Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of Sequential Microwave-Ultrasound-Assisted Extraction for Maximum Recovery of Quercetin and Total Flavonoids from Red Onion (Allium cepa L.) Skin Wastes

This study aimed to optimize the extraction conditions in order to maximize the recovery yields of quercetin and total flavonoids from red onion skin wastes using sequential microwave-ultrasound-assisted extraction. Five effective factors on quercetin extraction yield were investigated using response surface methodology. The method was successfully performed under optimal conditions of 60 s microwave irradiation followed by 15 min sonication at 70 °C, 70% ethanol with solvent to solid ratio of 30 mL/g. Based on the optimization results, ultrasound temperature was found to be a highly significant and influential factor for the recovery. The maximum recovery yields of quercetin and total flavonoids from red onion skin were estimated to be 10.32 and 12.52%, respectively. The predicted values for quercetin (10.05%) and total flavonoids (12.72%) were very close to the experimental results. The recovery yields 2 obtained from different extraction methods were as follows: ultrasound-microwave-assisted extraction (7.66% quercetin and 10.18% total flavonoids), ultrasound-assisted extraction (5.36% quercetin and 8.34% total flavonoids), and microwave-assisted extraction (5.03% quercetin and 7.91 % total flavonoids). The validity of the projected model was examined by the obtained experimental data; in which, the validated model was suitable for the recovery of the valuable products from onion skin wastes for further scale-up in the food processes.
A Model using Agile Methodologies for Defining Metrics to be used by the Public Sector in Brazil to set Remuneration for Outsourced Software Development

The process of contracting software factorieswithin the scope of the Federal Public Administration (APF, inPortuguese) in Brazil has undergone some changes due tolegislative alterations and a model has been proposed to improvethe rendering of services regarding delivering results. Softwarefactory contracts based on predictive processes and on themetrics of Function Points (FP) are the target of criticisms andhave problems in achieving results, as verified in previousresearch. The initial objective of the study now proposed will beto define a process model for formulating metrics that can beused in agile contracts as opposed to those of standardized FPwhich have already proved to be quite problematic and difficultfor the contractor to manage. Thus, in line with the theme ofagile contracts for software development companies, the studyproposed will seek to better understand the problem of softwaredevelopment and its maintenance by public bodies by means ofcontracts using agile methodologies and appropriate metrics forremunerating these services.
Cosmological insights on the strong gravitational lens power law density profile

F. S. Lima3,∗ R. F. L. Holanda1,2,3,† S. H. Pereira4,‡ and W. J. C. da Silva1§ Departamento de F́ısica, Universidade Federal do Rio Grande do Norte, Natal Rio Grande do Norte, 59072-970, Brasil Departamento de F́ısica, Universidade Federal de Campina Grande, 58429-900, Campina Grande PB, Brasil Departamento de F́ısica, Universidade Federal de Sergipe, 49100-000, Aracaju SE, Brazil and 4 Departamento de F́ısica, Faculdade de Engenharia de Guaratinguetá, Universidade Estadual Paulista (UNESP), 12516-410, Guaratinguetá SP, Brasil
Deterministic quantum one-time pad via Fibonacci anyons

Cheng-Qian Xu 2 and D. L. Zhou 2, 3, 4, ∗ Institute of Physics, Beijing National Laboratory for Condensed Matter Physics, Chinese Academy of Sciences, Beijing 100190, China School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China Collaborative Innovation Center of Quantum Matter, Beijing 100190, China Songshan Lake Materials Laboratory, Dongguan, Guandong 523808, China (Dated: August 24, 2021)
Training Quantum Neural Networks on NISQ Devices

The advent of noisy intermediate-scale quantum (NISQ) devices offers crucial opportunities for the development of quantum algorithms. Here we evaluate the noise tolerance of two quantum neural network (QNN) architectures on IBM's NISQ devices, namely, dissipative QNN (DQNN) whose building-block perceptron is a completely positive map, and the quantum approximate optimization algorithm (QAOA). We compare these two approaches to learning an unknown unitary. While both networks succeed in this learning task, we find that a DQNN learns an unknown unitary more reliably than QAOA and is less susceptible to gate noise.
Fe DOPED TiO2 THIN FILMS COATED ON GLASS FIBER TO INHIBIT BACTERIAL OF E. COLI PREPARED BY SOL-GEL METHOD

Fe doped TiO2 thin films coatings on glass fiber have been prepared by the sol-gel method and calcined at 500 °C for 2 h with a heating rate of 10 °C/min. The thin films were characterized by XRD, SEM, AFM, photocatalytic activity was determined by means of degradation of methylene blue (MB) solution and antibacterial activity was evaluated by the inactivation of Escherichia coli (E. coli) bacteria. The antibacterial activity against E. coli, has been studied applying the so called antibacterial drop test. The bactericidal activity for the above bacteria cells was estimated by relative number of bacteria survived calculated from the number of viable cells which form colonies on the nutrient agar plates. The results show that the thin films exhibited a high antibacterial activity, which was enhanced with the increase of Fe dopant concentration. With the highest dopant concentration investigated in this experiment (TiO2 doped Fe with 5 mol% condition) the thin films show antibacterial activity of 97.78% under UV irradiation for 40 min.
Deep Deterministic Path Following

