since it is not clear whether every sample generated by GAN would have equal impact in classification performance, this paper proposes to use Support Vector Machine (SVM) hyper plane for each class as reference for selecting samples, which have class discriminative information. Based on the crossvalidated experiments on development DB, the usage of the generated features could improve ASC performance.
On the relationships between ENSO and autumn rainfall in Iran.

An investigation of the relationships between Iranian autumn rainfall and the El Nino–Southern Oscillation (ENSO) phenomenon was explored for the period 1951–1990. A negative correlation between the Troup Southern Oscillation Index (SOI) and rainfall data was found for almost all of Iran. The relationships were found to be stronger and more consistent over some regions comprising the southern foothills of the Alborz Mountains, northwestern districts and central areas. For the other parts of the country, correlations were found to be either less significant or non-stationary. It was determined that the associations between SOI and rainfall over central parts of Iran have persistently improved for the recent period studied. The impacts of ENSO on rainfall amounts during low and high phases of the SO index were also studied. It was found that during El Nino episodes, the amount of rainfall over various parts of the country was several times more than during La Nina periods. The associations between SOI and surface air pressure data were found to be poor and insignificant. The possibility of rainfall forecasting was also explored and the results suggest that autumn rainfall could be predicted a season ahead for some parts of the country. A mechanism for the influence of the ENSO cycle on Iranian rainfall is suggested. Copyright © 2000 Royal Meteorological Society
An exploratory study of diversity among computer science graduate students at top North American universities

Although diﬀerent organizations have deﬁned policies toward diversity in academia, many argue that minorities are still disadvantaged in being admitted to universities due to biases. In this study, we collected the information of 13,936 graduate students majoring in computer science at the top 25 North American universities and used statistical hypothesis tests to realize whether there is a preference for students’ gender and nationality in the admission processes. In addition to partiality patterns, we discuss the relations between gender/nationality diversity and the scientiﬁc achievements of research teams. Our ﬁndings show that there is no gender bias in graduate students’ admission to research groups, but we observed partiality in students’ nationality.
Option Valuation with Conditional Skewness

There is extensive empirical evidence that index option prices systematically differ from Black-Scholes prices. Out-of-the-money put prices (and in-the-money call prices) are relatively high compared to the Black-Scholes price. Motivated by these empirical facts, we develop a new discrete-time dynamic model of stock returns with Inverse Gaussian innovations. The model allows for conditional skewness as well as conditional heteroskedasticity and a leverage effect. We present an analytic option pricing formula consistent with this stock return dynamic. An extensive empirical test of the model using S&P500 index options shows that the new Inverse Gaussian GARCH model's performance is superior to a standard existing nested model for out-of-the money puts, thus demonstrating the importance of conditional skewness. The discrete-time Inverse Gaussian GARCH process has two interesting continuous-time limits. One limit is the standard stochastic volatility model of Heston (1993). The other is a pure jump process with stochastic intensity. Using these limit results, an equivalent motivation for our model is that it generalizes standard stochastic volatility models by allowing for "jumps" and other fat-tailed negative movements in stock returns. The empirical results therefore also demonstrate the importance of jumps for the pricing of out-of-the-money puts.
Randomized Conditional Flow Matching for Video Prediction

We introduce a novel generative model for video prediction based on latent ﬂow matching, an efﬁcient alternative to diffusion-based models. In contrast to prior work that either incurs a high training cost by modeling the past through a memory state, as in recurrent neural networks, or limits the computational load by conditioning only on a predeﬁned window of past frames, we efﬁciently and effectively take the past into account by conditioning at inference time only on a small random set of past frames at each integration step of the learned ﬂow. Moreover, to enable the generation of high-resolution videos and speed up the training, we work in the latent space of a pretrained VQGAN. Furthermore, we propose to approximate the initial condition of the ﬂow ODE with the previous noisy frame. This allows to reduce the number of integration steps and hence, speed up the sampling at inference time. We call our model Random frame conditional ﬂow Integration for VidEo pRe-diction, or, in short, RIVER. We show that RIVER achieves superior or on par performance compared to prior work on common video prediction benchmarks.
Whole-mantle radially anisotropic shear velocity structure from spectral-element waveform tomography

