Miracle-dz/UniFAR-FTUs huggingface dataset:
CHARACTERIZATION OF THREE DIMENSIONAL SCAFFOLDS FROM LOCAL CHITOSAN/ALGINATE/GEOTHERMAL SILICA FOR POTENTIAL TISSUE ENGINEERING APPLICATIONS

Biocomposite scaffolds can be used to repair bone damage caused by accident or illness. As natural polymers, chitosan and alginate have been widely used for biocomposite applications. However, chitosan-based biocomposites have low mechanical strength. In Indonesia, geothermal power plants produce silica sludge as industrial waste containing around 50% amorphous silica. In this study, geothermal silica was purified and incorporated into chitosan-based scaffolds via a lyophilized method. In addition to Fourier transform infrared spectroscopy analysis, physical properties such as gelation, swelling ratio, and mechanical strength were analysed to assess the effects of the geothermal silica. The presence of silica was found to decrease the swelling ability in the biocomposites and increase mechanical strength. The highest mechanical strength was achieved at composition ratio of chitosan:alginate:geothermal silica = 1:1:1. Scanning electron microscopy analysis confirmed that the silica addition resulted in larger interconnected pores in scaffolds, which help increase cell infiltration and nutrient absorption for cell growth. Larger pores may have contributed to the decrease in swelling ability of the biocomposites while silica contributed to stronger structure under wet condition. The biocompsites did not show any indication of cytotoxicity on mammalian cell culture systems. Results of this study shed light on the potential of industrial waste as raw materials for tissue engineering applications.
A simple electronic medical record system designed for research

With extensive deployment of electronic medical record (EMR) systems, EMR usability remains a major source of frustration to clinicians. There is a significant need for a simple EMR software package that will enable investigators to study design and usability in a laboratory setting. We developed an open-source software package that implements the display functions of an EMR system. The user interface emphasizes the temporal display of data such as vital signs, medication administrations, and laboratory test results. It is well suited to support research about clinician information-seeking behaviors and adaptive user interfaces in terms of measures that include task accuracy, time to completion, and cognitive load. The Simple EMR System is freely available to the research community on GitHub.
Validation of a Knowledge Test in Turkish Patients on Warfarin Therapy at an Ambulatory Anticoagulation Clinic

Objectives This study aimed to evaluate the validity and reliability of an oral anticoagulation knowledge (OAK) test in Turkish patients on warfarin therapy at an anticoagulant outpatient clinic. Materials and Methods This study was conducted at an ambulatory anticoagulation clinic and included patients older than 18 years who had been using warfarin for at least six months. Patients' demographic and clinical data were collected. Internal consistency was calculated using the Kuder-Richardson 20 (KR-20) coefficient, and the test-retest reliability of the Turkish version of the OAK test was assessed. Results Patients' mean age was 59.83±11.93 (26-90) years (n=240; 133 women). The mean score of the OAK test was 14.19±3.01. The test-retest reliability of the scale (n=30) was moderate for the total score (p<0.001). The KR-20 value, a measure of internal consistency, was 0.671. Patients of a younger age and higher educational level were more likely to have higher levels of anticoagulation knowledge than patients of an older age and lower education level (p<0.05 for both comparisons). Conclusion The Turkish version of the OAK test can be used to determine the patients' knowledge on oral anticoagulation.
Theoretical concept of linear higher and algebraic equations

This paper contributes to the theoretical analysis of linear Differential Algebraic Equations of higher order as well as of the regularity and singularity of matrix polynomials. An algebraic number field is a finite extension field of the field of rational numbers. Within an algebraic number field is a ring of algebraic integers, which plays a role similar to the usual integers in the rational numbers.Some invariants and condensed forms under appropriate equivalent transformations are given for systems of linear higher-order Differential-Algebraic Equations’ with constant and variable coefficients. Inductively, based on condensed forms the original Differential-Algebraic Equations system can be transformed by differentiation-and-elimination steps into an equivalent strangeness-free system, from which the solution behaviour (including consistency of initial conditions and unique solvability) of the original DifferentialAlgebraic Equations system and related initial value problem can be directly read off. It is shown that the following equivalence holds for a Differential-Algebraic Equations system with strangeness-index ^ and square and constant coefficients. For any consistent initial condition and any right-hand side the associated initial value problem has a unique solution if and only if the matrix polynomial associated with the system is regular.
The modified interatomic potentials of FeS2 in atomistic simulations

