Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of the Community Structure in a Large-Scale Production Network in Japan

Abstract Inter-firm organizations, which play a driving role in the economy of a country, can be represented in the form of a customer–supplier network. Such a network exhibits a heavy-tailed degree distribution, disassortative mixing and a prominent community structure. We analyze a large-scale data set of customer–supplier relationships containing data from one million Japanese firms. Using a directed network framework, we show that the production network exhibits the characteristics listed above. We conduct detailed investigations to characterize the communities in the network. The topology within smaller communities is found to be very close to a tree-like structure but becomes denser as the community size increases. A large fraction ( ∼ 40 % ) of firms with relatively small in- or out-degrees have customers or suppliers solely from within their own communities, indicating interactions of a highly local nature. The interaction strengths between communities as measured by the inter-community link weights follow a highly heterogeneous distribution. We further present the statistically significant over-expressions of different prefectures and sectors within different communities.
Convolutional autoencoded echo state network for the prediction of extreme events in turbulence

Extreme events are sudden changes of a turbulent state or observable. The time-accurate and statistical prediction of extreme events is diﬃcult to achieve because their dynamics are chaotic, and hence unpredictable. The goal of this paper is to propose a methodology to learn the turbulent dynamics from data for the time-accurate prediction of unseen extreme events. We break down the spatiotemporal turbulent problem into spatial and temporal problems. First, we reduce the high-dimensional turbulent dynamics into a lower-dimensional latent space by using a nonlinear transformation based on convolutional neural networks. Second, we compute the temporal dynamics in the low-dimensional latent space by using a reservoir computer, which is a form of recurrent neural network. We seamlessly assemble the two architectures into the convolutional au-toencoded echo state network (CAE-ESN). The test case is a three-dimensional (3D) turbulent ﬂow (the minimal ﬂow unit) in which quasi-relaminarization events with their bursts in kinetic energy and dissipation rate are the extreme events. We show that the CAE-ESN is (i) scalable to a 3D turbulent ﬂow; (ii) eﬃcient, requiring approximately one-tenth as many degrees of freedom as proper orthogonal decomposition for compressing the data; and (iii) accurate in time-accurately forecasting the occurrence of the quasi-relaminarization events and their statistics. The proposed architecture opens opportunities for reduced-order modeling and time-accurate prediction of turbulent ﬂow from data.
Improved Action Potential Detection for Imaging Techniques by exploiting Fuzzy C-Means Clustering

In recent years, observing neuronal activity by inferring action potentials (APs) in mammals has attracted much attention. A common way to observe thousands of neurons simultaneously is by using calcium imaging techniques. However, estimating the APs based on the fluorescence signal obtained by the calcium imaging technique is a challenging task due to noise, slow imaging rates and especially the nonlinearity of the calcium binding kinetics. Though the electrical recording technique can measure the APs very precisely, it is rather time consuming in practice. In this paper, an approach is proposed that reconstruct the APs based on the noisy fluorescence signal. For this purpose, at first the forward-backward filtering is applied on the fluorescence signal to reduce the level of noise and to avoid the nonlinear shift in time with respect to the true fluorescence signal. Then, for each local maximum in the filtered fluorescence signal, three characteristic values, namely, the integral, the amplitude and the gradient are extracted to localize the neuronal activity. By exploiting the fuzzy c-means clustering method, the time instants and the number of APs can be estimated. The proposed approach is validated by using the well-established spikefinder challenge data. The comparison shows that the proposed approach outperforms other existing AP estimation approaches.
Student Perceptions of Economic Education Through the Implementation of Merdeka Belajar Kampus Merdeka

This study aimed to determine whether Merdeka Belajar Kampus Merdeka (MBKM) activities could achieve program goals, assess the implementation of the MBKM program, and examine how MBKM activities were developed and implemented, which included assisting teachers in teaching, supporting school administration, assisting in technology adaptation, and improving skills of students in reading and counting. This research was conducted using qualitative methods. The procedure used in this research is to identify research data sources, collect and evaluate qualitative data, and draw conclusions and suggestions based on qualitative data analysis. The subjects of this study were 27 graduates of the MBKM Batch 2 Economics Study Program. This research helped assist teaching activities in adopting technology by teachers and school administration as well as improving students' literacy and numeracy skills. The results of this study indicate that the implementation of the learning process for MBKM Batch 2 participants, both offline and through home visits, has been running in accordance with the objectives of implementing the Kampus Mengajar (KM). They also pointed out that the implementation of the MBKM in supporting school administration and teachers has been running according to this objective.
Impact of the heat release distribution on high-frequency transverse thermoacoustic driving in premixed swirl flames

