Miracle-dz/UniFAR-FTUs huggingface dataset:
DATA ENVELOPMENT ANALYSIS (DEA) FOR SAFETY-BASED EFFICIENCY EVALUATION OF CONSTRUCTION SITES

Identifying efficient construction sites in terms of safety is a challenge that must be addressed while assessing and evaluating performance criteria. This paper presents a method for comparative and relative analysis of construction sites in terms of their safety performance. The proposed method employs Data Envelopment Analysis (DEA) for identifying efficiency of construction sites, also known as decision making units (DMU’s). Safety climate factor and number of incidents are respectively used as inputs and output of the DEA system. Four various scenarios are performed and analyzed, and results are compared to evaluate efficient construction sites. Results show that the number of incidents incurred at construction sites, which is the output of the DEA framework, is the dominant factor correlating with the efficiency of construction sites. Moreover, specific safety climate factors are investigated to examine their impact on the efficiency calculated. Some other important observations that all comply with practical expectations are also reported.
Reinforcement Learning in Sensor-Guided AIBO Robots

—A common challenge in autonomous mobile applications is maintaining controlled movement of an agent on unknown or unanticipated surfaces. The Tekkotsu application framework, built to facilitate research with the Sony AIBO robot, has basic walking functions but does not include any adaptive behavior. Through the use of an indoor vector-tracking system, we attempt to implement a reinforcement learning algorithm, Q-learning, to intelligently learn new surfaces and thus improve the existing walking mechanism.
CROSS-ENTROPY APPROACH AND GENETIC ALGORITHMS FOR PSS LOCATION AND TUNING

 Abstract—Mixed Integer Non Linear Programming (MINLP) via the cross-entropy approach for the optimal location and tuning of Power System Stabilizer (PSS) is presented in this paper. The considered problem is to maximize the damping ratio of the global system under a minimum number of PSS and simultaneously to find out the best candidate machines to be equipped with PSSs. The damping ratio objective is achieved by tuning the controllers to shift the lightly damped and undamped electromechanical modes of all plants to a prescribed zone in the s-plane. This problem of tuning and location of PSS over a wide range of system configurations is formulated as a MINLP problem where the objective is the aggregation of the two objectives on the damping ratio and on the PSS’s number. The mixed optimization problem, with a great combinatory aspect, is solved by an extension of the cross-entropy approach from the rare event framework. The performance of this technique for damping the oscillations in multimachine power systems is confirmed through eigenvalues analysis over many scenarios. The proposed method is assessed by a comparative study with a previous approach based on the genetic algorithms.
Functional and Epidemiological Characterization ofNon-Synonymous Single Nucleotide Polymorphisms in IRAK2

Toll-like receptors (TLR) play an essential role in innate immunity, in which they indentify invasive pathogens and initiate the immune responses to eliminate the pathogens. Interleukine-1receptor-associated kinases are key kinases in the TLR signalling pathway where they integrate signals from activated receptor complexes to then induce the activation of downstream transcription factors NF-κB, AP-1 and IRFs. It is known that IRAK4 deficiency in humans is related with recurrent pyogenic bacterial infections which can be lethal without any facilitation of health care and antibiotics. In light of this fact, this study focuses on analysing the general role of IRAK2 on the pathogenesis of certain kinds of diseases. Known non-synonymous single nucleotide polymorphisms (SNPs) of IRAK2 were selected from the NCBI SNP database, which served as probes to gain insights of the molecular functions of IRAK2. The SNPs of interest were screened by overexpressing the constructs of variants into a human cell line and detecting the activation of NF-κB. IRAK2 R214G and L392V decreased the activation of NF-κB and the production of IL-8. Although R214G and L392V varied the interaction intensity with IRAK2 and IRAK4, these two variants still maintained the interaction with the downstream signalling partner TRAF6. Interestingly, IRAK2 R214G and L392V both reduced the ubiquitination levels of TRAF6. The ubiquitination of TRAF6 is an essential step for the NF-κB activation. Moreover, when stimulating human IRAK2 WT reconstituted murine Irak2 knockout macrophages with TLR2, 4 and 7 ligands, the TNF-α production was massively enhanced. Meanwhile, phosphorylation of p38, p65, ERK was enhanced as well. Intriguingly, the phosphorylation of Akt was only observed in the IRAK2 WT reconstituted macrophages. Additionally, the R214G and L392V reconstituted macrophages reduced the transcriptional activation of Il-1β and Il-6 in comparison to IRAK2 WT. The epidemiological analyses revealed that IRAK2 R214G (rs35060588) reduced the survival time of colorectal cancer patients; IRAK2 L392V (rs3855283) significantly reduced the auto clearance of HCV in patients who are then susceptible to become a chronic HCV infection; additionally L392V also increased the possibility of progression from gastritis to gastric cancer. Collectively, this information would be helpful for the future personalized therapy.
A review of Machine Learning techniques used in Opinion Mining