The modified interatomic potentials were used for both energy minimization and molecular dynamics to study the surfaces and the bulk structure of pyrite. With energy minimization we calculated the surface energies of the surfaces {100}, {110}, {111} and {210}. They revealed that {100} surface is the most stable surface. When we compared the surface energies calculated from the original potentials and the adjusted potentials, it is clear that the adjusted potentials improve the stability of the surfaces. It was also revealed that water stabilizes the surfaces, since the surface energies decreases when hydrated. Molecular dynamics (MD) was used to evaluate the effect of temperature on the bulk structure.
OCT4 expression in human embryonic stem cells: spatio-temporal dynamics and fate transitions

The improved in vitro regulation of human embryonic stem cell (hESC) pluripotency and differentiation trajectories is required for their promising clinical applications. The temporal and spatial quantification of the molecular interactions controlling pluripotency is also necessary for the development of successful mathematical and computational models. Here we use time-lapse experimental data of OCT4-mCherry fluorescence intensity to quantify the temporal and spatial dynamics of the pluripotency transcription factor OCT4 in a growing hESC colony in the presence and absence of BMP4. We characterise the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis, quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 evolution for individual cells and pairs of their descendants. We find that OCT4 abundance in the daughter cells fluctuates sub-diffusively, showing anti-persistent self-regulation. We obtain the stationary probability distributions governing hESC transitions amongst the different cell states and establish the times at which pro-fate cells (which later give rise to pluripotent or differentiated cells) cluster in the colony. By quantifying the similarities between the OCT4 expression amongst neighbouring cells, we show that hESCs express similar OCT4 to cells within their local neighbourhood within the first two days of the experiment and before BMP4 treatment. Our framework allows us to quantify the relevant properties of proliferating hESC colonies and the procedure is widely applicable to other transcription factors and cell populations.
Bioefficacy of enhanced diatomaceous earth and botanical powders on the mortality and progeny production of Acanthoscelides obtectus (Coleoptera: Chrysomelidae), Sitophilus granarius (Coleoptera: Dryophthoridae) and Tribolium castaneum (Coleoptera: Tenebrionidae) in stored grain cereals

Food losses caused by insects during postharvest storage are of paramount economic importance worldwide, especially in Africa. Laboratory bioassays were conducted in stored grains to determine the toxicity of powders of Eugenia aromatica and Moringa oleifera alone or combined with enhanced diatomaceous earth (Probe-A® DE, 89.0% SiO2 and 5% silica aerogel) to adult Sitophilus granarius, Tribolium castaneum and Acanthoscelides obtectus. Adult mortality was observed up to 7 days, while progeny productionwas recorded at 6–10 weeks. LD50 and LT50 values for adult test insects exposed to plant powders and DE, showed that A. obtectus was the most susceptible towards the botanicals (LD50 0.179% and 0.088% wt/wt for E. aromatica and M. oleifera, respectively), followed by S. granarius. Tribolium castaneum was most tolerant (LD50 1.42% wt/wt and 1.40% wt/wt for E. aromatica and M. oleifera, respectively). The combined mixture of plant powders and DE controlled the beetles faster compared to the plant powders alone. LT50 ranged from 55.7 h to 62.5 h for T. castaneum exposed to 1.0% M. oleifera and 1.0% DE, and 0.5% E. aromatica and 1.0% DE, respectively. Botanicals caused significant reduction of F1 adults compared to the control. Combined action of botanical insecticides with DE as a grain protectant in an integrated pest management approach is discussed.
Uji toksisitas akut (LC50) limbah pengeboran minyak bumi terhadap Daphnia magna