Self-excited, high-frequency first transversal thermoacoustic instabilities in a cylindrical combustion chamber equipped with a premixed swirl-stabilized flame are investigated. Phase-locked image analysis of the phenomena shows the displacement of the flame and a higher burning rate in the region of elevated pressure. The impact of diffuser angle and fuel composition on the stability limits and the flame position is investigated. The Rayleigh-Index is computed for a three-dimensional domain based on analytical flame transfer functions for experimentally obtained data of OH*-chemiluminescence as measure for the spatial heat release. Two models from different sources are applied, which describe the interaction between flame and acoustic locally. The axial dependence of the amplitude of the transversal mode is computed by a numerical model, which takes the temperature distribution inside the combustion chamber into account. The comparison of the Rayleigh-Index of different operation points shows a correlation with the stability limits for some, but not for all investigated configurations.
Temperature-Dependent Photoluminescence Analysis of 1.0 MeV Electron Irradiation-Induced Nonradiative Recombination Centers in n+–p GaAs Middle Cell of GaInP/GaAs/Ge Triple-Junction Solar Cells

The effects of irradiation of 1.0 MeV electrons on the n+–p GaAs middle cell of GaInP/GaAs/Ge triple-junction solar cells are investigated by temperature-dependent photoluminescence (PL) measurements in the 10–300 K temperature range. The appearance of thermal quenching of the PL intensity with increasing temperature confirms the presence of a nonradiative recombination center in the cell after the electron irradiation, and the thermal activation energy of the center is determined using the Arrhenius plot of the PL intensity. Furthermore, by comparing the thermal activation and the ionization energies of the defects, the nonradiative recombination center in the n+–p GaAs middle cell acting as a primary defect is identified as the E5 electron trap located at Ec−0.96 eV.
Femtosecond laser direct-write of optofluidics in polymer-coated optical fiber

Multifunctional lab in fiber technology seeks to translate the accomplishments of optofluidic, lab on chip devices into silica fibers. a robust, flexible, and ubiquitous optical communication platform that can underpin the ‘Internet of Things’ with distributed sensors, or enable lab on chip functions deep inside our bodies. Femtosecond lasers have driven significant advances in three-dimensional processing, enabling optical circuits, microfluidics, and micro-mechanical structures to be formed around the core of the fiber. However, such processing typically requires the stripping and recoating of the polymer buffer or jacket, increasing processing time and mechanically weakening the device. This paper reports on a comprehensive assessment of laser damage in urethane-acrylate-coated fiber. The results show a sufficient processing window is available for femtosecond laser processing of the fiber without damaging the polymer jacket. The fiber core, cladding, and buffer could be simultaneously processed without removal of the buffer jacket. Three-dimensional lab in fiber devices were successfully fabricated by distortion-free immersionlens focusing, presenting fiber-cladding optical circuits and progress towards chemically-etched channels, microfluidic cavities, and MEMS structure inside buffer-coated fiber.
Target Design Optimization of KIPT Neutron Source Facility

Argonne National Laboratory (ANL) of the United Sta tes developed and designed a neutron source facility for Kharkov Institute of Physics and Techn ology (KIPT) of Ukraine. The facility was construc ted at Kharkov, Ukraine and its commissioning process h a been started. The facility has an electron accelerator driving a subcritical assembly. The el ectron beam power is 100 kW using 100 MeV electrons . The subcritical assembly has WWR-M2 fuel assemblies with U-235 enrichment of 19.7 wt%. The facility will be utilized to perform basic and applied nucle ar research, to produce medical isotopes, and to tr ain young nuclear specialists. Solid target design wit h stacked disks is selected and each target disk is cooled by water from both side. Tungsten or natural urani um s the target material. This paper presents the target design optimization to maximize the neutron yield and the neutron flux level of the facility wh ile satisfying the thermal-hydraulic design criteria. Monte Carlo computer code MCNPX is utilized for the neutron yield analyses with ENDF/B-VII.0 nuclear da ta libraries, as a function of target thickness usi ng a simplified target model without cladding or coolant channels. It’s found that the neutron yield satur ates at target thickness ~ 60 mm for both tungsten and uran i m materials. For the final target design with cladding and coolant channels, the total uranium th ickness is 56.5 mm, and the total tungsten thicknes s is only 33.0 mm. The tungsten material has a large ab sorption cross section for thermal neutrons. If th e tungsten thickness is increased, the neutron yield gain is offset by the neutron absorption reaction. The paper presents the design optimization analyses for both target materials.
The protective effect of bosentan on methotrexate-induced oral mucositis in rats