The proliferation of internet technologies and smart phones has led to an increasing number of people using social networking and micro blogging platforms. Consumers are constantly expressing opinions about various kinds of products and services in multiple formats in huge numbers. This has led to the development of a new discipline called opinion mining that focuses on automatically classifying the sentiments of users without manual intervention. Opinion mining has acquired immense research and commercial interest due to its potential applications for users and businesses alike. While many methods are available for automatically classifying sentiments of web based articles and reviews, machine learning techniques like naive bayes, support vector machines, maximum entropy, decision trees etc are being widely used in opinion mining tasks. This paper attempts to understand the problem of opinion mining and how machine learning algorithms are being implemented to analyze the sentiments of user opinions
HORIZONTAL TRANSFER OF vanA-MEDIATED RESISTANCE AMONG COMMENSAL AND STREPTOGRAMIN-RESISTANT ENTEROCOCCI DERIVED FROM MULTI-COMPONENT FOOD SYSTEMS

OF THE DISSERTATION Horizontal Transfer of vanA-Mediated Resistance Among Commensal and Streptogramin-Resistant Enterococci Derived from Multi-Component Food Systems By: Erin A. Christensen Dissertation Director: Karl R. Matthews, Ph.D. The enterococci are commensal lactic acid bacteria of the gastrointestinal tract of humans and animals, and are ubiquitous in nature. They have a long history of use as starter cultures in European fermented foods and are used commercially as probiotics. Within the past two decades, vancomycin-resistant enterococci (VRE) have emerged as significant nosocomial pathogens, treatable with few antibiotics. In the US, the agricultural use of virginiamycin, a streptogramin growth promoter, has raised concern regarding cross-resistance among VRE to the clinical streptogramin quinupristindalfopristin. Horizontal transfer of antibiotic resistance genes is mediated by aggregation substances and sex pheromones that induce conjugation, but the role of these virulence factors in enterococci of food origin is largely unknown. The potential for enterococci isolated from multi-component creamy deli salads (including macaroni, potato, chicken, and seafood) to harbor and disseminate the high-level vancomycin resistance gene vanA
Double soft theorem for generalised biadjoint scalar amplitudes

We study double soft theorem for the generalised biadjoint scalar field theory whose amplitudes are computed in terms of punctures on \mathbb{CP}^{k-1}ℂℙk−1. We find that whenever the double soft limit does not decouple into a product of single soft factors, the leading contributions to the double soft theorems come from the degenerate solutions, otherwise the non-degenerate solutions dominate. Our analysis uses the regular solutions to the scattering equations. Most of the results are presented for k=3k=3 but we show how they generalise to arbitrary kk. We have explicit analytic results, for any kk, in the case when soft external states are adjacent.
Degree for weakly upper semicontinuous perturbations of quasi-m-accretive operators

In the paper, we provide the construction of a coincidence degree being a homotopy invariant detecting the existence of solutions of equations or inclusions of the form Ax ∈ F(x), x ∈ U, where A:D(A)⊸E is an m-accretive operator in a Banach space E, F:K⊸E is a weakly upper semicontinuous set-valued map constrained to an open subset U of a closed set K ⊂ E. Two different approaches are presented. The theory is applied to show the existence of non-trivial positive solutions of some nonlinear second-order partial differential equations with discontinuities. This article is part of the theme issue ‘Topological degree and fixed point theories in differential and difference equations’.
Audio Dequantization for High Fidelity Audio Generation in Flow-based Neural Vocoder

In recent works, a flow-based neural vocoder has shown significant improvement in real-time speech generation task. The sequence of invertible flow operations allows the model to convert samples from simple distribution to audio samples. However, training a continuous density model on discrete audio data can degrade model performance due to the topological difference between latent and actual distribution. To resolve this problem, we propose audio dequantization methods in flow-based neural vocoder for high fidelity audio generation. Data dequantization is a well-known method in image generation but has not yet been studied in the audio domain. For this reason, we implement various audio dequantization methods in flow-based neural vocoder and investigate the effect on the generated audio. We conduct various objective performance assessments and subjective evaluation to show that audio dequantization can improve audio generation quality. From our experiments, using audio dequantization produces waveform audio with better harmonic structure and fewer digital artifacts.
Imitation learning based on entropy-regularized forward and inverse reinforcement learning