S U M M A R Y The radially anisotropic shear velocity structure of the Earth’s mantle provides a critical window on the interior dynamics of the planet, with isotropic variations that are interpreted in terms of thermal and compositional heterogeneity and anisotropy in terms of flow. While significant progress has been made in the more than 30 yr since the advent of global seismic tomography, many open questions remain regarding the dual roles of temperature and composition in shaping mantle convection, as well as interactions between different dominant scales of convective phenomena. We believe that advanced seismic imaging techniques, such as waveform inversion using accurate numerical simulations of the seismic wavefield, represent a clear path forwards towards addressing these open questions through application to whole-mantle imaging. To this end, we employ a ‘hybrid’ waveform-inversion approach, which combines the accuracy and generality of the spectral finite element method (SEM) for forward modelling of the global wavefield, with non-linear asymptotic coupling theory for efficient inverse modelling. The resulting whole-mantle model (SEMUCB-WM1) builds on the earlier successful application of these techniques for global modelling at upper mantle and transition-zone depths (≤800 km) which delivered the models SEMum and SEMum2. Indeed, SEMUCB-WM1 is the first whole-mantle model derived from fully numerical SEM-based forward modelling. Here, we detail the technical aspects of the development of our wholemantle model, as well as provide a broad discussion of isotropic and radially anisotropic model structure. We also include an extensive discussion of model uncertainties, specifically focused on assessing our results at transition-zone and lower-mantle depths.
Sensitivity of the equilibrium surface temperature of a GCM to systematic changes in atmospheric carbon dioxide

The equilibrium response of surface temperature to atmospheric CO{sub 2} concentration, for six values between 100 and 1,000 ppm, is calculated from a series of general circulation model experiments. This response is nonlinear, showing greater sensitivity for lower values of CO{sub 2} than for the higher values. It is suggested that changes in CO{sub 2} concentration of a given magnitude (e.g., 100 ppm) play a larger role in the Pleistocene ice age type temperature variations, than in causing global temperature changes due to anthropogenic increases.
Pachner moves, generic complexity, and randomising 3-manifold triangulations

We study the computational complexity of decision problems on triangulated 3-manifolds. In this setting there has been encouraging initial progress in recent years, but many important questions remain wide open. The “simple” problem of 3-sphere recognition and the related problem of unknot recognition are both known to be in NP, by work of Schleimer [18] and earlier work of Hass, Lagarias and Pippenger [9] respectively. In recent announcements by Kuperberg [13] and Hass and Kuperberg [8], these problems are also in co-NP if the generalised Riemann hypothesis holds. It remains a major open question as to whether either problem can be solved in polynomial time. There are very few hardness results for such problems. A notable example due to Agol, Hass and Thurston is knot genus: if we generalise unknot recognition to computing knot genus, and we generalise the ambient space from S to an arbitrary 3-manifold, then the problem becomes NP-complete [1]. The key construction in their result can also be adapted for problems relating to least-area surfaces [1, 7].
Improving selective classification performance of deep neural networks through post-hoc logit normalization and temperature scaling

This paper addresses the problem of selective classification for deep neural networks, where a model is allowed to abstain from low-confidence predictions to avoid potential errors. Specifically, we tackle the problem of optimizing the confidence estimator of a fixed classifier, aiming to enhance its misclassification detection performance, i.e., its ability to discriminate between correct and incorrect predictions by assigning higher confidence values to the correct ones. Previous work has found that different classifiers exhibit varying levels of misclassification detection performance, particularly when using the maximum softmax probability (MSP) as a measure of confidence. However, we argue that these findings are mainly due to a sub-optimal confidence estimator being used for each model. To overcome this issue, we propose a simple and efficient post-hoc confidence estimator, named p -NormSoftmax, which consists of transforming the logits through p -norm normalization and temperature scaling, followed by taking the MSP, where p and the temperature are optimized based on a hold-out set. This estimator can be easily applied on top of an already trained model and, in many cases, can significantly improve its selective classification performance. When applied to 84 pretrained Imagenet classifiers, our method yields an average improvement of 16% in the area under the risk-coverage curve (AURC), exceeding 40% for some models. Furthermore, after applying p -NormSoftmax, we observe that these models exhibit approximately the same level of misclassification detection performance, implying that a model’s selective classification performance is almost entirely determined by its accuracy at full coverage.
Near-Optimal Fully First-Order Algorithms for Finding Stationary Points in Bilevel Optimization

