Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumer acceptability and sensory evaluation of a fermented cassava product (Nigerian fufu)

The purpose of the study was to evaluate the sensory profile and acceptability of six types of fufu (a fermented cassava product) produced by different processes using sensory evaluation and consumer acceptability testing. Fufu samples were selected to evaluate the acceptability of dried fufu flours, which are increasingly being consumed, and compare with a traditional paste and a newly developed paste that produces less environmental waste. Descriptive sensory profiles of the selected samples demonstrated distinct differences in sensory profiles. They were evaluated for consumer acceptance at three demographic locations; Lagos (n = 91), Ibadan (n = 121) and Abeokuta (n = 99), Nigeria. Fufu made from a paste that produced less environmental waste had the highest acceptance scores, followed by flour and paste made by the traditional method and finally the remaining flours. Average consumer liking between the three locations did not differ. Three distinct consumer segments were identified and the number of consumers in these segments differed between the locations. Consumers differentiated fufu made from pastes and from flour. Internal preference mapping indicated that consumers associated the flours with sensory attributes such as sticky texture and raw cassava odour, while pastes were associated with soft texture, and shiny and creamy appearance. Fufu acceptance varies widely among consumers and is related to preferences for distinct fufu flavour profiles. Correlations between consumer acceptance scores and sensory scores appeared to be non-linear for many attributes but a larger sample size of fufu samples would be necessary to confirm this. The implications of these finding are discussed.
Gold Nanoparticle Adsorption and Aggregation Kinetics at the Silica−Water Interface

Gold nanoparticles have been synthesized as colloids using a citrate and borohydride reduction of auric chloride producing 15 and 45 nm particles, respectively. The nanoparticle adsorption kinetics have been observed at the silica-water interface by evanescent wave cavity ring-down spectroscopy. AFM images have been used to determine the surface coverage from which the extinction coefficients of the particles in water have been determined at two interrogation wavelengths: 15 nm particles e(635 nm) = 6.4 ± 1.6 × 107 M-1 cm-1, e(830 nm) = 9.8 ± 0.2 × 106 M-1 cm-1; and 45 nm particles e(635 nm) = 3.1 ± 1.8 × 109 M-1 cm-1, e(830 nm) = 9.2 ± 1.5 × 108 M-1 cm-1. These values are larger than the Mie scattering calculations would predict. Mono- and multilayer adsorption kinetics have been observed with monolayer binding constants KD = 2.75 ± 0.55 nM for the 15 nm particles and 0.74 ± 0.47 nM for the 45 nm particles. An initial slope analysis of the binding kinetics shows the 15 nm particles undergo aggregation a.
Nondeterminism and Instability in Neural Network Optimization

Nondeterminism in neural network optimization produces uncertainty in performance, making small improvements difficult to discern from run-to-run variability. While uncertainty can be reduced by training multiple model copies, doing so is time-consuming, costly, and harms reproducibility. In this work, we establish an experimental protocol for understanding the effect of optimization nondeterminism on model diversity, allowing us to isolate the effects of a variety of sources of nondeterminism. Surprisingly, we find that all sources of nondeterminism have similar effects on measures of model diversity. To explain this intriguing fact, we identify the instability of model training, taken as an end-to-end procedure, as the key determinant. We show that even one-bit changes in initial parameters result in models converging to vastly different values. Last, we propose two approaches for reducing the effects of instability on run-to-run variability.
Real-Time Observation of the Transformation of Ultrafine Atmospheric Particle Modes

An Engine Exhaust Particle Sizer was used on a mobile platform to measure ultrafine particle (5.6–560 nm) concentration profiles in streets and tunnels. Particle size distribution data along the lengths of the tunnels showed that number concentration profiles of < 30 nm particles were highly nonlinear. Conventional point measurement studies inside tunnels to determine, for instance, emission factors of ultrafine particles in this range are therefore location dependent and may not be representative. The particle concentration profiles obtained in this experiment allowed the study of the transformation of 10 nm particles to 50 nm particles. Outside of the tunnels and on heavily traveled highways, particles exhibited a dominant mode at 10 nm and a minor mode at 50 nm. Inside the tunnels, 10 nm particles gradually decreased after an abrupt increase, while 50 nm particles increased from entrance to exit. The 10 nm particles decreased at 2–4 times the rate of increase of the 50 nm particles, therefore, condensational growth of the 10 nm particles alone cannot explain the decrease. The estimated hetero-coagulation rate between 10 nm particles and particles > 30 nm inside the tunnel was substantially higher than that outside due to the increase in the concentration of particles > 30 nm. The hetero-coagulation process is proposed to play a key role in the transformation of particle modes in the tunnel. This study provides new insight on the transformation of ultrafine particle modes in vehicular plumes.
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction

A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while existing video prediction models have produced promising results on small datasets, they suffer from severe underfitting when trained on large and diverse datasets. To address this underfitting challenge, we first observe that the ability to train larger video prediction models is often bottlenecked by the memory constraints of GPUs or TPUs. In parallel, deep hierarchical latent variable models can produce higher quality predictions by capturing the multi-level stochasticity of future observations, but end-to-end optimization of such models is notably difficult. Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction. We introduce Greedy Hierarchical Variational Autoencoders (GHVAEs), a method that learns highfidelity video predictions by greedily training each level of a hierarchical autoencoder. In comparison to state- of-the-art models, GHVAEs provide 17-55% gains in prediction performance on four video datasets, a 35–40% higher success rate on real robot tasks, and can improve performance monotonically by simply adding more modules. Visualization and more details are at https://sites.google. com/view/ghvae.
ANALYSIS OF THE PRESSURE EFFECT ON THE CRYSTALLIZATION KINETICS OF POLYPROPYLENE: DILATOMETRIC MEASUREMENTS AND THERMAL GRADIENT MODELING

Dilatometric measurements in isobaric cooling mode were performed to study the pressure effects on the crystallization kinetics of polypropylene (PP) up to 100 MPa. The experimental specific volume curves were analyzed by taking into account the thermal gradient that appears in the sample even for relatively low cooling rates. The Tait equation was used to describe the specific volume of the purely amorphous phase, and linear variations of the purely crystalline phase specific volume are considered. The relative crystal linity was modeled using the Nakamura equation, which is relevant for non-constant cooling rates. Considering an Avrami exponent of 3, the Nakamura rate constant was obtained first at atmospheric pressure and then generalized for higher pressures considering the equilibrium melting temperature variation. The obtained intrinsic specific volume was validated by computing the thermal gradient in the sample and comparing the calculated average specific volume to the experimental one.
Determination of Geometrical Length of Airborne Carbon Nanotubes by Electron Microscopy, Model Calculation, and Filtration Method

The geometrical length of carbon nanotubes (CNTs) is of significant importance and this study aims at its fast determination. We evaluated a method to determine the geometrical length of CNTs by electrical mobility classification and developed a filtration based method. The measurement was carried out in air and the suitable generation method for airborne CNTs from liquid suspensions was investigated, followed by a proper control of the CNT dispersion degree by changing the CNT concentration in the liquid suspension. Airborne CNTs were generated by an atomizer, classified by a differential mobility analyzer and characterized by electron microscopy. The CNT geometrical lengths measured by electron microscopy served as the basis for comparison with other methods. Two theoretical models (Lall and Friedlander 2006; Li et al. 2012) for elongated particles were investigated and the comparison showed good agreement. This demonstrated the validity of the approach to determine the CNT length by the combination of mobility measurement and model calculation. The newly developed method, which employed a filtration model with uniform screens, showed comparable results with the CNT lengths as obtained from the electron microscopy. Copyright 2013 American Association for Aerosol Research
Nonadiabatic dynamics across a first-order quantum phase transition: Quantized bubble nucleation

Metastability is a quintessential feature of first order quantum phase transitions, which is lost either by dynamical instability or by nucleating bubbles of a true vacuum through quantum tunneling. By considering a drive across the first order quantum phase transition in the quantum Ising chain in the presence of both transverse and longitudinal fields, we reveal multiple regions in the parameter space where the initial metastable state loses its metastability in successive stages. The mechanism responsible is found to be semi-degenerate resonant tunnelings to states with specific bubble sizes. We show that such dynamics of quantized bubble nucleations can be understood in terms of Landau-Zener transitions, which provide quantitative predictions of nucleation probabilities for different bubble sizes.
Eilenberg Mac Lane spectra as p$p$ ‐cyclonic Thom spectra