This paper proposes Entropy-Regularized Imitation Learning (ERIL), which is a combination of forward and inverse reinforcement learning under the framework of the entropy-regularized Markov decision process. ERIL minimizes the reverse Kullback-Leibler (KL) divergence between two probability distributions induced by a learner and an expert. Inverse reinforcement learning (RL) in ERIL evaluates the log-ratio between two distributions using the density ratio trick, which is widely used in generative adversarial networks. More specifically, the log-ratio is estimated by building two binary discriminators. The first discriminator is a state-only function, and it tries to distinguish the state generated by the forward RL step from the expert's state. The second discriminator is a function of current state, action, and transitioned state, and it distinguishes the generated experiences from the ones provided by the expert. Since the second discriminator has the same hyperparameters of the forward RL step, it can be used to control the discriminator's ability. The forward RL minimizes the reverse KL estimated by the inverse RL. We show that minimizing the reverse KL divergence is equivalent to finding an optimal policy under entropy regularization. Consequently, a new policy is derived from an algorithm that resembles Dynamic Policy Programming and Soft Actor-Critic. Our experimental results on MuJoCo-simulated environments show that ERIL is more sample-efficient than such previous methods. We further apply the method to human behaviors in performing a pole-balancing task and show that the estimated reward functions show how every subject achieves the goal.
Search-Based Software Engineering for Self-Adaptive Systems: Survey, Disappointments, Suggestions and Opportunities

Search-Based Software Engineering (SBSE) is a promising paradigm that exploits the computational search to optimize different processes when engineering complex software systems. Self-adaptive system (SAS) is one category of such complex systems that permits to optimize different functional and non-functional objectives/criteria under changing environments (e.g., requirements and workload), which involves problems that are subject to search. In this regard, over years, there has been a considerable amount of work that investigates SBSE for SASs. In this paper, we provide the first systematic and comprehensive survey exclusively on SBSE for SASs, covering papers in 27 venues from 7 repositories, which eventually leads to several key statistics from the most notable 74 primary studies in this particular field of research. Our results, surprisingly, have revealed five disappointments that are of utmost importance and can result in serve consequences but have been overwhelmingly ignored in existing studies. We provide theoretical and/or experimental evidence to justify our arguments against the disappointments, present suggestions, and highlight the promising research opportunities towards their mitigation. We also elaborate on three other emergent, but currently under-explored opportunities for future work on SBSE for SASs. By mitigating the disappointments revealed in this work, together with the highlighted opportunities, we hope to be able to excite a much more significant growth in this particular research direction.
Searching for exotic B meson decays with the CMS L1 track trigger

The CMS phase II track trigger may allow for a displaced dimuon vertex trigger with qualitatively lower $p_T$ thresholds than existing dimuon triggers. With this technique, we show that the CMS reach for exotic $B$-meson decays involving a displaced dimuon resonance, such as a light, Higgs-mixed scalar, can be competitive with that of LHCb and Belle II.
Colonization Pattern of Azospirillum brasilense Yu62 on Maize Roots

Plasmid pVK1001 which carried the gfp gene of GFPmut2, a mutant of GFP, was introduced into Azospirillum brasilense Yu62 by electroporation. Maize seedlings were inoculated with the GFP_labelled bacteria and grown gnotobiotically in flask with semi_solid agar medium. Observations were performed with confocal laser scanning microscopy (CLSM) and electron microscopy, respectively, at 8 d and 12 d after inoculation. Confocal laser scanning microscopy showed that A. brasilense Yu62 could penetrate into the cortex tissue, colonizing in the intercellular spaces of the parenchyma cells of the cortex tissue. Transmission and scanning electron microscopy (TEM) showed that the majority of the bacteria colonized on the root surface and only a minority of them resided in the root interior.
On the Power of Automata Minimization in Temporal Synthesis

Reactive synthesis is the problem of automatically constructing a reactive system from a formal specification, with the guarantee that the executions of the system align with the specification. The specification is often described in temporal logic. Some types of specifications can be converted into deterministic finite automata (DFA) as an intermediate step in synthesis, thus benefiting from the fact that DFAs can be fully minimized in polynomial time. In this work we investigate DFA-minimization algorithms in the context of temporal synthesis. In particular, we compare between the Hopcroft and Brzozowski minimization algorithms, adapting them to start from temporal-logic formulas and integrating them into an existing temporal synthesis framework. While earlier studies comparing the two algorithms for randomly-generated automata concluded that neither algorithm dominates, our results suggest that in the context of temporal-synthesis, Hopcroft's algorithm is the best choice. Analyzing the results, we observe that the reason for the poor performance of Brzozowski's algorithm is a discrepancy between theory and practice. This algorithm first constructs a DFA for the reverse language of the specification and then performs a series of operations to transform it into a minimal DFA for the specification itself. In theory, the DFA for the reverse language can be exponentially smaller, which would potentially make this algorithm more efficient than directly constructing the DFA for the original specification. In practice, however, we find that the reverse DFA is often of comparable size or even larger, which cancels the advantage that this approach could have.
Monstrous M-Theory