Bilevel optimization has various applications such as hyper-parameter optimization and meta-learning. Designing theoretically eﬃcient algorithms for bilevel optimization is more challenging than standard optimization because the lower-level problem deﬁnes the feasibility set implicitly via another optimization problem. One tractable case is when the lower-level problem permits strong convexity. Recent works show that second-order methods can provably converge to an ǫ -ﬁrst-order stationary point of the problem at a rate of ˜ O ( ǫ − 2 ) , yet these algorithms require a Hessian-vector product oracle. Kwon et al. [50] resolved the problem by proposing a ﬁrst-order method that can achieve the same goal at a slower rate of ˜ O ( ǫ − 3 ) . In this work, we provide an improved analysis demonstrating that the ﬁrst-order method can also ﬁnd an ǫ -ﬁrst-order stationary point within ˜ O ( ǫ − 2 ) oracle complexity, which matches the upper bounds for second-order methods in the dependency on ǫ . Our analysis further leads to simple ﬁrst-order algorithms that can achieve similar near-optimal rates in ﬁnding second-order stationary points and in distributed bilevel problems.
Soil structure development and preferential solute flow

Soil structure and preferential flow are thought to be highly related, but quantification of this relationship has remained problematic. A combination of experiments on two soils at local and field scale was used to study the relationships between the degree of structural development, saturated conductivities Ks, moisture release, breakthrough parameters, and observations of preferential flow at the field scale. The convection dispersion equation and mobile‐immobile model were fitted to breakthrough curves, and flow channels were stained with dye. Well‐developed structure was associated with high dispersivities, a high coefficient of variation of Ks, and low mobile water contents, low exchange coefficients, and a low dyed area. Field‐scale observations matched the differences in structure and preferential flow parameters observed at the local scales. Local‐scale preferential flow appeared to highly influence field‐scale solute transport, indicating that this is an important local‐scale process which is not dampened at the field scale. A conceptual model of subsoil structural development was expanded with physical parameters matching the structural development stage to predict the likelihood of preferential flow on the basis of the saturation regime of the horizon or pedon.
Molecular Machines in the Synapse: Overlapping Protein Sets Control Distinct Steps in Neurosecretion

Activity regulated neurotransmission shapes the computational properties of a neuron and involves the concerted action of many proteins. Classical, intuitive working models often assign specific proteins to specific steps in such complex cellular processes, whereas modern systems theories emphasize more integrated functions of proteins. To test how often synaptic proteins participate in multiple steps in neurotransmission we present a novel probabilistic method to analyze complex functional data from genetic perturbation studies on neuronal secretion. Our method uses a mixture of probabilistic principal component analyzers to cluster genetic perturbations on two distinct steps in synaptic secretion, vesicle priming and fusion, and accounts for the poor standardization between different studies. Clustering data from 121 perturbations revealed that different perturbations of a given protein are often assigned to different steps in the release process. Furthermore, vesicle priming and fusion are inversely correlated for most of those perturbations where a specific protein domain was mutated to create a gain-of-function variant. Finally, two different modes of vesicle release, spontaneous and action potential evoked release, were affected similarly by most perturbations. This data suggests that the presynaptic protein network has evolved as a highly integrated supramolecular machine, which is responsible for both spontaneous and activity induced release, with a group of core proteins using different domains to act on multiple steps in the release process.
Temporal and spatial variations in CH4 concentrations in a Japanese warm-temperate mixed forest

To clarify the characteristics of the variation in methane (CH4) concentrations in a warm-temperate mixed forest in Japan, we measured vertical profiles and daily and seasonal variations in CH4 concentrations, and soil and foliage (Quercus serrata) CH4 fluxes. CH4 concentrations increased during rainy periods, and decreased in August. Almost constant CH4 uptake was observed at the forest floor throughout the year, but no CH4 flux was observed from the foliage. In the profile measurements, the CH4 concentration was slightly higher at canopy height when friction velocity (u*) was lower than 0.3 m s-1. These phenomena were observed on the same day at micrometeorological towers in ridge and valley positions. Variations in CH4 concentrations increased in summer, around ground level, and at canopy height. These results suggest that certain large scale factors govern variations in CH4 concentration around ground level and at canopy height. To understand the overall role of the forest in CH4 circulation, more continuous measurements as close as possible to sinks and sources will be needed.