Miracle-dz/UniFAR-FTUs huggingface dataset:
Sec bacterial outer-membrane

The outer-membrane of Gram-negative bacteria is critical for surface adhesion, pathogenicity, antibiotic resistance and survival. The major constituent – hydrophobic b -barrel O uter- M embrane P roteins (OMPs) – are first secreted across the inner-membrane through the Sec-translocon for delivery to periplasmic chaperones, for example SurA, which prevent aggregation. OMPs are then offloaded to the b - B arrel A ssembly M achinery (BAM) in the outer-membrane for insertion and folding. We show the H olo- T rans L ocon (HTL) – an assembly of the protein-channel core-complex SecYEG, the ancillary sub-complex SecDF, and the membrane ‘insertase’ YidC – contacts BAM through periplasmic domains of SecDF and YidC, ensuring efficient OMP maturation. Furthermore, the proton-motive force (PMF) across the inner-membrane acts at distinct stages of protein secretion: (1) SecA-driven translocation through SecYEG and (2) communication of conformational changes via SecDF across the periplasm to BAM. The latter presumably drives efficient passage of OMPs. These interactions provide insights of inter-membrane organisation and communication, the importance of which is becoming increasingly apparent. study explores the role of the ancillary components of the Sec machinery for protein secretion, and for downstream trafficking through the periplasm for delivery to the outer-membrane and OMP maturation. In particular, we examine the possibility of a direct interaction between the HTL and BAM machineries to facilitate protein transport through the envelope. The basic properties and structure of the inter-membrane super-complex are investigated, as well as its importance for OMP folding and insertion. The implications of this interaction and its modulation caused by proton transport through SecDF are profound. Thus, we consider their consequences for the mechanism of protein transport through the Sec and BAM machineries, and for outer-membrane biogenesis.
Schaum's Outline of Mathematical Methods for Business and Economics

Schaum's Outline of Mathematica Methods for Business and Economics 1. Review 2. Equations and Graphs 3. Functions 4. Systems of Equations 5. Linear (or Matrix) Algebra 6. Solving Linear Equations with Matrix Algebra 7. Linear Programming: Using Graphs 8. Linear Programming: The Simplex Algorithm and the Dual 9. Differential Calculus: The Derivative and the Rules of Differentiation 10. Differential Calculus: Uses of the Derivative 11. Exponential and Logarithmic Functions 12. Integral Calculus 13. Calculus of Multivariable Functions
Novel Bionanocellulose/κ-Carrageenan Composites for Tissue Engineering

In this work, novel bacterial cellulose/κ-carrageenan (BNC/κ-Car) composites, being potential scaffolds for tissue engineering (TE), and outperforming the two polymers when used as scaffolds separately, were for the first time obtained using an in situ method, based on the stationary culture of bacteria Komagateibacter xylinus E25. The composites were compared with native BNC in terms of the morphology of fibers, chemical composition, crystallinity, tensile and compression strength, water holding capacity, water retention ratio and swelling properties. Murine chondrogenic ATDC5 cells were applied to assess the utility of the BNC/κ-Car composites as potential scaffolds. The impact of the composites on the cells viability, chondrogenic differentiation, and expression patterns of Col1α1, Col2α1, Runx2, and Sox9, which are indicative of ATDC5 chondrogenic differentiation, was determined. None of the composites obtained in this study caused the chondrocyte hypertrophy. All of them supported the differentiation of ATDC5 cells to more chondrogenic phenotype.
Live forensics on the Windows 10 secure kernel.

