Miracle-dz/UniFAR-FTUs huggingface dataset:
A MIX-decision CNN solution for real-time semantic segmentation on embedded devices

Semantic segmentation (SS) is a critical computer vision task that involves labeling each pixel in an image with a corresponding semantic class. It has numerous applications, such as autonomous driving, augmented reality, and video surveillance, which require real-time processing. However, many existing SS algorithms prioritize accuracy over computational efficiency, often resulting in complex models with high memory and computational requirements and future leading to challenging applications with critical real-time performance and limited computational resources. The paper proposes a novel CNN architecture incorporating binary neural network techniques to improve efficiency without sacrificing accuracy. The proposed MIX-Decision CNN architecture combines accuracy and efficiency and is optimized for real-time performance on embedded platforms. The paper uses the KITTI dataset to benchmark the proposed architecture on three embedded platforms: Raspberry Pi 3, Raspberry Pi 4, and NVIDIA Jetson Nano 4 GB. The paper's main contributions are two-fold: (i) the novel MIX-Decision CNN architecture offers an efficient and accurate solution for real-time SS tasks using binary neural network techniques, and (ii) the paper demonstrates the feasibility of the proposed architecture by implementing and benchmarking it on various embedded platforms. The results show promising performance for real-world scenarios where real-time processing is crucial.
The Value of Mental Health Screening for Adolescents Attending School-Based Health Centers

Although 65% of school-based health centers (SBHCs) offer mental health services, at-risk youth are commonly overlooked or do not follow up when referrals are made. Universal screenings may increase identification of those in need of mental health support. We examined the number of youth at three SBHCs referred for mental health consultations. Comparisons were made between provider referrals and those with elevated scores on the Youth Pediatric Symptom Checklist-17 (Y-PSC-17). Of the 585 patients in this study, 37 (6.32%) were referred solely by their Y-PSC-17 score, 36 (6.15%) youth were singularly referred by providers, and 19 (3.25%) were referred by both modalities. Almost three-quarters (n = 67; 72.8%) of identified youth elected to receive psychotherapeutic services. Approximately half of the youth referred by providers (n = 19; 52.8%) received individual services, while 36.11% (n = 13) received group services. Smaller proportions of youth with elevated Y-PSC-17 scores received individual (n = 7; 18.9%) and group services (n = 11; 29.7%). Youth referred solely by a medical provider had significantly lower scores than those identified through an elevated Y-PSC-17 score. Findings highlight the necessity of universal screening in pediatric settings in addition to physician referrals to provide a supplemental layer of prevention and early identification of behavioral health concerns. In this study, 40% referrals would have been missed if both modalities were not in place. Educating medical providers on youth mental health concerns may enhance identification of those in need of mental health supports and in turn improve long-term outcomes.
STRATEGI SEKOLAH DALAM PENERAPAN NILAI RELIGIUS DAN TANGGUNG JAWAB DI MAN SIDOARJO

Penerapan nilai karakter sangat penting bagi generasi penerus bangsa karena nilai menjadi suatu hal yang dijunjung tinggi dalam kehidupan sehari-hari dan nilai karakter dapat mempengaruhi tindakan seseorang. Permasalahan dalam penelitian ini adalah bagaimana strategi sekolah dalam penerapan nilai religius dan tanggung jawab. Penelitian ini bertujuan untuk mendeskripsikan strategi sekolah dalam penerapan nilai religius dan tanggung jawab di MAN Sidoarjo, mendeskripsikan daya dukung yang dimiliki oleh sekolah dalam membentuk karakter religius dan tanggung jawab. Teori yang digunakan dalam penelitian ini yaitu teori belajar Albert Bandura (1997) yang terdiri atas fase attention, retension, reproduction dan motivasion. Metode penelitian ini menggunakan pendekatan kualitatif dengan desain deskriptif. Analisis data dalam penelitian ini menggunakan model Miles dan Huberman yaitu pengumpulan data, reduksi data, penyajian data dan penarikan kesimpulan. Hasil penelitian ini menunjukkan bahwasannya penerapan nilai religius dan tanggung jawab di MAN Sidoarjo menggunakan teknik modeling yang diterapkan dalam program sekolah dan program pembinaan. Program sekolah meliputi program BTQ, tahfidz qur’an dan boarding school. Tujuan penerapan nilai religius di MAN Sidoarjo sebagai bentuk pendidikan karakter yang diupayakan oleh sekolah agar peserta didik dapat membentengi diri dengan ilmu agama, karena semakin hari jaman akan semakin berkembang sehingga segala informasi dan budaya dapat masuk di Indonesia secara bebas, sedangkan nilai tanggung jawab diterapkan untuk meminimalisir pelanggaran yang dilakukan oleh peserta didik.
Robust Optimization with Application in Asset Management