This paper deploys the Deep Deterministic Policy Gradient (DDPG) algorithm for longitudinal and lateral control of a simulated car to solve a path following task. The DDPG agent was implemented using PyTorch and trained and evaluated on a custom kinematic bicycle environment created in Python. The performance was evaluated by measuring cross-track error and velocity error, relative to a reference path. Results show how the agent can learn a policy allowing for small cross-track error, as well as adapting the acceleration to minimize the velocity error.
APOGEE view of the globular cluster NGC 6544

The second phase of the APOGEE survey is providing near-infrared, high-resolution, high signal-to-noise spectra of stars in the halo, disk, bar and bulge of the Milky Way. The near-infrared spectral window is especially important in the study of the Galactic bulge, where stars are obscured by the dust and gas of the disk in its line-of-sight. We present a chemical characterisation of the globular cluster NGC 6544 with high-resolution spectroscopy. The characterisation of the cluster chemical fingerprint, given its status of “interloper” towards the Galactic bulge and clear signatures of tidal disruption in its core is crucial for future chemical tagging efforts. Cluster members were selected from the DR16 of the APOGEE survey, using chemo-dynamical criteria of individual stars. A sample of 23 members of the cluster was selected. An analysis considering the intra-cluster abundance variations, known anticorrelations is given. According to the RGB content of the cluster, the iron content and α-enhancement are [Fe/H] =−1.44±0.04 dex and [ α / Fe ] = 0.20±0.04 dex, respectively. Cluster members show a significant spread in [Fe/H] and [Al/Fe] that is larger than expected based on measurement errors. An [Al/Fe] spread, signal of an Mg-Al anticorrelation is observed and used to constraint the cluster mass budget, along with C, N, Mg, Si, K, Ca, and Ce element variations are discussed. Across all the analysed evolutionary stages (RGB and AGB), about ∼ 2/3 (14 out of 23) show distinct chemical patterns, possibly associated with second-generation stars.
On Determinism of Game Engines Used for Simulation-Based Autonomous Vehicle Verification

Game engines are increasingly used as simulation platforms by the autonomous vehicle community to develop vehicle control systems and test environments. A key requirement for simulation-based development and verification is determinism, since a deterministic process will always produce the same output given the same initial conditions and event history. Thus, in a deterministic simulation environment, tests are rendered repeatable and yield simulation results that are trustworthy and straightforward to debug. However, game engines are seldom deterministic. This paper reviews and identifies the potential causes and effects of non-deterministic behaviours in game engines. A case study using CARLA, an open-source autonomous driving simulation environment powered by Unreal Engine, is presented to highlight its inherent shortcomings in providing sufficient precision in experimental results. Different configurations and utilisations of the software and hardware are explored to determine an operational domain where the simulation precision is sufficiently high i.e. variance between repeated executions becomes negligible for development and testing work. Finally, a method of a general nature is proposed, that can be used to find the domains of permissible variance in game engine simulations for any given system configuration.
Learning to recover orientations from projections in single-particle cryo-EM

A major challenge in single-particle cryo-electron microscopy (cryo-EM) is that the orientations adopted by the 3D particles prior to imaging are unknown; yet, this knowledge is essential for high-resolution reconstruction. We present a method to recover these orientations directly from the acquired set of 2D projections. Our approach consists of two steps: (i) the estimation of distances between pairs of projections, and (ii) the recovery of the orientation of each projection from these distances. In step (i), pairwise distances are estimated by a Siamese neural network trained on synthetic cryo-EM projections from resolved bio-structures. In step (ii), orientations are recovered by minimizing the difference between the distances estimated from the projections and the distances induced by the recovered orientations. We evaluated the method on synthetic cryo-EM datasets. Current results demonstrate that orientations can be accurately recovered from projections that are shifted and corrupted with a high level of noise. The accuracy of the recovery depends on the accuracy of the distance estimator. While not yet deployed in a real experimental setup, the proposed method offers a novel learning-based take on orientation recovery in SPA. Our code is available at https://github.com/JelenaBanjac/protein-reconstruction
Stealthy False Data Injection Attack Detection in Smart Grids with Uncertainties: A Deep Transfer Learning Based Approach