The field of Digital Forensics is always changing together with developments in computer hardware and operating systems. In June of 2015 Windows 10 was released to consumers offering numerous changes to the operating system. One of the most interesting features from the perspective of Digital Forensics is a feature known as Device Guard. Device Guard is said to offer protection from advanced malware such as rootkits, polymorphic viruses and even zero day exploits. Device Guard accomplishes this by introducing VBS based security which introduces a new secure kernel that is separate from the normal kernel. This thesis takes a look at the internals of the secure kernel and the trustlets running within. This thesis also discusses different methods on how to best acquire a full live memory dump as well as making a program that can analyze it.
A Multi-Objective Optimization Framework for Multi-Stakeholder Fairness-Aware Recommendation

Nowadays, most online services are hosted on multi-stakeholder marketplaces, where consumers and producers may have different objectives. Conventional recommendation systems, however, mainly focus on maximizing consumers’ satisfaction by recommending the most relevant items to each individual. This may result in unfair exposure of items, thus jeopardizing producer benefits. Additionally, they do not care whether consumers from diverse demographic groups are equally satisfied. To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee. We first propose four fairness constraints on consumers and producers. In order to train the whole framework in an end-to-end way, we utilize the smooth rank and stochastic ranking policy to make these fairness criteria differentiable and friendly to back-propagation. Then, we adopt the multiple gradient descent algorithm to generate a Pareto set of solutions, from which the most appropriate one is selected by the Least Misery Strategy. The experimental results demonstrate that Multi-FR largely improves recommendation fairness on multiple stakeholders over the state-of-the-art approaches while maintaining almost the same recommendation accuracy. The training efficiency study confirms our model’s ability to simultaneously optimize different fairness constraints for many stakeholders efficiently.
Graph neural networks for the prediction of molecular structure-property relationships

Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quantitative structure-property/activity relationships (QSPRs/QSARs) characterize molecules by descriptors which are then mapped to the properties of interest via a linear or nonlinear model. In contrast, graph neural networks, a novel machine learning method, directly work on the molecular graph, i.e., a graph representation where atoms correspond to nodes and bonds correspond to edges. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors as in QSPRs/QSARs. GNNs have been shown to achieve state-of-the-art prediction performance on various property predictions tasks and represent an active field of research. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition

Named entity recognition (NER) is the task to detect and classify entity spans in the text. When entity spans overlap between each other, the task is named as nested NER. Span-based methods have been widely used to tackle nested NER. Most of these methods get a score matrix, where each entry corresponds to a span. However, previous work ignores spatial relations in the score matrix. In this paper, we propose using Convolutional Neural Network (CNN) to model these spatial relations. Despite being simple, experiments in three commonly used nested NER datasets show that our model surpasses several recently proposed methods with the same pre-trained encoders. Further analysis shows that using CNN can help the model find more nested entities. Besides, we find that different papers use different sentence tokenizations for the three nested NER datasets, which will influence the comparison. Thus, we release a pre-processing script to facilitate future comparison.
On the Importance of Critical Period in Multi-stage Reinforcement Learning

The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plasticity. In recent studies, an AI agent, with a deep neural network mimicking mechanisms of actual neurons, exhibited a learning period similar to human's critical period. Especially during this initial period, the appropriate stimuli play a vital role in developing learning ability. However, transforming human cognitive bias into an appropriate shaping reward is quite challenging, and prior works on critical period do not focus on finding the appropriate stimulus. To take a step further, we propose multi-stage reinforcement learning to emphasize finding ``appropriate stimulus"around the critical period. Inspired by humans' early cognitive-developmental stage, we use multi-stage guidance near the critical period, and demonstrate the appropriate shaping reward (stage-2 guidance) in terms of the AI agent's performance, efficiency, and stability.
ENAGRAM: An App to Evaluate Preventative Nudges for Instagram

