Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Thermal Transformation of Automotive Shredder Residues into High-Value Nano Silicon Carbide

Automotive waste represents both a global waste challenge and the loss of valuable embedded resources. This study provides a sustainable solution to utilise the mixed plastics of automotive waste residue (ASR) as a resource that will curtail the landfilling of hazardous waste and its adverse consequences to the environment. In this research, the selective thermal transformation has been utilised to produce nano silicon carbide (SiC) using mixed plastics and glass from automotive waste as raw materials. The composition and formation mechanisms of SiC nanoparticles have been investigated by X-ray diffraction (XRD), X-ray-Photoelectron Spectroscopy (XPS) and Transmission Electron Microscopy (TEM). The as synthesised SiC nanoparticles at 1500 °C has uniform spherical shapes with the diameters of the fixed edges of about 50–100 nm with a porous structure. This facile way of synthesising SiC nanomaterials would lay the foundations for transforming complex wastes into value-added, high-performing materials, delivering significant economic and environmental benefits.
Effect of Carom Seed Oil on the Antimicrobial, Physicochemical and Mechanical Properties of Starch Based Edible Film

Packaging material is necessary in the preservation process. Edible films containing essential oils can be incorporated into the conventional food packaging systems with a dual purpose, edible and natural preservative, that can maintain quality, extend the shelf life and reduce the risk of pathogen growth specifically in unprocessed or minimally processed foods like fruits and vegetables. In present study, pumpkin-arrowroot starch based edible film incorporated with carom seed oil at 0.5%, 1% and 1.5% were prepared and studied for the antimicrobial properties. Film with 1.5% Carom seed oil showed exceedingly good antimicrobial activities against E. coli, Staphylococcus and Aspergillus. The films were further studied for physical, mechanical and water vapour transmission properties. The results indicated that the film with 1.5% carom seed oil did not alter the mechanical properties of the film significantly, compared to control film and is ideal for coating to extend the shelf life of food products.
Development and evaluation of scaffold-based nanosponge formulation for controlled drug delivery of naproxen and ibuprofen

Purpose: To develop and evaluate nanosponge (NS) based sustained release formulations of naproxen (NAP) and ibuprofen (IBU). Method: Six formulations of each candidate drug were prepared by emulsion solvent diffusion method, using varying ratios of polymers, i.e., ethyl cellulose and polyvinyl alcohol. The prepared formulations were evaluated for various parameters including production yield, particle size, polydispersity index, actual drug content and entrapment efficiency. Morphological, structural and thermo-analytical evaluations were performed using various techniques. In vitro release studies were performed on selected formulations. Results: Nanosponge (NS) formulations of naproxen and ibuprofen were successfully prepared by emulsion solvent diffusion method. The particle size of naproxen and ibuprofen nanosponge formulations ranged from 347.6 to 1358 nm and 248.7 to 327.6 nm, respectively. Formulations with equal proportion of ethyl cellulose and drug resulted in nanosponges with the desired particle size. Production yield, actual drug content and entrapment efficiency was dependent on the ratio of ethyl cellulose and polyvinyl alcohol. Formulations with equal proportion showed least PDI values (0.09 for NAP and 0.07 for IBU) and highest zeta potential (-27.2 mV for NAP and -28.2 mV for IBU). Morphological, structural and thermo-analytical analysis confirmed the encapsulation of drugs in nanosponge cavities, and exhibited spherical and porous morphology. Nanosponge formulations gave a sustained release pattern, based on Higuchi model. Drug release mechanism was Fickian followed Korsmeyer-Peppas model, due probably to the porosity of the nanosponge. Conclusion: Sustained release nanosponge formulations of naproxen and ibuprofen have successfully been prepared. Keywords: Nanosponge, Naproxen, Ibuprofen, Emulsion solvent diffusion method, Sustained release
Optimal Tuning of the SMC Parameters for a Two-Link Manipulator Co-Simulation Control