Kegiatan eksplorasi minyak dan gas bumi dapat menghasilkan limbah yang berpotensi toksik bagi lingkungan perairan. Penelitian ini bertujuan untuk menghitung konsentrasi supernatan cutting yang dapat mematikan 50% biota uji dengan waktu pemaparan 96 jam. Percobaan menggunakan wadah kaca untuk menentukan nilai LC50 pada waktu pemaparan 24, 48, 72 dan 96 jam. Kegiatan penelitian dibagi menjadi dua tahapan, yaitu uji pendahuluan dan uji utama. Berdasarkan hasil penelitian, nilai LC50-96 jam dari supernatan cutting sebesar 58.241 ppm. Dengan demikian, sesuai PerMenESDM Nomor 45 Tahun 2006 bahwa limbah minyak bumi tersebut dapat dibuang langsung ke perairan, karena nilai LC50-96 jam >30.000 ppm. Semakin lama D. magna terpapar cutting, semakin banyak bagian tubuh D. magna yang mengalami kerusakan.
Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning

Stochastic approximation, a data-driven approach for finding the root of an unknown operator, provides a unified framework for solving many problems in stochastic optimization and reinforcement learning. Motivated by a growing interest in multi-agent and multi-task learning, we study a decentralized variant of stochastic approximation over a network of agents, where the goal is to find the root of the aggregate of the local operators at the agents. In this method, each agent implements a local stochastic approximation using noisy samples from its operator while averaging its iterates with the ones received from its neighbors. Our main contribution is to provide a finite-time analysis of the decentralized stochastic approximation method and to characterize the impacts of the underlying communication topology between agents. Our model for the data observed at each agent is that it is sampled from a Markov process; this lack of independence makes the iterates biased and (potentially) unbounded. Under mild assumptions we show that the convergence rate of the proposed method is essentially the same as if the samples were independent, differing only by a log factor that represents the mixing time of the Markov process. Finally, we present applications of the proposed method on a number of interesting learning problems in multi-agent systems, including distributed robust system identification and decentralized Q-learning for solving multitask reinforcement learning.
Impact of Pre-symptomatic Transmission on Epidemic Spreading in Contact Networks

Infectious diseases that incorporate pre-symptomatic transmission are challenging to monitor, model, predict and contain. We address this scenario by studying a variant of stochastic susceptible-exposed-infected-recovered (SEIR) model on arbitrary network instances using an analytical framework based on the method of dynamic message-passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks at a low computational cost compared to numerical simulation. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic. Our studies show quantitatively the deficiency of using the reproduction number as a measure for predicting the spreading rate in some topologies, how effective isolation reduces the need in strict social distancing measures, and the importance of mass testing in scenarios with long asymptomatic exposure since isolation of symptomatic individuals is insufficient to mitigate the spread.
DeepQ Stepper: A framework for reactive dynamic walking on uneven terrain

Reactive stepping and push recovery for biped robots is often restricted to flat terrains because of the difficulty in computing capture regions for nonlinear dynamic models. In this paper, we address this limitation by proposing a novel 3D reactive stepper, the DeepQ stepper, that can approximately learn the 3D capture regions of both simplified and full robot dynamic models using reinforcement learning, which can then be used to find optimal steps. The stepper can take into account the entire dynamics of the robot, ignored in most reactive steppers, leading to a significant improvement in performance. The DeepQ stepper can handle nonconvex terrain with obstacles, walk on restricted surfaces like stepping stones while tracking different velocities, and recover from external disturbances for a constant low computational cost.
Differentiable Channel Pruning Search