Online self-disclosure is perhaps one of the last decade’s most studied communication processes, thanks to the introduction of Online Social Networks (OSNs) like Facebook. Self-disclosure research has contributed significantly to the design of preventative nudges seeking to support and guide users when revealing private information in OSNs. Still, assessing the effectiveness of these solutions is often challenging since changing or modifying the choice architecture of OSN platforms is practically unfeasible. In turn, the effectiveness of numerous nudging designs is supported primarily by self-reported data instead of actual behavioral information. Objective: This work presents ENAGRAM, an app for evaluating preventative nudges, and reports the first results of an empirical study conducted with it. Such a study aims to showcase how the app (and the data collected with it) can be leveraged to assess the effectiveness of a particular nudging approach. Method: We used ENAGRAM as a vehicle to test a risk-based strategy for nudging the self-disclosure decisions of Instagram users. For this, we created two variations of the same nudge (i.e., with and without risk information) and tested it in a between-subjects experimental setting. Study participants (N=22) were recruited via Prolific and asked to use the app regularly for 7 days. An online survey was distributed at the end of the experiment to measure some privacy-related constructs. Results: From the data collected with ENAGRAM, we observed lower (though non-significant) self-disclosure levels when applying risk-based interventions. The constructs measured with the survey were not significant either, except for participants’ External Information Privacy Concerns (EIPC). Implications: Our results suggest that (i) ENAGRAM is a suitable alternative for conducting longitudinal experiments in a privacy-friendly way, and (ii) it provides a flexible framework for the evaluation of a broad spectrum of nudging solutions.
Developing an emotion-affective open-domain chatbot with positively transitioned sentiment data

In this paper, we describe a data-driven approach for developing Emily, an emotion-affective open-domain chatbot. The proposed data enhancing method can explicitly model positively transitioned (PT) sentiment data from multi-turn dialogues. We construct a dialogue corpus with PT sentiment data and will release it for public use. By ﬁne-tuning a pre-trained dialogue model using the produced PT enhanced dialogues, we are able to develop an emotion-affective open-domain chatbot exhibiting close-to-human performance in various emotion-affective metrics. We evaluate Emily against a few state-of-the-art (SOTA) open-domain chatbots and show the effectiveness of the proposed approach.
Second Order Ensemble Langevin Method for Sampling and Inverse Problems

We propose a sampling method based on an ensemble approximation of second order Langevin dynamics. The log target density is appended with a quadratic term in an auxiliary momentum variable and damped-driven Hamiltonian dynamics introduced; the resulting stochastic differential equation is invariant to the Gibbs measure, with marginal on the position coordinates given by the target. A preconditioner based on covariance under the law of the dynamics does not change this invariance property, and is introduced to accelerate convergence to the Gibbs measure. The resulting mean-field dynamics may be approximated by an ensemble method; this results in a gradient-free and affine-invariant stochastic dynamical system. Numerical results demonstrate its potential as the basis for a numerical sampler in Bayesian inverse problems.
Smart parking sensors, technologies and applications for open parking lots: a review

Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This study reviews the literature on the usage of smart parking sensors, technologies, applications and evaluates their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this study suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real-time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS) has provided promising approaches to automating the design of network architectures for lower prediction error. Recently, the emerging application scenarios of deep learning have raised higher demands for network architectures considering multiple design criteria: number of parameters/floating-point operations, and inference latency, among others. From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them. Nonetheless, there is still a clear gap confining the related research along this pathway: on the one hand, there is a lack of a general problem formulation of NAS tasks from an optimization point of view; on the other hand, there are challenges in conducting benchmark assessments of EMO algorithms on NAS tasks. To bridge the gap: (i) we formulate NAS tasks into general multi-objective optimization problems and analyze the complex characteristics from an optimization point of view; (ii) we present an end-to-end pipeline, dubbed $\texttt{EvoXBench}$, to generate benchmark test problems for EMO algorithms to run efficiently -- without the requirement of GPUs or Pytorch/Tensorflow; (iii) we instantiate two test suites comprehensively covering two datasets, seven search spaces, and three hardware devices, involving up to eight objectives. Based on the above, we validate the proposed test suites using six representative EMO algorithms and provide some empirical analyses. The code of $\texttt{EvoXBench}$ is available from $\href{https://github.com/EMI-Group/EvoXBench}{\rm{here}}$.
Suppressing decoherence in noisy intermediate-scale quantum processors with unitary operations

