Miracle-dz/UniFAR-FTUs huggingface dataset:
An Uncertainty Measure for Prediction of Non-Gaussian Process Surrogates

Abstract Model management is an essential component in data-driven surrogate-assisted evolutionary optimization. In model management, the solutions with a large degree of uncertainty in approximation play an important role. They can strengthen the exploration ability of algorithms and improve the accuracy of surrogates. However, there is no theoretical method to measure the uncertainty of prediction of Non-Gaussian process surrogates. To address this issue, this article proposes a method to measure the uncertainty. In this method, a stationary random field with a known zero mean is used to measure the uncertainty of prediction of Non-Gaussian process surrogates. Based on experimental analyses, this method is able to measure the uncertainty of prediction of Non-Gaussian process surrogates. The method's effectiveness is demonstrated on a set of benchmark problems in single surrogate and ensemble surrogates cases.
Multivariable closed-loop identification: from indirect identification to dual-Youla parametrization

Classical indirect methods of closed-loop identification can be applied on the basis of different closed-loop transfer functions. Here the multivariable situation is considered and conditions are formulated under which identified approximative plant models are guaranteed to be stabilized by the present controller. Additionally it is shown in which sense the classical indirect methods are generalized by a previously introduced identification method based on the dual-Youla parametrization. For stable controllers the two methods are shown to be basically equivalent to each other.
Analysis of the planetary boundary layer with a database of large-eddy simulation experiments

Observational studies of a planetary boundary layer (PBL) are difficult. Ground-born measurements usually characterize only a small portion of the PBL immediately above the surface. Air-born measurements cannot be obtained close to the surface and therefore cannot capture any significant portion of the PBL interior. Moreover, observations are limited in choice of instrumentation, time, duration, location of measurements and occasional weather conditions. Although turbulence-resolving simulations with a large-eddy simulation (LES) code do not supplant observational studies, they provide valuable complementary information on different aspect of the PBL dynamics, which otherwise difficult to acquire. These circumstances motivated development of a medium-resolution database (DATABASE64) of turbulence-resolving simulations, which is available on this ftp URL DATABASE64 covers a range of physical parameters typical for the barotropic SBL over a homogeneous rough surface. LES runs in DATABASE64 simulate 16 hours' evolution of the PBL turbulence. They are utilized to study both transition and equilibrium SBL cases as well as to calibrate turbulence parameterizations of meteorological models. The data can be also used to falsify theoretical constructions with regards to the PBL.
Locally affine geometries of order 2 where shrinkings are affine expansions

Given a locally a affine geometry Γ of order 2 and a flag-transitive subgroup , suppose that the shrinkings of Γ are isomorphic to the a affine expansion of the upper residue of a line of Γ by a homogeneous representation in a 2-group. We shall prove that, under certain hypotheses on the stabilizers G p and G l of a point p and a line l , we have G=R{G p } for a representation group R of . We also show how to apply this result in the classification of flag-transitive c -extended P- and T-geometries.
Can a student Large Language Model perform as well as it's teacher?

The burgeoning complexity of contemporary deep learning models, while achieving unparalleled accuracy, has inadvertently introduced deployment challenges in resource-constrained environments. Knowledge distillation, a technique aiming to transfer knowledge from a high-capacity"teacher"model to a streamlined"student"model, emerges as a promising solution to this dilemma. This paper provides a comprehensive overview of the knowledge distillation paradigm, emphasizing its foundational principles such as the utility of soft labels and the significance of temperature scaling. Through meticulous examination, we elucidate the critical determinants of successful distillation, including the architecture of the student model, the caliber of the teacher, and the delicate balance of hyperparameters. While acknowledging its profound advantages, we also delve into the complexities and challenges inherent in the process. Our exploration underscores knowledge distillation's potential as a pivotal technique in optimizing the trade-off between model performance and deployment efficiency.
Approximate Design of Optimal Disturbance Rejection for Discrete-Time Systems with Multiple Delayed Inputs: Application to a Jacket-Type Offshore Structure