—Most traditional false data injection attack (FDIA) detection approaches rely on static system parameters or a single known snapshot of dynamic ones. However, such a setting signiﬁcantly weakens the practicality of these approaches when facing the fact that the system parameters are dynamic and cannot be accurately known during operation due to the presence of uncertainties in practical smart grids. In this paper, we propose an FDIA detection mechanism from the perspective of transfer learning. Speciﬁcally, the known initial/approximate system is treated as a source domain, which provides abundant simulated normal and attack data. The real world’s unknown running system is taken as a target domain where sufﬁcient real normal data are collected for tracking the latest system states online. The designed transfer strategy that aims at making full use of data in hand is divided into two optimization stages. In the ﬁrst stage, a deep neural network (DNN) is built by simultaneously optimizing several well-designed terms with both simulated data and real data, and then it is ﬁne-tuned via real data in the second stage. Several case studies on the IEEE 14-bus power system verify the effectiveness of the proposed mechanism.
Trimanipulation: Evaluation of human performance in a 3-handed coordination task

Many teleoperation tasks require three or more tools working together, which need the cooperation of multiple operators. The effectiveness of such schemes may be limited by communication issues between individuals. Trimanipulation by a single operator using an artificial third arm controlled together with their natural arms may address this issue. Foot-controlled interfaces have previously shown the capability to be used for the continuous control of robot arms. However, the use of such interfaces for controlling a supernumerary robotic limb in coordination with the natural limbs is not well understood. In this paper, a teleoperation task imitating physically-coupled hands in a virtual reality scene was conducted with 14 subjects to evaluate human performance during trimanipulation. The participants were required to move three limbs together in a coordinated way mimicking three arms holding a shared physical object. It was found that after a short practice session, three-hand trimanipulation with a single subject’s hands and foot was still slower than dyad operation. However, they displayed similar performance in their success rate and higher motion efficiency than two people cooperating.
Recurrent Equilibrium Networks: Unconstrained Learning of Stable and Robust Dynamical Models

This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical models for applications in machine learning and system identification. The new model class has "built in" guarantees of stability and robustness: all models in the class are contracting – a strong form of nonlinear stability – and models can have prescribed Lipschitz bounds. RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks, all deep feedforward neural networks, and all stable Wiener/Hammerstein models. RENs are parameterized directly by a vector in ℝN, i.e. stability and robustness are ensured without parameter constraints, which simplifies learning since generic methods for unconstrained optimization can be used. The performance of the robustness of the new model set is evaluated on benchmark nonlinear system identification problems.
Preparation of one 87Rb and one 133Cs atom in a single optical tweezer

We report the preparation of exactly one 87Rb atom and one 133Cs atom in the same optical tweezer as the essential first step towards the construction of a tweezer array of individually trapped 87Rb133Cs molecules. Through careful selection of the tweezer wavelengths, we show how to engineer species-selective trapping potentials suitable for high-fidelity preparation of Rb + Cs atom pairs. Using a wavelength of 814 nm to trap Rb and 938 nm to trap Cs, we achieve loading probabilities of 0.508(6) for Rb and 0.547(6) for Cs using standard red-detuned molasses cooling. Loading the traps sequentially yields exactly one Rb and one Cs atom in 28.4(6)% of experimental runs. Using a combination of an acousto-optic deflector and a piezo-controlled mirror to control the relative position of the tweezers, we merge the two tweezers, retaining the atom pair with a probability of 0.99(−0.02)(+0.01) . We use this capability to study hyperfine-state-dependent collisions of Rb and Cs in the combined tweezer and compare the measured two-body loss rates with coupled-channel quantum scattering calculations.
Fair and Differentially Private Distributed Frequency Estimation

In order to remain competitive, Internet companies collect and analyse user data for the purpose of improving user experiences. Frequency estimation is a widely used statistical tool which could potentially conflict with the relevant privacy regulations. Privacy preserving analytic methods based on differential privacy have been proposed, which either require a large user base or a trusted server; hence may give big companies an unfair advantage while handicapping smaller organizations in their growth opportunity. To address this issue, this paper proposes a fair privacy-preserving sampling-based frequency estimation method and provides a relation between its privacy guarantee, output accuracy, and number of participants. We designed decentralized privacy-preserving aggregation mechanisms using multi-party computation technique and established that, for a limited number of participants and a fixed privacy level, our mechanisms perform better than those that are based on traditional perturbation methods; hence, provide smaller companies a fair growth opportunity. We further propose an architectural model to support weighted aggregation in order to achieve higher accuracy estimate to cater for users with different privacy requirements. Compared to the unweighted aggregation, our method provides a more accurate estimate. Extensive experiments are conducted to show the effectiveness of the proposed methods.
Neuro-Symbolic VQA: A review from the perspective of AGI desiderata

