Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use Of Agricultural Waste In Solving Of Environmental Problems In Democratic Republic Of The Congo

— The presence of chromium in wastewaters produced from mining industries in Democratic Republic of the Congo is harmful for the environment. As a promising and polishing method for heavy metals removal from wastewaters, adsorption has been used with agricultural waste as low adsorbent. In this study, the use of sugar beet pulp has been investigated for the removal of hexavalent chromium from aqueous in batch adsorption mode. Adsorption experiments were conducted to obtain the optimal conditions combinations for the highest percent removal. Four parameters were studied such as contact time (30–180min), initial chromium concentration (10-50mg/L), pH (2-9) and solid to liquid ratio (0.25 g/100 mL-3 g/100 mL). However, the agitation rate was equal to 100 rpm and the room temperature was considered. The highest percent removals of 90.53% for 10 ppm concentration and 76.34% for 50 ppm concentration were achieved at the following conditions: pH 2, contact time 90 min, S/L ratios of 2 g/100 mL and 3 g/100 mL respectively. Langmuir and Freundlich adsorption isotherms were used for the mathematical description of adsorption equilibrium. Freundlich isotherm fitted well the equilibrium data. The maximum capacity (Q max ) from the Langmuir isotherm was found to be 11.299 mg/g. These results indicated that sugar beet pulp can be used as low cost adsorbent for the removal of chromium from mining industries wastewater to avoid environmental problems.
Magnetic field distributions around superconducting strips on ferromagnetic substrates

The complex-field approach is developed to derive analytical expressions of the magnetic field distributions around superconducting strips on ferromagnetic substrates (SC/FM strips). We consider the ferromagnetic substrates as ideal soft magnets with an infinite magnetic permeability, neglecting the ferromagnetic hysteresis. On the basis of the critical state model for a superconducting strip, the ac susceptibility $\chi_1'+i\chi_1''$ of a SC/FM strip exposed to a perpendicular ac magnetic field is theoretically investigated, and the results are compared with those for superconducting strips on nonmagnetic substrates (SC/NM strips). The real part $\chi_1'$ for $H_0/j_cd_s\to 0$ (where $H_0$ is the amplitude of the ac magnetic field, $j_c$ is the critical current density, and $d_s$ is the thickness of the superconducting strip) of a SC/FM strip is 3/4 of that of a SC/NM strip. The imaginary part $\chi_1''$ (or ac loss $Q$) for $H_0/j_cd_s<0.14$ of a SC/FM strip is larger than that of a SC/NM strip, even when the ferromagnetic hysteresis is neglected, and this enhancement of $\chi_1''$ (or $Q$) is due to the edge effect of the ferromagnetic substrate.
A Backward Regressed Capsule Neural Network for Plant Leaf Disease Detection

: This study investigated the introduction of backward regression coupled with DenseNet features in a Capsule Neural Network (CapsNet) for plant leaf disease classification. Plant diseases are considered one of the main factors influencing food production and therefore fast crop disease detection and recognition are important in enhancing food security interventions. CapsNets have successfully been adopted for plant leaf disease classification however, backpropagation of signals to preceding layers is still a challenge due to low gradient flow. In addition, parameter and computational complexities exist due to complex features. Therefore, this study implemented a loop connectivity pattern to improve gradient flow in the convolution layer and backward regression for feature selection. We observed a 99.7% F1 score with backward regression and 87% F1 score without backward regression accuracy on testing our framework based on the standard Plant Village (PV) dataset comprising ten tomato classes with 9080 images. Additionally, CapsNet with backward regression showed relatively higher and stable accuracy when sensitivity analysis was performed by varying testing and training dataset percentages. In comparison Support Vector Machines (SVM), Artificial Neural Networks (ANN), AlexNet, ResNet, VGGNet, Inception V3, and VGG 16 deep learning approaches scored 84.5, 88.6, 99.3, 97.87, 99.14, and 98.2%, respectively. These findings indicate that the introduction of backward regression of features in the CapsNet model may be a decent and, in most cases superior and less expensive alternative for phrase categorization models based on CNNs and RNNs. Therefore, the accuracy of plant disease detection may be enhanced even further with the aid of the fusion of several classifiers and the integration of a backward regressed capsule neural network.
Entanglement at the interplay between single- and many-bodyness

The tensor network representation of the ground state of a Bethe chain is analytically obtained and studied in relation to its entanglement distribution. Block entanglement displays a maximum at the interplay between single- and many-bodyness. In systems of two fermions, tensor networks describing states with substantial many-body entropy cannot be written as a sequence of next-neighbor unitaries applied on an uncorrelated state, but need four-next-neighbor unitaries in addition. This differs from the idea that ground states can be obtained as a sequence of next-neighbor operations applied on an initial tensor network lacking many-body correlations. The work uncovers the transcendence of the notion of many-bodyness in the implementation of protocols based on matrix product states.
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System