Article history: Received October 23, 2022 Accept March 29, 2023 Available online June 10, 2023 This study aimed to evaluate the effectiveness of bosentan as a protective agent on the rat’s cheek mucosa for Methotrexate-induced oral mucositis. Twenty-four Wister-albino rats, about 12-16 weeks and weighing 300-400 g, were used. The animals were divided into three groups. In the first control group, the second group was treated with methotrexate (80 mg/kg), and the third group was treated with methotrexate and bosentan 60 mg/kg. The microscopical parameters were estimated, the pro-inflammatory marker (TNF-α) and intercellular adhesive molecule-1(ICAM-1) measurement in the buccal mucosa. The animals were sacrificed on day 15, and the buccal mucosa was dissected for histological and immunohistochemical analysis. Bosentan decreased the severity of the Methotrexateinduced oral mucositis by a significant decrease in the histological score and a significant decrease in TNF-α and ICAM-1 immune histochemical expression at day fifteen. Bosentan at a concentration of 60 mg/kg/day produced protection against Methotrexate-induced oral mucositis by its anti-inflammatory action and reduction in the histological score and, therefore, can be used as a protective agent against Methotrexate-induced oral mucositis.
LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning

Dexterous manipulation has been a long-standing challenge in robotics. While machine learning techniques have shown some promise, results have largely been currently limited to simulation. This can be mostly attributed to the lack of suitable hardware. In this paper, we present LEAP Hand, a low-cost dexterous and anthropomorphic hand for machine learning research. In contrast to previous hands, LEAP Hand has a novel kinematic structure that allows maximal dexterity regardless of finger pose. LEAP Hand is low-cost and can be assembled in 4 hours at a cost of 2000 USD from readily available parts. It is capable of consistently exerting large torques over long durations of time. We show that LEAP Hand can be used to perform several manipulation tasks in the real world -- from visual teleoperation to learning from passive video data and sim2real. LEAP Hand significantly outperforms its closest competitor Allegro Hand in all our experiments while being 1/8th of the cost. We release detailed assembly instructions, the Sim2Real pipeline and a development platform with useful APIs on our website at https://leap-hand.github. io/
The perception of silence

Significance Do we only hear sounds? Or can we also hear silence? These questions are the subject of a centuries-old philosophical debate between two camps: the perceptual view (we literally hear silence), and the cognitive view (we only judge or infer silence). Here, we take an empirical approach to resolve this theoretical controversy. We show that silences can “substitute” for sounds in event-based auditory illusions. Seven experiments introduce three “silence illusions,” adapted from perceptual illusions previously thought to arise only with sounds. In all cases, silences elicited temporal distortions perfectly analogous to their sound-based counterparts, suggesting that auditory processing treats moments of silence the way it treats sounds. Silence is truly perceived, not merely inferred.
Adolescence and Stress: A Longitudinal Examination of the Role of Stress on Health Outcomes and the Moderating Role of Self-Regulation and Social Support

Adolescence and Stress: A Longitudinal Examination of the Role of Stress on Health Outcomes and the Moderating Role of Self-Regulation and Social Support Joshua T. Christensen Department of Psychology, Brigham Young University Doctor of Philosophy Stress impacts all individuals during each developmental period of their lives. Stress has been linked to various physical and mental health outcomes across the lifespan. Adolescence may prove to be a unique time to examine stress and its potential effects on various health outcomes, as well as possible mediating or moderating factors. The purpose of this study was to examine the link between stress and body mass index (BMI) while incorporating potential mediators of anxiety, depression, and externalizing behaviors as well as moderators of social support and selfregulation. Using a sample of 500 adolescents living in the Pacific Northwest, moderated mediation SEM models were estimated to test for these relationships. Daily hassles was a significant positive predictor of depression and externalizing behavior, while family chaos only positive predicted externalizing behavior. None of the three mediators significantly predicted BMI. When the moderators were added, two significant interactions were found between family chaos and parent social support for anxiety and externalizing behavior with another significant interaction found between family chaos and peer social support for depression. Specifically, the positive link between family chaos and these three mediators was weaker at higher levels of parent and peer social support. Thus, parent and peer social support may buffer the effect of familial stress on mental health. Future research can examine these relationships, as well as other potential moderators between stress and health outcomes during adolescence.
An Inquiry-Based Quantitative Reasoning Course for Business Students

Abstract Quantitative Reasoning for Business is a two-semester sequence that serves as an alternative to elementary and intermediate algebra for first-year business students with weak mathematical preparation. Students who take the sequence have been retained at a higher rate and demonstrated a larger reduction in math anxiety than those who take the traditional developmental courses. We share the challenges that led to our choices in adopting and modifying inquiry-based instruction in order to serve our population.
Tourists’ risk perception towards Kashmir valley: An analysis using Tourism Risk Index