This dissertation first analyzes parametric convex conic optimization problems and corresponding robust problems with respect to stability properties. Robustification is based on a worst case approach explicitly considering uncertainty in the parameters. After theoretical investigations of benefits and costs of this method, robustification is applied to the well-known portfolio optimization problem of Markowitz. The focus is on modelling the needed uncertainty set for the practical problem appropriately. The promising results of robust portfolio optimization are illustrated using real data.
Nonequilibrium candidate Monte Carlo: A new tool for equilibrium simulation

Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also rapidly sample uncorrelated configurations. Here, we introduce a new class of moves based on nonequilibrium dynamics: Candidate configurations are generated through a finite-time process in which a system is actively driven out of equilibrium, and accepted with criteria that preserve the equilibrium distribution. The acceptance rule is similar to the Me-tropolis acceptance probability, but related to the nonequilibrium work rather than the instantaneous energy difference. Our method is applicable to sampling from both a single thermodynamic state or a mixture of thermodynamic states, and allows both coordinates and thermodynamic parameters to be driven in nonequilibrium proposals. Whereas generating finite-time switching trajectories incurs an additional cost, driving some degrees of freedom while allowing others to evolve naturally can lead to large enhancements in acceptance probabilities, greatly reducing structural correlation times. Using nonequilibrium driven processes vastly expands the repertoire of useful Monte Carlo proposals in simulations of dense solvated systems.
Assessing accuracy and precision for space‐based measurements of carbon dioxide: An associated statistical methodology revisited

Analyzing retrieval accuracy and precision is an important element of space‐based CO2 retrievals. However, this error analysis is sometimes challenging to perform rigorously because of the subtlety of Multivariate Statistics. To help address this issue, we revisit some fundamentals of Multivariate Statistics that help reveal the statistical essence of the associated error analysis. We show that the related statistical methodology is useful for revealing the intrinsic discrepancy and relation between the retrieval error for a nonzero‐variate CO2 state and that for a zero‐variate one. Our study suggests that the two scenarios essentially yield the same‐magnitude accuracy, while the latter scenario yields a better precision than the former. We also use this methodology to obtain a rigorous framework systematically and explore a broadly used approximate framework for analyzing CO2 retrieval errors. The approximate framework introduces errors due to an essential, but often forgotten, fact that a priori climatology in reality is never equal to the true state. Due to the nature of the problem considered, realistic numerical simulations that produce synthetic spectra may be more appropriate than remote sensing data for our specific exploration. As highlighted in our retrieval simulations, utilizing the approximate framework may not be universally satisfactory in assessing the accuracy and precision of Xco2 retrievals (with errors up to 0.17–0.28 ppm and 1.4–1.7 ppm, respectively, at SNR = 400). In situ measurements of CO2 are needed to further our understanding of this issue and related implications.
Adaptation and Utilization Phase of Radio Frequency Identification (RFID) Technology at Lancang Kuning University