This paper presents the trajectory tracking control of a two-link planar robot manipulator using MSC Adams and MATLAB co-simulation which enables the innovative virtual prototyping of the systems without any mathematical expressions. Firstly, the tracking control performance of the planar manipulator is investigated using the Sliding Mode Control (SMC) controller and the Proportional Integral Derivative (PID) controller in terms of the performance analysis. As a result, the SMC demonstrates effective control performances compared to the PID controller according to the required trajectory, settling time, and end position of the system. Then, the SMC controller parameters are determined using the different optimization methods offered as open source by MATLAB/Response Optimization Toolbox and compared to each other. In the virtual co-simulation, the trajectory tracking control performance is observed to be improved by optimizing the parameters of the SMC controller using Simplex Search (SS) method. All control results are examined and presented with graphics and international error standards.
The assessment of sulfonylcalix[4]arene derivatives as inhibitors of protein tyrosine phosphatases

Мета роботи. Порівняння похідних сульфонілкалікс[4]арену, які містять здатні та нездатні до іонізації залишки на верхньому вінці макроциклу, як інгібіторів РТР1В та інших протеїнотирозинфосфатаз. Результати та їх обговорення. Властивості сульфонілкалікс[4]арену з чотирма фосфонатними групами, приєднаними до верхнього вінця макроциклу, порівнювалися з властивостями сполук, які містять чотири трет-бутильні або трифторацетамідні замісники. Було встановлено, що сульфонілкалікс[4]арентетракіс-метилфосфонова кислота інгібує PTP1B зі значенням IC50 в низькомікромолярному діапазоні без селективності стосовно інших PTPаз, таких як TC-PTP, MEG1, MEG2, SHP2 та PTPβ. Модифікація сульфонілкалікс[4]арену залишками трифторацетаміду забезпечила інгібування PTP1B зі значенням IC50 1,4 мкМ з 4-28-кратною селективністю відносно інших PTPаз. Для з’ясування інгібувальної здатності похідної сульфонілкалікс[4]арену з трифторацетамідними замісниками відносно PTP1B було застосовано молекулярний докінг та моделювання методом молекулярної динаміки. Обговорюється можливий механізм інгібування. Експериментальна частина. Активність сполук досліджували спектрофотометрично, вимірюючи швидкість ферментативного гідролізу п-нітрофенілфосфату як субстрату протеїнотирозинфосфатаз. Молекулярний докінг було виконано за допомогою AutoDock Vina. Висновки. Це дослідження може бути основою нового підходу до розробки інгібіторів PTPаз шляхом модифікації верхнього вінця сульфонілкалікс[4]аренового каркасу неіоногенними замісниками.
Hypoparathyroidism

Maintenance of normal serum calcium levels involves the regulation of the flux of calcium between the intestinal tract, kidneys and bone. Calcium itself, PTH and 1,25-dihydroxyvitamin D3 all play a role in calcium regulation. 1,25-dihydroxyvitamin D3 facilitates intestinal calcium absorption, whilst both 1,25-dihydroxyvitamin D3 and PTH stimulate calcium release from bone. PTH also stimulates the conversion of 25-hydroxyvitamin D3 to 1,25-dihydroxyvitamin D3 enabling distal renal tubular calcium reabsorption. High concentrations of serum calcium inhibit PTH secretion while low concentrations stimulate it. Phosphate reabsorption from the kidney is reduced by PTH. Thus if PTH levels are low, serum phosphate will rise (more will be reabsorbed).
Atomic Layer Deposition and Properties of HfO2-Al2O3 Nanolaminates

Nanocrystalline HfO 2 :Al 2 O 3 mixture ﬁlms and nanolaminates were grown by atomic layer deposition at 350 ◦ C from metal chloride precursors and water. Formation of metastable HfO 2 polymorphs versus monoclinic phase was affected by the relative amount and thickness of constituent oxide layers. The ﬁlms exhibited saturative magnetization and charge polarization in externally applied ﬁelds at room temperature. The ﬁlms also demonstrated resistive switching behavior with considerable window between low and high resistance states.
Causal inference for multiple treatments using fractional factorial designs