In this paper, we propose the differentiable channel pruning search (DCPS) of convolutional neural networks. Unlike traditional channel pruning algorithms which require users to manually set prune ratio for each convolutional layer, DCPS search the optimal combination of prune ratio that automatically. Inspired by the differentiable architecture search (DARTS), we draws lessons from the continuous relaxation and leverages the gradient information to balance the metrics and performance. However, directly applying the DARTS scheme will cause channel mismatching problem and huge memory consumption. Therefore, we introduce a novel weight sharing technique which can elegantly eliminate the shape mismatching problem with negligible additional resource. We test the proposed algorithm on image classification task and it achieves the state-of-the-art pruning results for image classification on CIFAR-10, CIFAR-100 and ImageNet. DCPS is further utilized for semantic segmentation on PASCAL VOC 2012 for two purposes. The first is to demonstrate that task-specific channel pruning achieves better performance against transferring slim models, and the second is to prove the memory efficiency of DCPS as the task demand more memory budget than classification. Results of the experiments validate the effectiveness and wide applicability of DCPS.
Commutators of weighted Hardy operator on weighted $\lambda$-central Morrey space

In this paper, the authors prove the boundedness of commutators generated by the weighted Hardy operator on weighted $\lambda$-central Morrey space with the weight $\omega$ satisfying the doubling condition. Moreover, the authors give the characterization for the weighted $\lambda$-central Campanato space by introducing a new kind of operator which is related to the commutator of weighted Hardy operator.
Learning to Represent Action Values as a Hypergraph on the Action Vertices

Action-value estimation is a critical component of many reinforcement learning (RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens of representation learning, good representations of both state and action can facilitate action-value estimation. While advances in deep learning have seamlessly driven progress in learning state representations, given the specificity of the notion of agency to RL, little attention has been paid to learning action representations. We conjecture that leveraging the combinatorial structure of multi-dimensional action spaces is a key ingredient for learning good representations of action. To test this, we set forth the action hypergraph networks framework---a class of functions for learning action representations with a relational inductive bias. Using this framework we realise an agent class based on a combination with deep Q-networks, which we dub hypergraph Q-networks. We show the effectiveness of our approach on a myriad of domains: illustrative prediction problems under minimal confounding effects, Atari 2600 games, and physical control benchmarks.
Hybrid Backpropagation Parallel Reservoir Networks

In many real-world applications, fully-differentiable RNNs such as LSTMs and GRUs have been widely deployed to solve time series learning tasks. These networks train via Backpropagation Through Time, which can work well in practice but involves a biologically unrealistic unrolling of the network in time for gradient updates, are computationally expensive, and can be hard to tune. A second paradigm, Reservoir Computing, keeps the recurrent weight matrix fixed and random. Here, we propose a novel hybrid network, which we call Hybrid Backpropagation Parallel Echo State Network (HBP-ESN) which combines the effectiveness of learning random temporal features of reservoirs with the readout power of a deep neural network with batch normalization. We demonstrate that our new network outperforms LSTMs and GRUs, including multi-layer "deep" versions of these networks, on two complex real-world multi-dimensional time series datasets: gesture recognition using skeleton keypoints from ChaLearn, and the DEAP dataset for emotion recognition from EEG measurements. We show also that the inclusion of a novel meta-ring structure, which we call HBP-ESN M-Ring, achieves similar performance to one large reservoir while decreasing the memory required by an order of magnitude. We thus offer this new hybrid reservoir deep learning paradigm as a new alternative direction for RNN learning of temporal or sequential data.
Geographic variation of Gloydius halys caucasicus (Serpentes: Viperidae) in Iran