: The development of computer technology that is in line with the human will to facilitate daily work, this gave rise to the idea of being able to develop inventions to be used both individually and in groups as part of an agency/institutional organization. In recent years, automatic identification (Auto-ID) procedures have become very popular especially in campus and industrial environments; contactless identification has developed into an independent interdisciplinary field, which is no longer suitable for the use of any (conventional) single-discipline focus. The RFID technology in this case is a discipline of several elements from very diverse disciplines: databases, RF and EMC technology, semiconductor technology, data protection and cryptography, telecommunications, manufacturing technology
Algorithm development using an agnostic machine learning platform for spectroscopy (AMPS)

Alakai Defense Systems has developed several standoff ultra-violet (UV) Raman systems over the years to enable detection of hazardous chemicals from a safe distance. These systems have traditionally used classical non-machine-learning- based algorithms, but Alakai together with its partner Systems & Technology Research (STR) are currently developing the Agnostic Machine learning Platform for Spectroscopy (AMPS). AMPS, implemented using PyTorch, automatically creates and optimizes tailored one-dimensional (1D) convolutional neural networks (CNN) when trained on simulated or measured data. Several emerging and novel techniques, including advanced domain adaptation approaches, have been implemented to increase model robustness and minimize training data requirements. While the created models are optimized for a specific modality, AMPS itself is agnostic—it can be used for any spectroscopic modality that produces 1D spectra. AMPS has shown promising results for long-wave infrared (LWIR) reflectance spectroscopy as well as UV and near-infrared (NIR) Raman. This talk will focus on AMPS models created using both simulated UV Raman data as well as measured UV Raman data taken with Alakai’s Portable Raman Improvised Explosives Detection (PRIED) system. Performance between AMPS and Alakai’s legacy algorithms will be compared.
Direct lifts of coupled cell networks

In networks of dynamical systems, there are spaces defined in terms of equalities of cell coordinates which are flow-invariant under any dynamical system that has a form consistent with the given underlying network structure—the network synchrony subspaces. Given a network and one of its synchrony subspaces, any system with a form consistent with the network, restricted to the synchrony subspace, defines a new system which is consistent with a smaller network, called the quotient network of the original network by the synchrony subspace. Moreover, any system associated with the quotient can be interpreted as the restriction to the synchrony subspace of a system associated with the original network. We call the larger network a lift of the smaller network, and a lift can be interpreted as a result of the cellular splitting of the smaller network. In this paper, we address the question of the uniqueness in this lifting process in terms of the networks’ topologies. A lift G of a given network Q is said to be direct when there are no intermediate lifts of Q between them. We provide necessary and sufficient conditions for a lift of a general network to be direct. Our results characterize direct lifts using the subnetworks of all splitting cells of Q and of all split cells of G. We show that G is a direct lift of Q if and only if either the split subnetwork is a direct lift or consists of two copies of the splitting subnetwork. These results are then applied to the class of regular uniform networks and to the special classes of ring networks and acyclic networks. We also illustrate that one of the applications of our results is to the lifting bifurcation problem.
Investigating the structure of the oxide on Ni‐Cr‐Mo alloys while presenting a method for analysis of complex oxides using QUASES

X‐ray photoelectron spectroscopy (XPS) is a technique that is widely used to study thin oxide films because of its extremely high surface sensitivity. Utilizing the QUASES (Quantitative Analysis of Surfaces by Electron Spectroscopy) software package developed by Sven Tougaard (University of Southern Denmark), a user can obtain additional information that is not extracted in conventional XPS analysis, specifically the composition as a function of depth. Presented here is the QUASES analysis of four Ni‐Cr‐Mo alloys performed while testing various inelastic mean free path (IMFP) determination methods in the context of providing a framework for the analysis of complex oxides in QUASES. Ni‐Cr‐Mo alloys are often used to replace conventional materials under aggressive conditions, because of their exceptional corrosion resistance. Their corrosion resistance is conferred by the formation of an inert surface oxide film that protects the underlying metal. Using the QUASES software, the thickness of the air‐formed oxide on four Ni‐Cr‐Mo alloys was found to lie within the range of 2.5–3.6 nm. They were found to be composed of an inner Cr2O3 layer and an outer Cr (OH)3 layer, with a transition zone where the two coexisted. Oxidized Mo species, MoO2 and MoO3, were found in trace amounts at the boundary between the Cr2O3‐only and mixed Cr2O3/Cr (OH)3 regions of the oxide. We also determined that using 20% reduced IMFP values gave results similar to those obtained using electron effective attenuation length (EAL) values. Auger depth profiles showed comparable trends to the QUASES models.
Lagrangian particle tracking post-processing for linking CFD to bioreactor analysis