This work tackles a central machine learning problem of performance degradation on out-of-distribution (OOD) test sets. The problem is particularly salient in medical imaging based diagnosis system that appears to be accurate but fails when tested in new hospitals/datasets. Recent studies indicate the system might learn shortcut and non-relevant features instead of generalizable features, so-called good features. We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets. With that, we formulate a novel model training scheme for the deep neural network to learn good features for classification and/or detection tasks ensuring a consistent generalization performance on OOD test sets. The experimental results qualitatively and quantitatively demonstrate the superior performance of our method using the benchmark CXR image data sets on classification tasks.
Impacts of census differential privacy for small-area disease mapping to monitor health inequities

The U.S. Census Bureau will implement a modernized privacy-preserving disclosure avoidance system (DAS), which includes application of differential privacy, on publicly released 2020 census data. There are concerns that the DAS may bias small-area and demographically stratified population counts, which play a critical role in public health research, serving as denominators in estimation of disease/mortality rates. Using three DAS demonstration products, we quantify errors attributable to reliance on DAS-protected denominators in standard small-area disease mapping models for characterizing health inequities. We conduct simulation studies and real data analyses of inequities in premature mortality at the census tract level in Massachusetts and Georgia. Results show that overall patterns of inequity by racialized group and economic deprivation level are not compromised by the DAS. While early versions of DAS induce errors in mortality rate estimation that are larger for Black than non-Hispanic white populations in Massachusetts, this issue is ameliorated in newer DAS versions.
A regionalization method based on a cluster probability model

A regionalization method based on a cluster probability model (a mixed multivariate Gaussian model) is proposed for grouping sites into nonoverlapping contiguous homogeneous regions defined by a Voronoi tessellation (Theisson polygons). The cluster probability model is applied to second‐order standardized annual sample properties (mean, coefficient of variation, and autocorrelation) evaluated at the daily level of aggregation taken from each of 234 daily rainfall records with positions located in the Basque Country, Spain. Using the Bayesian information criterion, four clusters of sites are identified (which do not fall into contiguous regions). The distances between all neighboring pairs of sites connected by edges from the Delaunay planar graph are found. The probability that a site belongs to each of the four clusters is extracted from the fitted Gaussian model and multiplied into the probability that the neighboring site belongs to the same cluster. These products are divided by the squared distance between the sites and are summed to give an overall measure of a site belonging to a cluster that takes into account the classification of neighboring sites. Regions from the Voronoi tessellation of the points are classed on the basis of this measure and according to whether they are spatially isolated from other regions of the same class. Points that have the least influence on the variance of residual errors of the fitted model are found using a criterion based on Wilks' lambda for multivariate analysis of variance, and the classes of the least influential points are adjusted to ensure the overall regions are contiguous.
Estimating Racial Disparities in Emergency General Surgery

Research documents that Black patients experience worse general surgery outcomes than white patients in the United States. In this paper, we focus on an important but less-examined category: the surgical treatment of emergency general surgery (EGS) conditions, which refers to medical emergencies where the injury is"endogenous,"such as a burst appendix. Our goal is to assess racial disparities for common outcomes after EGS treatment using an administrative database of hospital claims in New York, Florida, and Pennsylvania, and to understand the extent to which differences are attributable to patient-level risk factors versus hospital-level factors. To do so, we use a class of linear weighting estimators that re-weight white patients to have a similar distribution of baseline characteristics as Black patients. This framework nests many common approaches, including matching and linear regression, but offers important advantages over these methods in terms of controlling imbalance between groups, minimizing extrapolation, and reducing computation time. Applying this approach to the claims data, we find that disparities estimates that adjust for the admitting hospital are substantially smaller than estimates that adjust for patient baseline characteristics only, suggesting that hospital-specific factors are important drivers of racial disparities in EGS outcomes.
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health