The study is concerned with problem of optimal disturbance rejection for a class of discrete-time systems with multiple delayed inputs. In order to avoid the two-point boundary value (TPBV) problem with items of time-delay and time-advance caused by multiple delayed inputs, the discrete-time system with multiple delayed inputs is transformed into a delay-free system by introducing a variable transformation, and the original performance index is reformulated as a corresponding form without the explicit appearance of time-delay items. Then, the approximate optimal disturbance rejection controller (AODRC) is derived from Riccati equation and Stein equation based on the reduced system and reformulated performance index, which is combined with feedback item of system state, feedforward item of disturbances, and items of delayed inputs. Also, the existence and uniqueness of AODRC are proved, and the stability of the closed-loop system is analysed. Finally, numerical examples of disturbance rejection for jacket-type offshore structure and pure mathematical model are illustrated to validate the feasibility and effectiveness of the proposed approach.
PERIODIC VARIABILITY OF LOW-MASS STARS IN SLOAN DIGITAL SKY SURVEY STRIPE 82

We present a catalog of periodic stellar variability in the “Stripe 82” region of the Sloan Digital Sky Survey. After aggregating and re-calibrating catalog-level data from the survey, we ran a period-finding algorithm (Supersmoother) on all point-source light curves. We used color selection to identify systems that are likely to contain low-mass stars, in particular M dwarfs and white dwarfs. In total, we found 207 candidates, the vast majority of which appear to be in eclipsing binary systems. The catalog described in this paper includes 42 candidate M dwarf/white dwarf pairs, four white dwarf pairs, 59 systems whose colors indicate they are composed of two M dwarfs and whose light-curve shapes suggest they are in detached eclipsing binaries, and 28 M dwarf systems whose light-curve shapes suggest they are in contact binaries. We find no detached systems with periods longer than 3 days, thus the majority of our sources are likely to have experienced orbital spin-up and enhanced magnetic activity. Indeed, 26 of 27 M dwarf systems that we have spectra for show signs of chromospheric magnetic activity, far higher than the 24% seen in field stars of the same spectral type. We also find binaries composed of stars that bracket the expected boundary between partially and fully convective interiors, which will allow the measurement of the stellar mass–radius relationship across this transition. The majority of our contact systems have short orbital periods, with small variance (0.02 days) in the sample near the observed cutoff of 0.22 days. The accumulation of these stars at short orbital period suggests that the process of angular momentum loss, leading to period evolution, becomes less efficient at short periods. These short-period systems are in a novel regime for studying the effects of orbital spin-up and enhanced magnetic activity, which are thought to be the source of discrepancies between mass–radius predictions and measurements of these properties in eclipsing binaries.
Spectral cross-calibration of the konus-wind, the suzaku/WAM, and the swift/BAT data using gamma-ray bursts

We report on the spectral cross-calibration results of the Konus-Wind, the Suzaku/WAM, and the Swift/BAT instruments using simultaneously observed gamma-ray bursts (GRBs). This is the first attempt to use simultaneously observed GRBs as a spectral calibration source to understand systematic problems among the instruments. Based on these joint spectral fits, we find that 1) although a constant factor (a normalization factor) agrees within 20% among the instruments, the BAT constant factor shows a systematically smaller value by 10-20% compared to that of Konus-Wind, 2) there is a systematic trend that the low-energy photon index becomes steeper by 0.1-0.2 and Epeak becomes systematically higher by 10-20% when including the BAT data in the joint fits, and 3) the high-energy photon index agrees within 0.2 among the instruments. Our results show that cross-calibration based on joint spectral analysis is an important step to understanding the instrumental effects which could be affecting the scientific results from the GRB prompt emission data.
WEAK GRAVITATIONAL LENSING OF THE COSMIC MICROWAVE BACKGROUND REVISITED