Purpose: This research aims to present a Tourism Risk Index (TRI) for risk analysis at tourism destinations that can act as a usable tool to accurately capture the perception of risk by tourists. Methods: The TRI for this study is developed in Kashmir valley (India). The development of index began with the assumption that general perceptions echoed through mass media and word of mouth about the lack of security in the Kashmir valley are correct. It was followed by a survey of 370 tourists visiting the valley about common types of risks identified through the literature on tourism. Results: The results are not along expected lines rather suggest that visiting tourists perceive Kashmir valley as safe. The findings show that Kashmir valley is perceived overall as less risky on all components, and in descending order, these ranks as personal safety, natural risk, cultural risk, and human-induced risk. Implications: Destination managers would have promoted Kashmir valley with a different level of confidence if this insight would have been available to them, and the possibility of its positive effect on the perception of tourists not visiting the valley cannot be ruled out. The index can be used to consistently track the tourism risks of Kashmir or any other destination by inclusions of new risks as they crop up from time to time.
Global Inducing Point Posterior Approximations for Federated Bayesian Neural Networks

In federated learning, we seek to train a predictive model when the training data is sharded across (possibly many) different clients or users’ devices. One attractive model type is a Bayesian Neural Network (BNN) as it combines the predictive capacity of a neural network with natural regularization and uncertainty estimates by having distributions over the model parameters. Since the posterior distribution over the BNN’s weights is intractable, we specify an approximating variational posterior distribution that is tractable and optimize this approximate posterior with respect to the training data using Variational Inference (VI). The structure of this approximating posterior consequently affects the performance of our model. A standard—but simplistic—approximating posterior is the mean-field variational approximation (MFVI). Ober and Aitchison [2021] recently proposed a posterior approximation method based on global inducing points (GI) that outperformed the MFVI method in standard Bayesian learning settings. In this work, we extend the GI variational approximation for BNNs to the federated learning setting and evaluate its performance. In the first part of this thesis, we provide a more in-depth background for the problem set-up described above. We then develop the theory necessary to extend the GI method to federated BNNs and provide a theoretical analysis of the communication and computational complexity of a federated BNN with GI as approximate posterior. In our evaluations, we compare it to using a standard MFVI approximation. In the second part of this thesis, we complete an empirical evaluation of GI and MFVI in BNNs under various federated learning settings. Due to the computational complexity of Bayesian federated learning, we limit our experiments to shallow BNNs with a small number of clients and medium-scale datasets. We evaluate said methods based on their predictive performance, communication and time efficiency, and convergence dynamics across both homogeneous and inhomogeneous client data distributions. We demonstrate that, across every setting, GI outperforms MFVI in all aspects except for training time efficiency, due to its computational complexity.
Sim-Piece+: Efficient Time Series Data Compression

Lossy compression enhances the scalability of contemporary data collection infrastructures by reducing the volume of data that needs to be transmitted and stored. Sim-Piece is a state-of-the-art lossy compressing algorithm for time series data that outperforms competing techniques, attaining compression ratios with more than twofold improvement on average over what prior lossy algorithms can offer. In this work, we introduce Sim-Piece+, an enhanced iteration of Sim-Piece that further enhances compression ratios through the utilization of advanced data encoding techniques
Revisiting controlled mixture sampling for rendering applications

Monte Carlo rendering makes heavy use of mixture sampling and multiple importance sampling (MIS). Previous work has shown that control variates can be used to make such mixtures more efficient and more robust. However, the existing approaches failed to yield practical applications, chiefly because their underlying theory is based on the unrealistic assumption that a single mixture is optimized for a single integral. This is in stark contrast with rendering reality, where millions of integrals are computed---one per pixel---and each is infinitely recursive. We adapt and extend the theory introduced by previous work to tackle the challenges of real-world rendering applications. We achieve robust mixture sampling and (approximately) optimal MIS weighting for common applications such as light selection, BSDF sampling, and path guiding.
Poster: Improving Legacy IoT Device Security through Open-Source Intervention

—When a vendor can no longer support an Internet of Things (IoT) device, what is next? With the rapid growth of IoT, vendors are constantly motivated to develop new products to keep up with market demands. Over time, older products become “legacy” or “unsupported” by vendors and no longer receive software updates and security patches. Due to the long-term deployment nature of many IoT devices, they may remain active for years with several unpatched vulnerabilities. We ﬁrst examine the security threat that unsupported and unpatched devices pose to IoT ecosystems, and then propose a model that enables legacy IoT devices to transition to an open-source support model while respecting the original vendor’s intellectual property.