Hopkins and Mahowald gave a simple description of the mod p$p$ Eilenberg Mac Lane spectrum Fp${\mathbb {F}}_p$ as the free E2$\mathbb {E}_2$ ‐algebra with an equivalence of p$p$ and 0. We show for each faithful 2‐dimensional representation λ$\lambda$ of a p$p$ ‐group G$G$ that the G$G$ ‐equivariant Eilenberg Mac Lane spectrum F̲p$\underline{\mathbb {F}}_p$ is the free Eλ$\mathbb {E}_{\lambda }$ ‐algebra with an equivalence of p$p$ and 0. This unifies and simplifies recent work of Behrens, Hahn, and Wilson, and extends it to include the dihedral 2‐subgroups of O(2)$\textnormal {O}(2)$ . The main new idea is that F̲p$\underline{\mathbb {F}}_p$ has a simple description as a p$p$ ‐cyclonic module over THH(Fp)$\mathop {\rm THH}\nolimits (\mathbb {F}_p)$ . We show that our result is the best possible one in that it gives all groups G$G$ and representations V$V$ such that F̲p$\underline{\mathbb {F}}_p$ is the free EV$\mathbb {E}_V$ ‐algebra with an equivalence of p$p$ and 0.
APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTI‐COUNTRY VARS

Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi-country datasets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this paper, we develop fast Bayesian methods for estimating PVARs using integrated rotated Gaussian approximations. We exploit the fact that domestic information is often more important than international information and group the coefficients accordingly. Fast approximations are used to estimate the latter while the former are estimated with precision using Markov chain Monte Carlo techniques. We illustrate, using a huge model of the world economy, that it produces competitive forecasts quickly.
Behavior From the Void: Unsupervised Active Pre-Training

We introduce a new unsupervised pre-training method for reinforcement learning called APT, which stands for Active Pre-Training. APT learns behaviors and representations by actively searching for novel states in reward-free environments. The key novel idea is to explore the environment by maximizing a non-parametric entropy computed in an abstract representation space, which avoids challenging density modeling and consequently allows our approach to scale much better in environments that have high-dimensional observations (e.g., image observations). We empirically evaluate APT by exposing task-specific reward after a long unsupervised pre-training phase. In Atari games, APT achieves human-level performance on 12 games and obtains highly competitive performance compared to canonical fully supervised RL algorithms. On DMControl suite, APT beats all baselines in terms of asymptotic performance and data efficiency and dramatically improves performance on tasks that are extremely difficult to train from scratch.
Set Representation Learning with Generalized Sliced-Wasserstein Embeddings

An increasing number of machine learning tasks deal with learning representations from set-structured data. Solutions to these problems involve the composition of permutation-equivariant modules (e.g., self-attention, or individual processing via feed-forward neural networks) and permutation-invariant modules (e.g., global average pooling, or pooling by multi-head attention). In this paper, we propose a geometrically-interpretable framework for learning representations from set-structured data, which is rooted in the optimal mass transportation problem. In particular, we treat elements of a set as samples from a probability measure and propose an exact Euclidean embedding for Generalized Sliced Wasserstein (GSW) distances to learn from set-structured data effectively. We evaluate our proposed framework on multiple supervised and unsupervised set learning tasks and demonstrate its superiority over state-of-the-art set representation learning approaches.
Inter-Carrier Interference Mitigation for Differentially Coherent Detection in Underwater Acoustic OFDM Systems

Suppressing the inter-carrier interference (ICI) is crucial for differentially coherent detection in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems due to the fact that the UWA channel is inherently violently Doppler-shifted. In this paper, we propose a new ICI suppression method, referred to as the partially-shifted fast Fourier transform (PS-FFT), which eliminates the ICI from both the time and frequency domains. Specifically, the PS-FFT first divides the received signal in the entire block duration into several short non-overlapping ones to reduce the channel variation in the time domain. It then applies the Fourier transform at several predefined frequencies to the received signal in each of these intervals to compensate Doppler shifts in the frequency domain. Finally, it weightedly combines the multiple demodulator outputs at each carrier as one output for symbol detection, with the combiner weights being solved by the stochastic gradient algorithm. Simulation results show that the PS-FFT dramatically outperforms the existing classical methods, the partial fast Fourier transform (P-FFT) and the fractional fast Fourier transform (F-FFT), for both medium and high Doppler factors and large carrier numbers in terms of the mean squared error (MSE). Numerically, the MSE of the PS-FFT is reduced by 61.83% – 84.89% compared to that of the F-FFT when the input signal-to-noise ratio (SNR) at the receiver ranges from 10 dB to 30 dB at a Doppler factor of 3 × 10−4 and a carrier number of 1024 where the P-FFT even cannot work.
Symplectic GARK methods for partitioned Hamiltonian systems