In this paper, we consider a risk-averse multi-armed bandit (MAB) problem where the goal is to learn a policy that minimizes the risk of low expected return, as opposed to maximizing the expected return itself, which is the objective in the usual approach to risk-neutral MAB. Specifically, we formulate this problem as a transfer learning problem between an expert and a learner agent in the presence of contexts that are only observable by the expert but not by the learner. Thus, such contexts are unobserved confounders (UCs) from the learner’s perspective. Given a dataset generated by the expert that excludes the UCs, the goal for the learner is to identify the true minimum-risk arm with fewer online learning steps, while avoiding possible biased decisions due to the presence of UCs in the expert’s data. To achieve this, we first formulate a mixed-integer linear program that uses the expert data to obtain causal bounds on the Conditional Value at Risk (CVaR) of the true return for all possible UCs. We then transfer these causal bounds to the learner by formulating a causal bound constrained Upper Confidence Bound (UCB) algorithm to reduce the variance of online exploration and, as a result, identify the true minimum-risk arm faster, with fewer new samples. We provide a regret analysis of our proposed method and show that it can achieve zero or constant regret. Finally, we use an emotion regulation in mobile health example to show that our proposed method outperforms risk-averse MAB methods without causal bounds.
Effects of the local chemical environment on vacancy diffusion in multi-principal element alloys

Multi-principal element alloys (MPEAs) are exciting systems showing remarkable properties compared to conventional materials due to their exceedingly large compositional space and spatially varying chemical environment. However, predicting fundamental properties from the local chemical environment is challenging due to the large scale of the problem. To investigate this fundamental problem, we employ a combination of atomistic simulations (using ab-initio and molecular dynamics) and convolutional neural networks (CNNs) to evaluate point defect and migration energies in an equimolar CoFeCrNi MPEA. We show how energies of point defects can be predicted with reasonable accuracy using a small subset of local chemical environments. Using the CNNs, we develop a lattice Monte Carlo simulation that computes the migration path and diffusivities of vacancies. Remarkably, our work illustrates how the local chemical environment leads rise to a distribution function of the point defect energies, which is responsible for the macroscopic diffusivity of vacancies. In particular, we observed that vacancies get trapped in super basins surrounded by large migration and connected with low migration energy states. As a result, vacancy diffusivity is highly dependent on the environment and could change several orders of magnitude for a given temperature. Our works illustrate the importance of understanding properties in MPEAs depending on the local chemical environment and the ability of CNN to provide a model for computing energies in high-dimensional spaces, which can be used to scale things up to higher-order models.
EDeNN: Event Decay Neural Networks for low latency vision

Despite the success of neural networks in computer vision tasks, digital 'neurons' are a very loose approximation of biological neurons. Today's learning approaches are designed to function on digital devices with digital data representations such as image frames. In contrast, biological vision systems are generally much more capable and efficient than state-of-the-art digital computer vision algorithms. Event cameras are an emerging sensor technology which imitates biological vision with asynchronously firing pixels, eschewing the concept of the image frame. To leverage modern learning techniques, many event-based algorithms are forced to accumulate events back to image frames, somewhat squandering the advantages of event cameras. We follow the opposite paradigm and develop a new type of neural network which operates closer to the original event data stream. We demonstrate state-of-the-art performance in angular velocity regression and competitive optical flow estimation, while avoiding difficulties related to training SNN. Furthermore, the processing latency of our proposed approach is less than 1/10 any other implementation, while continuous inference increases this improvement by another order of magnitude.
Majority Vote for Distributed Differentially Private Sign Selection

Privacy-preserving data analysis has become prevailing in recent years. In this paper, we propose a distributed group diﬀerentially private majority vote mechanism for the sign selection problem in a distributed setup. To achieve this, we apply the iterative peeling to the stability function and use the exponential mechanism to recover the signs. As applications, we study the private sign selection for mean estimation and linear regression problems in distributed systems. Our method recovers the support and signs with the optimal signal-to-noise ratio as in the non-private scenario, which is better than contemporary works of private variable selections. Moreover, the sign selection consistency is justiﬁed with theoretical guarantees. Simulation studies are conducted to demonstrate the eﬀectiveness of our proposed method.
Modeling Power Consumptions for Multirotor UAVs

Unmanned aerial vehicles (UAVs) have various advantages, but suffer from their limited energy in practical applications. Therefore, it is important to establish reasonable power consumption models for further efficient power. However, most existing works either establish theoretical power consumption models for fixed-wing UAVs and single-rotor UAVs or provide heuristic power consumption models for multirotor UAVs without rigorous mathematical derivations. This article aims to establish theoretical power consumption models for multirotor UAVs. To be specific, the closed-form power consumption models for a multirotor UAV in three flight statuses, i.e., forward flight, vertical ascent, and vertical descent, are derived by leveraging the relationship between single-rotor UAVs and multirotor UAVs in terms of power consumptions. On this basis, a generic flight power consumption model for the UAV in a 3-D scenario is obtained. Extensive experiments are conducted in real scenarios by using DJI M210 and a mobile app developed based on DJI Mobile SDK, and confirm the correctness and effectiveness of these models; in addition, power consumption model-based simulations are performed to further investigate the effect of the number of rotors and weight of payload on UAV power consumptions. The proposed power consumption models not only reveal how power consumptions of multirotor UAVs are affected by various factors, but also pave the way for introducing other applications.
Monotonic and Non-Monotonic Epidemiological Models on Networks