Quantum computers have a potential to overcome devices based on classical principles in various practically relevant problems, yet the power of the existing generation of quantum processors is not enough to demonstrate this experimentally. Currently available quantum devices have serious limitations, including limited numbers of qubits and noise processes that limit circuit depth caused by eﬀects of decoherence. In this work, by following the recent theoretical proposal [Opt. Eng. 59 , 061625 (2020)] we study an application of quantum state-dependent pre- and post-processing unitary operations for protecting the given (multi-qubit) quantum state against the eﬀect of identical single-qubit channels acting of each qubit. We demonstrate the increase in the ﬁdelity of the output quantum state both in a quantum emulation experiment, where all unitaries are perfect, and in a real experiment with a cloud-accessible quantum processor, where protecting unitaries themselves are aﬀected by the noise. We expect the considered approach can be useful for improving ﬁdelity of quantum states during realization of quantum algorithms and quantum communication protocols.
Reconstructing complex states of a 20-qubit quantum simulator

A prerequisite to the successful development of quantum computers and simulators is precise understanding of physical processes occurring therein, which can be achieved by measuring the quantum states they produce. However, the resources required for traditional quantum-state estimation scale exponentially with the system size, highlighting the need for alternative approaches. Here we demonstrate an efficient method for reconstruction of significantly entangled multi-qubit quantum states. Using a variational version of the matrix product state ansatz, we perform the tomography (in the pure-state approximation) of quantum states produced in a 20-qubit trapped-ion Ising-type quantum simulator, using the data acquired in only 27 bases with 1000 measurements in each basis. We observe superior state reconstruction quality and faster convergence compared to the methods based on neural network quantum state representations: restricted Boltzmann machines and feedforward neural networks with autoregressive architecture. Our results pave the way towards efficient experimental characterization of complex states produced by the quench dynamics of many-body quantum systems.
LargeNetVis: Visual Exploration of Large Temporal Networks Based on Community Taxonomies

Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible. In this paper, we propose LargeNetVis, a web-based visual analytics system designed to assist in analyzing small and large temporal networks. It successfully achieves this goal by leveraging three taxonomies focused on network communities to guide the visual exploration process. The system is composed of four interactive visual components: the first (Taxonomy Matrix) presents a summary of the network characteristics, the second (Global View) gives an overview of the network evolution, the third (a node-link diagram) enables community- and node-level structural analysis, and the fourth (a Temporal Activity Map – TAM) shows the community- and node-level activity under a temporal perspective. We demonstrate the usefulness and effectiveness of LargeNetVis through two usage scenarios and a user study with 14 participants.
Waveform Design for Mutual Interference Mitigation in Automotive Radar

The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates. To address the synchronous and asynchronous interference mitigation problems in similar radar systems, we ﬁrst propose herein two slow-time coding schemes to modulate the pulses within a coherent processing interval (CPI) for a single-input-single-output (SISO) scenario. Speciﬁcally, the ﬁrst coding scheme relies on Doppler shifting and the second one is devised based on an optimization approach. We further extend our discussion to the more general case of multiple-input-multiple-output (MIMO) radars and propose an efﬁcient algorithm to design waveforms to mitigate mutual interference in such systems. The proposed coding schemes are computationally efﬁcient in practice and the incorporation of the coding schemes requires only a slight modiﬁcation of the existing systems. Our numerical examples indicate that the proposed coding schemes can reduce the interference power level in a desired area of the cross-ambiguity function signiﬁcantly.
Blockchain-enabled tokenization for sustainable and inclusive infrastructure investment

Infrastructure is critical for enabling society to function and the economy to thrive, but there is an increasing mismatch between the need for infrastructure investments and available capital, which is in consequence of constraints on public resources and limited capacity to leverage the private sector co-financing under the current system. With the emergence of distributed ledger technology, such as blockchain-enabled tokenization, there is a significant potential to improve investment liquidity, transparency, efficiency and create new economic models to integrate non-financial values to promote sustainability and inclusiveness. This research analyzed 21 projects to investigate how tokenization is implemented in energy infrastructure projects. Exploratory case study analyses were conducted, which shows the diversity of tokenization arrangements. The state of the art, potential benefits, implications, and obstacles associated with the application of tokenization in infrastructure investment and development are discussed. The purpose of this research is to understand tokenization within the context of the energy sector but also to forecast its application in a broad spectrum of infrastructure projects (e.g., transportation, telecommunication, healthcare, education).