In this paper, we revisit the weak gravitational lensing of the cosmic microwave background (CMB). The key point is that the deflection angle of CMB photons varies with the redshift. From this we obtain a set of modified power spectra. Compared with those calculated previously, the temperature, the polarization E mode, and the cross power spectra correction are of the order 0.01%, while the polarization B mode correction is about 0.4% (to l = 2000).
The compact radio structure of the high-redshift blazar J1430+4204 before and after a major outburst

The high-redshift (z = 4.72) blazar J1430+4204 produced an exceptional radio outburst in 2006. We analyzed 15-GHz radio interferometric images obtained with the Very Long Baseline Array (VLBA) before and after the outburst, to search for possible structural changes on milli-arcsecond angular scales and to determine physical parameters of the source.
Caustics in turbulent aerosols: an excitable system approach

Abstract Aerosol particles are inertial particles. They cannot follow the surrounding fluid in a region of high vorticity. These encounters render the particle velocity field ${{\boldsymbol {v}}}$ locally compressible. Caustics occur if the trace of the gradient matrix $\textsf{$\boldsymbol{\sigma}$}$ of the field ${{\boldsymbol {v}}}$ locally diverges. For three-dimensional isotropic and homogeneous turbulence, the dynamics of the gradient matrix can be expressed in terms of three geometric invariants. In the present paper we establish a parametrisation of this problem where the dynamics takes the form of an excitable stochastic dynamical system with a three-dimensional phase space. The deterministic part of the dynamics is solved analytically. We show that the deterministic system has a globally attractive stable fixed point. Small noise induces excursions from the fixed point that typically relax straight back towards the fixed point. Caustics emerge as non-trivial return to a global fixed point when noise excites a trajectory across a stability threshold. The relaxation to the global fixed point will then involve at least one, and it may involve two or even three divergences of $\boldsymbol {Tr}\,\textsf{$\boldsymbol{\sigma}$}$. Based on a combination of analytical insights and numerical analysis, we determine the rate of occurrence, duration and relative observation probability of caustic events in turbulent aerosols. Our analysis reveals that each approach towards a divergence proceeds along a straight line in the phase space of the dynamical system, which can help identify caustics. Moreover, there are infinite ways in which caustics can arise, namely whenever $\boldsymbol {Tr}\,\textsf{$\boldsymbol{\sigma}$}$ tends to $-\infty$, so that no two caustics look the same.
Deterministic annealing, constrained clustering, and optimization

In previous work the authors (Phys. Rev. Let., vol.65, p.945-8, 1990) proposed the concept of deterministic annealing for the problem of clustering and vector quantization. This approach is summarized. The authors extend the clustering method to the constraint clustering method. Adding constraints to the deterministic annealing mechanism expands the variety of optimization problems which can be solved by this method. A brief presentation of the clustering approach is given. Two examples to which the constraint clustering approach can be applied are included. <<ETX>>
The study of ground-level ozone in Kiev and its impact on public health

Ground-level ozone in Kiev for an episode of its high concentration in August 2000 was simulated with the model of the urban air pollution UAM-V (Urban Airshed Model). The study of total ozone over Kiev and its concentration changes with height in the troposphere is made on the basis of ground-based observations with the infrared Fourier spectrometer at the Main Astronomical Observatory of National Academy of Sciences of Ukraine as a part of the ESA-NIVR-KNMI no 2907. In 2008 the satellite Aura-OMI data OMO3PR on the atmosphere ozone profiles became available. Beginning in 2005, these data include the ozone concentration in the lower layer of the atmosphere and can be used for the evaluation of the ground-level ozone concentrations in all cities of Ukraine. Some statistical investigation of ozone air pollution in Kiev and medical statistics data on respiratory system was carried out with the application of the "Statistica" package. The regression analysis, prognostic regression simulation, and retrospective prognosis of the epidemiological situation with respect to respiratory system pathologies in Kiev during 2000-2007 were performed.
Equal or Not? An Exploration of Eighth-Grade Students' Experience of Algebra

thought processes, this transition “varies significantly across individuals as well as across and within content areas” (Caskey & Anfara, 2007, p. 2). As they develop, adolescents become less restricted to concrete ways of thinking and are able to reason about what is possible. They are increasingly able to reason deductively as well as inductively and to think in hypothetical terms. Adolescents also develop greater capacity with
ECOCAPTURE@HOME: Protocol for the Remote Assessment of Apathy and Its Everyday-Life Consequences