Contact networks can significantly change the course of epidemics, affecting the rate of new infections and the mean size of an outbreak. Despite this dependence, some characteristics of epidemics are not contingent on the contact network and are probably predictable based only on the pathogen. Here we consider SIR-like pathogens and give an elementary proof that for any network increasing the probability of transmission increases the mean outbreak size. We also introduce a simple model, termed 2FleeSIR, in which susceptibles protect themselves by avoiding contacts with infectees. The 2FleeSIR model is non-monotonic: for some networks, increasing transmissibility actually decreases the final extent. The dynamics of 2FleeSIR are fundamentally different from SIR because 2FleeSIR exhibits no outbreak transition in densely-connected networks. We show that in non-monotonic epidemics, public health officials might be able to intervene in a fundamentally new way to change the network so as to control the effect of unexpectedly-high virulence. However, interventions that decrease transmissibility might actually cause more people to become infected.
TEACH: Temporal Action Composition for 3D Humans

Given a series of natural language descriptions, our task is to generate 3D human motions that correspond semantically to the text, and follow the temporal order of the instructions. In particular, our goal is to enable the synthesis of a series of actions, which we refer to as temporal action composition. The current state of the art in text-conditioned motion synthesis only takes a single action or a single sentence as input. This is partially due to lack of suitable training data containing action sequences, but also due to the computational complexity of their non-autoregressive model formulation, which does not scale well to long sequences. In this work, we address both issues. First, we exploit the recent BABEL motion-text collection, which has a wide range of labeled actions, many of which occur in a sequence with transitions between them. Next, we design a Transformer-based approach that operates non-autoregressively within an action, but autoregressively within the sequence of actions. This hierarchical formulation proves effective in our experiments when compared with multiple baselines. Our approach, called TEACH for “TEmporal Action Compositions for Human motions ”, produces realistic human motions for a wide variety of actions and temporal compositions from language descriptions. To encourage work on this new task, we make our code available for research purposes at teach.is. tue.mpg. de.
Retinal Image Restoration and Vessel Segmentation using Modified Cycle-CBAM and CBAM-UNet

Clinical screening with low-quality fundus images is challenging and significantly leads to misdiagnosis. This paper addresses the issue of improving the retinal image quality and vessel segmentation through retinal image restoration. More specifically, a cycle-consistent generative adversarial network (CycleGAN) with a convolution block attention module (CBAM) is used for retinal image restoration. A modified UNet is used for retinal vessel segmentation for the restored retinal images (CBAM-UNet). The proposed model consists of two generators and two discriminators. Generators translate images from one domain to another, i.e., from low to high quality and vice versa. Discriminators classify generated and original images. The retinal vessel segmentation model uses downsampling, bottlenecking, and upsampling layers to generate segmented images. The CBAM has been used to enhance the feature extraction of these models. The proposed method does not require paired image datasets, which are challenging to produce. Instead, it uses unpaired data that consists of low- and high-quality fundus images retrieved from publicly available datasets. The restoration performance of the proposed method was evaluated using full-reference evaluation metrics, e.g., peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The retinal vessel segmentation performance was compared with the ground-truth fundus images. The proposed method can significantly reduce the degradation effects caused by out-of-focus blurring, color distortion, low, high, and uneven illumination. Experimental results show the effectiveness of the proposed method for retinal image restoration and vessel segmentation.
Steady helix states in a resonant XXZ Heisenberg model with Dzyaloshinskii-Moriya interaction

We systematically investigate possible helix states in XXZ Heisenberg model with Dzyaloshinskii-Moriya (DM) interaction. Exact solutions show that a set of precession helix states can be constructed by deliberate superposition of degenerate eigenstates of the Hamiltonian under the resonant condition. When a non-Hermitian balance boundary term is imposed as a quenching action, the quench dynamics shows that a steady helix state emerges from some easily prepared initial states, including saturate and maximally mixed ferromagnetic states, according to the analysis of perturbation method. The corresponding dynamics for near resonant cases is also investigated numerically, indicating the robustness of the scheme. Our findings highlight the cooperation of non-Hermiticity and the DM interaction in quantum spin system, suggesting a way for preparing steady helix state in non-Hermitian quantum spin system.