We consider the design and analysis of multi‐factor experiments using fractional factorial and incomplete designs within the potential outcome framework. These designs are particularly useful when limited resources make running a full factorial design infeasible. We connect our design‐based methods to standard regression methods. We further motivate the usefulness of these designs in multi‐factor observational studies, where certain treatment combinations may be so rare that there are no measured outcomes in the observed data corresponding to them. Therefore, conceptualizing a hypothetical fractional factorial experiment instead of a full factorial experiment allows for appropriate analysis in those settings. We illustrate our approach using biomedical data from the 2003–2004 cycle of the National Health and Nutrition Examination Survey to examine the effects of four common pesticides on body mass index.
Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff

Low-dimensional structure of data can solve the adversarial robustness-accuracy conflict for machine learning systems. Modern machine learning systems have demonstrated breakthrough performance in a multitude of applications. However, they are known to be highly vulnerable to small perturbations to the input data, known as adversarial attacks. There are many well-documented examples of such behavior, for example small perturbations of an image, which is imperceptible to a human, can significantly degrade performance of modern classifiers. Adversarial training has been put forward as a way to improve robustness of learning algorithms to adversarial attacks. However, this benefit often comes at the cost of decreasing accuracy on natural unperturbed inputs, pointing to a potential conflict between adversarial robustness and standard accuracy. In “Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff,” Adel Javanmard and Mohammad Mehrabi develop a theory to show that when the data enjoys low-dimensional structure, then it is possible to train models that are nearly optimal with respect to both, the standard and robust accuracies.
Biomedical text summarization using Conditional Generative Adversarial Network(CGAN)

Text summarization in medicine can help doctors for reducing the time to access important information from countless documents. The paper offers a supervised extractive summarization method based on conditional generative adversarial networks using convolutional neural networks. Unlike previous models, which often use greedy methods to select sentences, we use a new approach for selecting sentences. Moreover, we provide a network for biomedical word embedding, which improves summarization. An essential contribution of the paper is introducing a new loss function for the discriminator, making the discriminator perform better. The proposed model achieves results comparable to the state-of-the-art approaches, as determined by the ROUGE metric. Experiments on the medical dataset show that the proposed method works on average 5% better than the competing models and is more similar to the reference summaries.
Learning Stable Vector Fields on Lie Groups

Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that learning adaptive, reactive, smooth, and stable vector fields is possible. However, these approaches define vector fields on a flat Euclidean manifold, while representing vector fields for orientations requires modeling the dynamics in non-Euclidean manifolds, such as Lie Groups. In this paper, we present a novel vector field model that can guarantee most of the properties of previous approaches i.e., stability, smoothness, and reactivity beyond the Euclidean space. In the experimental evaluation, we show the performance of our proposed vector field model to learn stable vector fields for full robot poses as SE(2) and SE(3) in both simulated and real robotics tasks.
Occlusion-Robust Object Pose Estimation with Holistic Representation

Practical object pose estimation demands robustness against occlusions to the target object. State-of-the-art (SOTA) object pose estimators take a two-stage approach, where the first stage predicts 2D landmarks using a deep network and the second stage solves for 6DOF pose from 2D-3D correspondences. Albeit widely adopted, such two-stage approaches could suffer from novel occlusions when generalising and weak landmark coherence due to disrupted features. To address these issues, we develop a novel occlude-and-blackout batch augmentation technique to learn occlusion-robust deep features, and a multi-precision supervision architecture to encourage holistic pose representation learning for accurate and coherent landmark predictions. We perform careful ablation tests to verify the impact of our innovations and compare our method to SOTA pose estimators. Without the need of any post-processing or refinement, our method exhibits superior performance on the LINEMOD dataset. On the YCB-Video dataset our method outperforms all non-refinement methods in terms of the ADD(-S) metric. We also demonstrate the high data-efficiency of our method. Our code is available at http://github.com/BoChenYS/ROPE
Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes

Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentation remains a challenging task due to the limited training data and the generalisation requirement for unseen classes. While recent progress has been particularly encouraging, we discover that existing methods tend to have poor performance in terms of meanIoU when query images contain other semantic classes besides the target class. To address this issue, we propose a novel self-supervised task that generates random pseudo-classes in the background of the query images, providing extra training data that would otherwise be unavailable when predicting individual target classes. To that end, we adopted superpixel segmentation for generating the pseudo-classes. With this extra supervision, we improved the meanIoU performance of the state-of-the-art method by 2.5% and 5.1% on the one-shot tasks, as well as 6.7% and 4.4% on the five-shot tasks, on the PASCAL-5i and COCO benchmarks, respectively.
A Meta Path based SME Credit Risk Measuring Method

Nowadays small and medium-sized enterprises have become an essential part of the national economy. With the increasing number of such enterprises, how to evaluate their credit risk becomes a hot issue. Unlike big enterprises with massive data to analyze, it is hard to ﬁnd enough information of small enterprises to assess their ﬁnancial status. Limited by the lack of primary data, how to inference small enterprises’s credit risk from secondary data, like information of their upstream, downstream, parent and subsidiary enterprises, attracts big attention from industry and academy. Targeting on accurately evaluating the credit risk of the small and medium-sized enterprise (SME), in this paper, we exploit the representative power of Information Network [5] on various kinds of SME entities and SME relationships to solve the problem. A novel feature named meta path feature proposed to measure the credit risk, which makes us able to evaluate the ﬁnancial status of SMEs from various perspectives. Experiments show that our method is eﬀective to identify SME with credit risks.
ESOD:Edge-based Task Scheduling for Object Detection

Object Detection on the mobile system is a challenge in terms of everything. Nowadays, many object detection models have been designed, and most of them concentrate on precision. However, the computation burden of those models on mobile systems is unacceptable. Researchers have designed some lightweight networks for mobiles by sacrificing precision. We present a novel edge-based task scheduling framework for object detection (termed as ESOD) . In detail, we train a DNN model (termed as pre-model) to predict which object detection model to use for the coming task and offloads to which edge servers by physical characteristics of the image task (e.g., brightness, saturation). The results show that ESOD can reduce latency and energy consumption by an average of 22.13% and 29.60% and improve the mAP to 45.8(with 0.9 mAP better), respectively, compared with the SOTA DETR model.
ModEL: A Modularized End-to-end Reinforcement Learning Framework for Autonomous Driving

—Heated debates continue over the best autonomous driving framework. The classic modular pipeline is widely adopted in the industry owing to its great interpretability and stability, whereas the end-to-end paradigm has demonstrated considerable simplicity and learnability along with the rise of deep learning. We introduce a new modularized end-to-end reinforcement learning framework (ModEL) for autonomous driving, which combines the merits of both previous approaches. The autonomous driving stack of ModEL is decomposed into perception, planning, and control module, leveraging scene understanding, end-to-end reinforcement learning, and PID control respectively. Furthermore, we build a fully functional autonomous vehicle to deploy this framework. Through extensive simulation and real-world experiments, our framework has shown great generalizability to various complicated scenarios and outperforms the competing baselines. 1
ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies

Preserving the privacy and security of big data in the context of cloud computing, while maintaining a certain level of efficiency of its processing remains to be a subject, open for improvement. One of the most popular applications epitomizing said concerns is found to be useful in genome analysis. This work proposes a secure multi-label tumor classification method using homomorphic encryption, whereby two different machine learning algorithms, SVM and XGBoost, are used to classify the encrypted genome data of different tumor types.
Selfish & opaque transaction ordering in the Bitcoin blockchain: the case for chain neutrality