Generalized Additive Runge-Kutta schemes have shown to be a suitable tool for solving ordinary differential equations with additively partitioned right-hand sides. This work develops symplectic GARK schemes for additively partitioned Hamiltonian systems. In a general setting, we derive conditions for symplecticness, as well as symmetry and time-reversibility. We show how symplectic and symmetric schemes can be constructed based on schemes which are only symplectic, or only symmetric. Special attention is given to the special case of partitioned schemes for Hamiltonians split into multiple potential and kinetic energies. Finally we show how symplectic GARK schemes can leverage different time scales and evaluation costs for different potentials, and provide efficient numerical solutions by using different order for these parts.
Rheology of mobile sediment beds in laminar shear flow: effects of creep and polydispersity

Abstract Classical scaling relationships for rheological quantities such as the $\mu (J)$-rheology have become increasingly popular for closures of two-phase flow modelling. However, these frameworks have been derived for monodisperse particles. We aim to extend these considerations to sediment transport modelling by using a more realistic sediment composition. We investigate the rheological behaviour of sheared sediment beds composed of polydisperse spherical particles in a laminar Couette-type shear flow. The sediment beds consist of particles with a diameter size ratio of up to 10, which corresponds to grains ranging from fine to coarse sand. The data was generated using fully coupled, grain resolved direct numerical simulations using a combined lattice Boltzmann–discrete element method. These highly resolved data yield detailed depth-resolved profiles of the relevant physical quantities that determine the rheology, i.e. the local shear rate of the fluid, particle volume fraction, total shear and granular pressure. A comparison against experimental data shows excellent agreement for the monodisperse case. We improve upon the parameterization of the $\mu (J)$-rheology by expressing its empirically derived parameters as a function of the maximum particle volume fraction. Furthermore, we extend these considerations by exploring the creeping regime for viscous numbers much lower than used by previous studies to calibrate these correlations. Considering the low viscous numbers of our data, we found that the friction coefficient governing the quasi-static state in the creeping regime tends to a finite value for vanishing shear, which decreases the critical friction coefficient by a factor of three for all cases investigated.
Centrifugation of polyelectrolyte flocculated clay slurry

The effects of polyelectrolyte flocculation on centrifugal sedimentation and filtration stages of clay slurry were highlighted in this study. The moisture removal efficiency was enhanced by flocculation and increasing the rotational speed. Slurry conditioned to charge neutralization point exhibited the best dewaterability. For flocculated sludges, the sedimentation effect became too significant to eliminate the centrifugal filtration stage. Over 93% of the moisture removal was accomplished by simply allowing the filtrate to flow through a compacted filter cake. Optimal flocculant dose and rotational speed were found in the centrifugal tests.
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others. However, continuously executing the entire DNN on mobile devices can quickly deplete their battery. Although task offloading to cloud/edge servers may decrease the mobile device’s computational burden, erratic patterns in channel quality, network, and edge server load can lead to a significant delay in task execution. Recently, approaches based on split computing (SC) have been proposed, where the DNN is split into a head and a tail model, executed respectively on the mobile device and on the edge server. Ultimately, this may reduce bandwidth usage as well as energy consumption. Another approach, called early exiting (EE), trains models to embed multiple “exits” earlier in the architecture, each providing increasingly higher target accuracy. Therefore, the tradeoff between accuracy and delay can be tuned according to the current conditions or application demands. In this article, we provide a comprehensive survey of the state of the art in SC and EE strategies by presenting a comparison of the most relevant approaches. We conclude the article by providing a set of compelling research challenges.
Moments of Markovian growth–collapse processes

Abstract We apply general moment identities for Poisson stochastic integrals with random integrands to the computation of the moments of Markovian growth–collapse processes. This extends existing formulas for mean and variance available in the literature to closed-form moment expressions of all orders. In comparison with other methods based on differential equations, our approach yields explicit summations in terms of the time parameter. We also treat the case of the associated embedded chain, and provide recursive codes in Maple and Mathematica for the computation of moments and cumulants of any order with arbitrary cut-off moment sequences and jump size functions.
Global solutions for the generalized SQG equation and rearrangements

In this paper, we study the existence of rotating and traveling-wave solutions for the generalized surface quasi-geostrophic (gSQG) equation. The solutions are obtained by maximization of the energy over the set of rearrangements of a fixed function. The rotating solutions take the form of co-rotating vortices with N -fold symmetry. The traveling-wave solutions take the form of translating vortex pairs. Moreover, these solutions constitute the desingularization of co-rotating N point vortices and counter-rotating pairs. Some other quantitative properties are also established.