A Lagrangian particle tracking (LPT) solver is presented that can be used for studying culture, cell and particle interactions in flows, through coupling of the LPT solver to the Computational Fluid Dynamics (CFD) in postprocessing. The LPT solver is part of a three-way coupling strategy for flows dilutely laden with particles but in which particle-particle interactions are potentially important. The LPT solver is used as a post-processor, in conjunction with a strategy for mapping fluid flow data from the relevant historical fluid-only CFD database to Lagrangian particle locations. This strategy is effective in enabling cell-fluid, cell-particle and cell-cell interactions in stirred-tank bioreactor (STR) flows to be studied without requiring repeated CFD computations. In particular, studies of bioreactor STR particle inclusions can be iterated rapidly using historical CFD data sets using this approach, rather than repeated coupled CFD/LPT simulations for any change in inclusions. The LPT solver is used to reveal density effects on particle suspension and stress exposures.
Forecasting of meteorological drought using ARIMA model

Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems. The principal objective of the study is to carryout short-term annual forecasting of meteorological drought using Auto Regressive Integrated Moving Average (ARIMA) model in Lower Thirumanimuthar Sub-basin located in semi-arid region of Tamilnadu, India is chosen as the study area which is predominantly affected by drought over few decades. Suitable linear stochastic model, non seasonal autoregressive integrated moving average (ARIMA) was developed to predict drought. The best model was selected based on minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). Parameter estimation step indicates that the estimated model parameters are significantly different from zero. The predicted data using the best ARIMA model were compared to the observed data for model validation purpose in which the predicted data show reasonably good agreement with the actual data. Hence the models were applied to forecast drought in the Lower Thirumanimuthar sub-basin region up to 2 years in advance with good accuracy.
Dark Matter and Dark Energy: Mysteries of the Universe

The Universe has a flat geometry and its density is very close to critical density. However, the observed amount of matter accounts for only 5% of the critical density. The rest of the 95% is completely unknown to us which exists in the form of Dark Energy (68%) and Dark Matter (27%). We present an overview of how the very idea of the existence of Dark Matter emerged and some compelling evidences for the existence of such matter. Moreover, we also provide an insight on how scientific ideas have evolved from a static Universe to an expanding Universe and then to an accelerating Universe. In addition, we explain fundamental concepts related to Dark Energy and discuss briefly on the evidences of Dark Energy. We also discuss some alternative solutions to the problems of Dark Matter and Dark Energy provided by different scientists.
A contribution to knowledge of the Balkan Lepidoptera. Some Pyraloidea (Lepidoptera: Crambidae & Pyralidae) encountered recently in southern Serbia, Montenegro, the Republic of Macedonia and Albania

Pyraloidea (Lepidoptera: Crambidae & Pyralidae) were sampled in the territories of southern Serbia, Montenegro, the Former Yugoslav Republic of Macedonia and Albania on a total of 53 occasions during 2014, 2016 and 2017. A total of 173 species is reported here, comprising 97 Crambidae and 76 Pyralidae. Based upon published data, 29 species appear to be new to the fauna of Serbia, 5 species are new to the fauna of Macedonia and 37 are new to the fauna of Albania. The data are discussed.
Potassium chloride recovery from mechanically activated microcline through the chlorination roasting and leaching route