Most public blockchain protocols, including the popular Bitcoin and Ethereum blockchains, do not formally specify the order in which miners should select transactions from the pool of pending (or uncommitted) transactions for inclusion in the blockchain. Over the years, informal conventions or "norms" for transaction ordering have, however, emerged via the use of shared software by miners, e.g., the GetBlockTemplate (GBT) mining protocol in Bitcoin Core. Today, a widely held view is that Bitcoin miners prioritize transactions based on their offered "transaction fee-per-byte. " Bitcoin users are, consequently, encouraged to increase the fees to accelerate the commitment of their transactions, particularly during periods of congestion. In this paper, we audit the Bitcoin blockchain and present statistically significant evidence of mining pools deviating from the norms to accelerate the commitment of transactions for which they have (i) a selfish or vested interest, or (ii) received dark-fee payments via opaque (non-public) side-channels. As blockchains are increasingly being used as a record-keeping substrate for a variety of decentralized (financial technology) systems, our findings call for an urgent discussion on defining neutrality norms that miners must adhere to when ordering transactions in the chains. Finally, we make our data sets and scripts publicly available.
The Equilibrium Hypothesis: Rethinking implicit regularization in Deep Neural Networks

Modern Deep Neural Networks (DNNs) exhibit impressive generalization properties on a variety of tasks without explicit regularization, suggesting the existence of hidden regularization eﬀects. Recent work by Baratin et al. (2021) sheds light on an intriguing implicit regularization eﬀect, showing that some layers are much more aligned with data labels than other layers. This suggests that as the network grows in depth and width, an implicit layer selection phenomenon occurs during training. In this work, we provide the ﬁrst explanation for this alignment hierarchy . We introduce and empirically validate the Equilibrium Hypothesis which states that the layers that achieve some balance between forward and backward information loss are the ones with the highest alignment to data labels. Our experiments demonstrate an excellent match with the theoretical predictions.
Learning Proposals for Practical Energy-Based Regression

Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression. However, energy-based regression requires a proposal distribution to be manually designed for training, and an initial estimate has to be provided at test-time. We address both of these issues by introducing a conceptually simple method to automatically learn an effective proposal distribution, which is parameterized by a separate network head. To this end, we derive a surprising result, leading to a unified training objective that jointly minimizes the KL divergence from the proposal to the EBM, and the negative log-likelihood of the EBM. At test-time, we can then employ importance sampling with the trained proposal to efficiently evaluate the learned EBM and produce stand-alone predictions. Furthermore, we utilize our derived training objective to learn mixture density networks (MDNs) with a jointly trained energy-based teacher, consistently outperforming conventional MDN training on four real-world regression tasks within computer vision. Code is available at https://github.com/fregu856/ebms_proposals.
An efficient FV-based Virtual Boundary Method for the simulation of fluid-solid interaction

In this work, the Immersed Boundary Method (IBM) with feedback forcing introduced by Goldstein et al. (1993) and often referred in the literature as the Virtual Boundary Method (VBM), is addressed. The VBM has been extensively applied both within a Spectral and a Finite Difference (FD) framework. Here, we propose to combine the VBM with a computationally efficient Finite Volume (FV) method. We will show that for similar computational configurations, FV and FD methods provide significantly different results. Furthermore, we propose to modify the standard feedback forcing scheme, based on a Proportional-Integral (PI) controller, with the introduction of a derivative action, in order to obtain a Proportial-Integral-Derivative (PID) controller. The stability analysis for the Backward Differentiation Formula of order 1 (BDF1) time scheme is modified accordingly, and extended to the Backward Differentiation Formula of order 2 (BDF2) time scheme. We will show that, for the BDF2 time scheme, the derivative action allows to improve the stability characteristics of the system. Our approach is validated against numerical data available in the literature for a stationary/rigidly moving 2D circular cylinder in several configurations. Finally, a Fluid-Structure Interaction (FSI) benchmark, related to the frequency response of a cantilever beam coupled with a fluid, is presented: we numerically demonstrate that the introduction of the derivative action plays an important role in order to properly detect the fluid-structure interaction coupling.