An ultimate goal of the AI and ML fields is artificial general intelligence (AGI); although such systems remain science fiction, various models exhibit aspects of AGI. In this work, we look at neuro-symbolic (NS)approaches to visual question answering (VQA) from the perspective of AGI desiderata. We see how well these systems meet these desiderata, and how the desiderata often pull the scientist in opposing directions. It is my hope that through this work we can temper model evaluation on benchmarks with a discussion of the properties of these systems and their potential for future extension.
Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling

In this thesis, we address the data scarcity and limitations of linguistic theory by proposing language-agnostic multi-task training methods. First, we introduce a meta-learning-based approach, meta-transfer learning, in which information is judiciously extracted from high-resource monolingual speech data to the code-switching domain. The meta-transfer learning quickly adapts the model to the code-switching task from a number of monolingual tasks by learning to learn in a multi-task learning fashion. Second, we propose a novel multilingual meta-embeddings approach to effectively represent code-switching data by acquiring useful knowledge learned in other languages, learning the commonalities of closely related languages and leveraging lexical composition. The method is far more efficient compared to contextualized pre-trained multilingual models. Third, we introduce multi-task learning to integrate syntactic information as a transfer learning strategy to a language model and learn where to code-switch. To further alleviate the aforementioned issues, we propose a data augmentation method using Pointer-Gen, a neural network using a copy mechanism to teach the model the code-switch points from monolingual parallel sentences. We disentangle the need for linguistic theory, and the model captures code-switching points by attending to input words and aligning the parallel words, without requiring any word alignments or constituency parsers. More importantly, the model can be effectively used for languages that are syntactically different, and it outperforms the linguistic theory-based models.
Towards Better Understanding of User Authorization Query Problem via Multi-variable Complexity Analysis

User authorization queries in the context of role-based access control have attracted considerable interest in the past 15 years. Such queries are used to determine whether it is possible to allocate a set of roles to a user that enables the user to complete a task, in the sense that all the permissions required to complete the task are assigned to the roles in that set. Answering such a query, in general, must take into account a number of factors, including, but not limited to, the roles to which the user is assigned and constraints on the sets of roles that can be activated. Answering such a query is known to be NP-hard. The presence of multiple parameters and the need to find efficient and exact solutions to the problem suggest that a multi-variate approach will enable us to better understand the complexity of the user authorization query problem (UAQ). In this article, we establish a number of complexity results for UAQ. Specifically, we show the problem remains hard even when quite restrictive conditions are imposed on the structure of the problem. Our fixed-parameter tractable (FPT) results show that we have to use either a parameter with potentially quite large values or quite a restricted version of UAQ. Moreover, our second FPT algorithm is complex and requires sophisticated, state-of-the-art techniques. In short, our results show that it is unlikely that all variants of UAQ that arise in practice can be solved reasonably quickly in general.
A Non-sequential Approach to Deep User Interest Model for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction plays an important role in many industrial applications, and recently a lot of attention is paid to the deep interest models which use attention mechanism to capture user interests from historical behaviors. However, most current models are based on sequential models which truncate the behavior sequences by a fixed length, thus have difficulties in handling very long behavior sequences. Another big problem is that sequences with the same length can be quite different in terms of time, carrying completely different meanings. In this paper, we propose a non-sequential approach to tackle the above problems. Specifically, we first represent the behavior data in a sparse key-vector format, where the vector contains rich behavior info such as time, count and category. Next, we enhance the Deep Interest Network to take such rich information into account by a novel attention network. The sparse representation makes it practical to handle large scale long behavior sequences. Finally, we introduce a multidimensional partition framework to mine behavior interactions. The framework can partition data into custom designed time buckets to capture the interactions among information aggregated in different time buckets. Similarly, it can also partition the data into different categories and capture the interactions among them. Experiments are conducted on two public datasets: one is an advertising dataset and the other is a production recommender dataset. Our models outperform other state-of-the-art models on both datasets.
Qubit Sensing: A New Attack Model for Multi-programming Quantum Computing

Noisy quantum computers suffer from readout or measurement error. It is a classical bit-flip error due to which state"1"is read out as"0"and vice-versa. The probability of readout error shows a state dependence i.e., flipping probability of state"1"may differ from flipping probability of state"0". Moreover, the probability shows correlation across qubits. These state-dependent and correlated error probability introduces a signature of victim outputs on adversary output when two programs are run simultaneously on the same quantum computer. This can be exploited to sense victim output which may contain sensitive information. In this paper, we systematically show that such readout error-dependent signatures exist and that an adversary can use such signature to infer a user output. We experimentally demonstrate the attack (inference) on 3 public IBM quantum computers. Using Jensen-Shannon Distance (JSD) a measure for statistical inference, we show that our approach identifies victim output with an accuracy of 96% on real hardware. We also present randomized output flipping as a lightweight yet effective countermeasure to thwart such information leakage attacks. Our analysis shows the countermeasure incurs a minor penalty of 0.05% in terms of fidelity.