In this study, effects of mechanical activation in the chlorination roasting and water leaching route known as CaCl2 process and developed for the production of potassium chloride (KCl) from potassium feldspar ores were studied. A microcline containing K-feldspar ore with 10.89% K2O was first intensively dry milled by a planetary ball mill and mixed with calcium chloride (CaCl2) and then roasted at temperatures up to 1000 °C to obtain KCl that will be finally dissolved by the water leaching. Potassium recovery by water leaching increased rapidly up to 800 °C. At higher temperatures, the recovery decreased fast due to the evaporation of KCl. According to the K recovery values per unit energy consumed, the optimum roasting temperature was determined as 750 °C and the milling time was 15 min. It was concluded that intensive milling causes mechanical activation of the microcline to reduce the chlorination roasting temperature, which triggers a rise in the K recovery by the water leaching.
Some Recent Developments in Non-Linear Time Series Modelling

Most of the recent work in time series analysis has been done on the assumption that the structure of the series can be described by linear models such as Autoregressive (AR), Moving Average (MA) or mixed Autoregressive-Moving Average (ARMA) models. However, there are occasions on which subject matter, theory •or data suggests that linear models are unsatisfactory and hence it is desirable to look at non-linear time series models. In the last decade several non-linear time series models have appeared in literature, specifically, bilinear time series models, threshold AR models, exponential AR models, random coefficient AR models, exponential moving average models and other related models. In this paper we have reviewed various non-linear time series models. We have also reviewed various tests of non-linearities developed by various authors. Since the model specification is the most important step in any time series model building, we have discussed the problem of model specification in the context of bilinear and threshold models in detail. SOME RECENT DEVELOPMENTS IN NON-LINEAR TIME SERIES MODELLING
Fisher information estimation using neural networks

In estimation theory, the Fisher information matrix (FIM) is a fundamental concept from which we can infer the well-known Cramér-Rao bound. A closed-form expression of the FIM is often intractable due to the lack or sophistication of statistical models. In this paper, we propose a Fisher Information Neural Estimator (FINE) based on neural networks and a relation between the f-divergence and the Fisher information. The proposed method produces an estimate of the FIM directly from observed data. It does not require knowledge or an estimate of the probability density function (pdf), and is therefore universally applicable. The proposed FINE is applicable for not only deterministic parameters but also random parameters. We show via numerical results that the proposed FINE can provide a highly-accurate FIM estimate with a low-computational complexity. Furthermore, we also propose an accelerated FINE version which can be used for scenarios with a high parameter dimension. Finally, we develop an algorithm to choose an appropriate size of the employed neural networks.
Coloured noise in Langevin simulations of superparamagnetic nanoparticles

The coloured noise formalism has long formed an important generalisation of the white noise limit assumed in many Langevin equations. The Langevin equation most typically applied to magnetic systems, namely the Landau-Lifshitz-Gilbert (LLG) equation makes use of the white noise approximation. The correct extension of the LLG model to the coloured noise is the Landau-Lifshitz-Miyazaki-Seki pair of Langevin equations. This pair of Langevin equations correctly incorporates a correlated damping term into the equa- tion of motion, constituting a realisation of the Fluctuation-Dissipation theorem for the coloured noise in the magnetic system. We undertake numerical investigation of the properties of systems of noninteracting magnetic moments evolving under the LLMS model. In particular, we apply the model to superparamagnetic spins. We investigate the escape rate for such spins and find that departure from uncorrelated behaviour occurs as the system time approaches the bath correlation time, and we see that the relevant system time for the superparamagnetic par- ticles is the Larmor precession time at the bottom of the well, leading us to conclude that materials with higher magnetic anisotropy constitute better candidates for the exhibition of non-Markovian properties. We also model non-Markovian spin dynamics by modifying the commonly used dis- crete orientation approximation from a Markovian rate equation to a Generalised Master Equation (GME), where the interwell transition rates are promoted to memory kernels. This model makes the qualitative prediction of a frequency-dependent diamagnetic sus- ceptibility, as well as a biexponential decay profile of the magnetisation. The predictions of the GME are compared to the results of LLMS simulations, where we find a similar diamagnetic phase transition and biexponential behaviour.