Apathy, a common neuropsychiatric symptom associated with dementia, has a strong impact on patients’ and caregivers’ quality of life. However, it is still poorly understood and hard to define. The main objective of the ECOCAPTURE programme is to define a behavioural signature of apathy using an ecological approach. Within this program, ECOCAPTURE@HOME is an observational study which aims to validate a method based on new technologies for the remote monitoring of apathy in real life. For this study, we plan to recruit 60 couples: 20 patient-caregiver dyads in which patients suffer from behavioral variant Fronto-Temporal Dementia, 20 patient-caregiver dyads in which patients suffer from Alzheimer Disease and 20 healthy control couples. These dyads will be followed for 28 consecutive days via multi-sensor bracelets collecting passive data (acceleration, electrodermal activity, blood volume pulse). Active data will also be collected by questionnaires on a smartphone application. Using a pool of metrics extracted from these passive and active data, we will validate a measurement model for three behavioural markers of apathy (i.e., daytime activity, quality of sleep, and emotional arousal). The final purpose is to facilitate the follow-up and precise diagnosis of apathy, towards a personalised treatment of this condition within everyday life.
OUTFLOWS AND MASSIVE STARS IN THE PROTOCLUSTER IRAS 05358+3543

We present new near-IR H2, CO J = 2–1, and CO J = 3–2 observations to study outflows in the massive star-forming region IRAS 05358+3543. The Canada–France–Hawaii Telescope H2 images and James Clerk Maxwell Telescope CO data cubes of the IRAS 05358 region reveal several new outflows, most of which emerge from the dense cluster of submillimeter cores associated with the Sh 2-233IR NE cluster to the northeast of IRAS 05358. We used Apache Point Observatory JHK spectra to determine line-of-sight velocities of the outflowing material. Analysis of archival Very Large Array cm continuum data and previously published very long baseline interferometry observations reveal a massive star binary as a probable source of one or two of the outflows. We have identified probable sources for six outflows and candidate counterflows for seven out of a total of 11 seen to be originating from the IRAS 05358 clusters. We classify the clumps within Sh 2-233IR NE as an early protocluster and Sh 2-233IR SW as a young cluster, and conclude that the outflow energy injection rate approximately matches the turbulent decay rate in Sh 2-233IR NE.
An Efficient Quantum Algorithm and Circuit to Generate Eigenstates Of SU(2) and SU(3) Representations

This thesis presents an efficient quantum algorithm and explicit circuits for generating eigenstates of arbitrary SU(2) and SU(3) representations. These include a wide variety of highly entangled states. The algorithm uses Schur transform that rotates the input computational basis states to the output Schur basis states with resources polynomial in number of qudits n. Using the fact that quantum logic is reversible, we accomplish the desired result using the inverse Schur transform. The algorithm can be easily generalized to any arbitrary higher groups.
HIV and suicide risk across adolescence and young adulthood: an examination of socio‐demographic, contextual and psychosocial risk factors for attempted suicide in a longitudinal cohort of ageing adolescents affected by HIV living in the New York City Area

As children become adolescents and young adults (AYA), their risk for attempting suicide increases dramatically, with chronic health conditions an important risk factor. This study examined correlates of suicidality across development in AYA living with perinatally acquired HIV (AYALPHIV) and those perinatally HIV‐exposed but uninfected (AYAPHEU).
Prevalence and factors associated with mild depressive and anxiety symptoms in older adults living with HIV from the Kenyan coast