We compared the morphological characters of populations of the Caucasian Pit Viper, Gloydius halys caucasicus, from different regions of the Alborz mountain range. For this purpose, we studied 7 metric and 21 meristic characters in 12 specimens from the western Alborz, 44 specimens from the eastern Alborz and nine specimens from the central Alborz Mountains. The results of the statistical analyses by npMANOVA, ANOVA and Kruskal Wallis tests showed significant differences between all three populations, while the western Alborz and the eastern Alborz populations are more distinct from each other. The important characters showing differences between them are: Subcaudals, dorsal scales in anterior part, dorsal scales in posterior part, supralabial scales, scales between gulars, gulars, ventrals, ratio of snout length over snout width, and ratio of snout length over head length.
Adversarial Dueling Bandits

We introduce the problem of regret minimization in Adversarial Dueling Bandits. As in classic Dueling Bandits, the learner has to repeatedly choose a pair of items and observe only a relative binary `win-loss' feedback for this pair, but here this feedback is generated from an arbitrary preference matrix, possibly chosen adversarially. Our main result is an algorithm whose $T$-round regret compared to the \emph{Borda-winner} from a set of $K$ items is $\tilde{O}(K^{1/3}T^{2/3})$, as well as a matching $\Omega(K^{1/3}T^{2/3})$ lower bound. We also prove a similar high probability regret bound. We further consider a simpler \emph{fixed-gap} adversarial setup, which bridges between two extreme preference feedback models for dueling bandits: stationary preferences and an arbitrary sequence of preferences. For the fixed-gap adversarial setup we give an $\smash{ \tilde{O}((K/\Delta^2)\log{T}) }$ regret algorithm, where $\Delta$ is the gap in Borda scores between the best item and all other items, and show a lower bound of $\Omega(K/\Delta^2)$ indicating that our dependence on the main problem parameters $K$ and $\Delta$ is tight (up to logarithmic factors).
Object Hider: Adversarial Patch Attack Against Object Detectors

Deep neural networks have been widely used in many computer vision tasks. However, it is proved that they are susceptible to small, imperceptible perturbations added to the input. Inputs with elaborately designed perturbations that can fool deep learning models are called adversarial examples, and they have drawn great concerns about the safety of deep neural networks. Object detection algorithms are designed to locate and classify objects in images or videos and they are the core of many computer vision tasks, which have great research value and wide applications. In this paper, we focus on adversarial attack on some state-of-the-art object detection models. As a practical alternative, we use adversarial patches for the attack. Two adversarial patch generation algorithms have been proposed: the heatmap-based algorithm and the consensus-based algorithm. The experiment results have shown that the proposed methods are highly effective, transferable and generic. Additionally, we have applied the proposed methods to competition "Adversarial Challenge on Object Detection" that is organized by Alibaba on the Tianchi platform and won top 7 in 1701 teams. Code is available at: this https URL
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles

Learning complex behaviors through interaction requires coordinated long-term planning. Random exploration and novelty search lack task-centric guidance and waste effort on non-informative interactions. Instead, decision making should target samples with the potential to optimize performance far into the future, while only reducing uncertainty where conducive to this objective. This paper presents latent optimistic value exploration (LOVE), a strategy that enables deep exploration through optimism in the face of uncertain long-term rewards. We combine finite horizon rollouts from a latent model with value function estimates to predict infinite horizon returns and recover associated uncertainty through ensembling. Policy training then proceeds on an upper confidence bound (UCB) objective to identify and select the interactions most promising to improve long-term performance. We apply LOVE to visual control tasks in continuous state-action spaces and demonstrate improved sample complexity on a selection of benchmarking tasks.
Inverse Optimal Control from Demonstration Segments

This paper develops an inverse optimal control method to learn an objective function from segments of demonstrations. Here, each segment is part of an optimal trajectory within any time interval of the horizon. The unknown objective function is parameterized as a weighted sum of given features with unknown weights. The proposed method shows that each trajectory segment can be transformed into a linear constraint to the unknown weights, and then all available segments are incrementally incorporated to solve for the unknown weights. Effectiveness of the proposed method is shown on a simulated 2-link robot arm and a 6-DoF maneuvering quadrotor system, in each of which only segment data of the systems' trajectories are available.