Empirical research on the burden and determinants of common mental disorders (CMDs), especially depression and anxiety, among older adults living with HIV (OALWH) in sub‐Saharan Africa is inadequate. To bridge the gap in Kenya we: (1) determined the prevalence of CMDs among OALWH on routine HIV care compared to HIV‐negative peers; (2) investigated HIV status as an independent predictor of CMDs in older adults; and (3) investigated CMD determinants.
Towards an Argument Knowledge Base for Autonomous Debating Systems

This paper addresses the labour-intensive nature and expert annotator requirements of manual argument annotation by providing an initial sentence-level pipeline for argument identification and sentence relationship prediction. Before establishing our approach, we review current efforts to produce combined techniques and the available abstract pipelines. Next, we use the methods indicated in this review to produce standalone identification and relationship prediction methods. Finally, we apply these methods in a pipeline to produce a combined approach. The presented results demonstrate effective sentence identification but shortcomings in relationship detection. Overall, this paper suggests that combined approaches to argumentation are a promising area requiring further research.
Probability Estimation of Snow Damage on Sugi (Cryptomeria japonica) Forest Stands by Logistic Regression Model in Toyama Prefecture, Japan

In this paper, we apply a logistic regression model to the data of snow damage on sugi (Cryptomeria japonica) occurred in Toyama prefecture (in Japan) in 2004 for estimating the risk probability. In order to specify the factors effecting snow damage, we apply a model selection procedure determining optimal subset of explanatory variables. In this process we consider the following 3 information criteria, 1) Akaike's information criterion, 2) Baysian information criterion, 3) Bias-corrected Akaike's information criterion. For the selected variables, we give a proper interpretation from the viewpoint of natural disaster.
SCALA-Speech: An Interactive System for Finding and Analyzing Speech Content in Audio Data

: Audio data does not contain as much static information as images and texts and thus analyses inherently require more time. Although in monitoring applications it is likely that large quantities of the captured audio files do not contain meaningful information, without prior knowledge investigators need to listen to all audio files in full length. In this work, a system for automatically finding and analyzing speech content in audio data is presented. The system provides different speech processing algorithms as well as a graphical interface (SCALA) for assisting investigators in audio analysis. The system consists of four components: speech detection, language recognition, speaker diarization/recognition and keyword spotting. SCALA-Speech structures audio data by recognizing speech regions, used languages and speaker changes, thus enabling investigators to listen to audio data more efficiently. Furthermore, specific speakers and keywords can be annotated and searched for. Usage of SCALA-Speech is demonstrated on audio tracks of videos linked in Twitter posts related to an exemplary topic.
A Loosely-coupled Neural-symbolic approach to Compliance of Electric Panels

This paper presents an ongoing work on project MAP4ID “Multipurpose Analytics Platform 4 Industrial Data”, where one of the objectives is to propose suitable combinations of machine learning and Answer Set Programming (ASP) to cope with industrial problems. In particular, we focus on a specific use case of the project, where we combine deep learning and ASP to solve a problem of compliance to blueprints of electric panels. The use case data was provided by Elettrocablaggi srl, a SME leader in the market. Our proposed solution couples an object-recognition layer, implemented resorting to deep neural networks, that identifies components in an image of an electric panel, and sends this information to a a logic program, that checks the compliance of the panel in the picture with the blueprint of the circuit.
GPU parallelism for SAT solving heuristics

Modern SAT solvers employ a number of smart techniques and strategies to achieve maximum efficiency in solving the Boolean Satisfiability problem. Among all components of a solver, the branching heuristics plays a crucial role in affecting the performance of the entire solver. Traditionally, the main branching heuristics that have appeared in the literature have been classified as look-back heuristics or look-ahead heuristics. As SAT technology has evolved, the former have become more and more preferable, for their demand for less computational effort. Graphics Processor Units (GPUs) are massively parallel devices that have spread enormously over the past few decades and offer great computing power at a relatively low cost. We describe how to exploit such computational power to efficiently implement look-ahead heuristics. Our aim is to “rehabilitate” these heuristics, by showing their effectiveness in the contest of a parallel SAT solver.