In 26+1 space–time dimensions, we introduce a gravity theory whose massless spectrum can be acted upon by the Monster group when reduced to 25+1 dimensions. This theory generalizes M-theory in many respects, and we name it Monstrous M-theory, or M2-theory. Upon Kaluza–Klein reduction to 25+1 dimensions, the M2-theory spectrum irreducibly splits as 1 ⊕ 196,883, where 1 is identified with the dilaton, and 196,883 is the dimension of the smallest non-trivial representation of the Monster. This provides a field theory explanation of the lowest instance of the Monstrous Moonshine, and it clarifies the definition of the Monster as the automorphism group of the Griess algebra by showing that such an algebra is not merely a sum of unrelated spaces, but descends from massless states for M2-theory, which includes Horowitz and Susskind’s bosonic M-theory as a subsector. Further evidence is provided by the decomposition of the coefficients of the partition function of Witten’s extremal Monster SCFT in terms of representations of SO24, the massless little group in 25+1; the purely bosonic nature of the involved SO24-representations may be traced back to the unique feature of 24 dimensions, which allow for a non-trivial generalization of the triality holding in 8 dimensions. Last but not least, a certain subsector of M2-theory, when coupled to a Rarita–Schwinger massless field in 26+1, exhibits the same number of bosonic and fermionic degrees of freedom; we cannot help but conjecture the existence of a would-be N=1 supergravity theory in 26+1 space–time dimensions.
Differentially Private Multi-Agent Planning for Logistic-Like Problems

Planning is one of the main approaches used to improve agents’ working efficiency by making plans beforehand. However, during planning, agents face the risk of having their private information leaked. This article proposes a novel strong privacy-preserving planning approach for logistic-like problems. This approach outperforms existing approaches by addressing two challenges: 1) simultaneously achieving strong privacy; completeness and efficiency; and 2) addressing communication constraints. These two challenges are prevalent in many real-world applications including logistics in military environments and packet routing in networks. To tackle these two challenges, our approach adopts the differential privacy technique, which can both guarantee strong privacy and control communication overhead. To the best of our knowledge, this article is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems. We theoretically prove the strong privacy and completeness of our approach and empirically demonstrate its efficiency. We also theoretically analyze the communication overhead of our approach and illustrate how differential privacy can be used to control it.
Universal Model for Deposition of Entities from Molecular to Nanosizes on Nanofibers

Understanding the behavior of entities is critical to the development of new policies and technologies aiming at global health and wellbeing; this paper presents a unified model developed and validated for the deposition of entities, ranging from a few Angstroms to tens of nanometers,on nanofibers. The transport of entities is based on convective diffusion and interfacial interactive diffusion. As the size of entities decreases to intermediate sizes - between gas molecules and nanoparticles - adhesion probability on the surface is less than unity, which is determined by the interfacial interactions and kinetics of adhesion. Dimensionless surface coverage provides a better understanding of adhesion kinetic by considering the effects of initial concentration and time. The model is validated by available experimental data in the literature.
Harmonic generation from gold nanolayers: an old problem under a new light

Understanding how light interacts at the nanoscale with metals, semiconductors, or ordinary dielectrics is pivotal if one is to properly engineer nano-antennas, filters and, more generally, devices that aim to harness the effects of new physical phenomena that manifest themselves at the nanoscale. We presently report experimental results on second and third harmonic generation from 20nm- and 70nm-thick gold layers, for TE- and TM-polarized incident light pulses. We highlight and discuss for the first time the relative roles bound electrons and an intensity dependent free electron density (hot electrons) play in third harmonic generation. While planar structures are generally the simplest to fabricate, metal layers that are only a few nanometers thick and partially transparent are almost never studied. Yet, transmission offers an additional reference point for comparison, which through relatively simple experimental measurements affords the opportunity to test the accuracy of available theoretical models. Our experimental results are explained well within the context of the microscopic hydrodynamic model that we employ to simulate second and third harmonic conversion efficiencies, and to simultaneously and uniquely predict the nonlinear dispersive properties of a gold nanolayer under pulsed illumination. Using our experimental observations and our model, based solely on the measured third harmonic power conversion efficiencies we predict |chi3|~10^(-18)-10^(-17)(m/V)^2, triggered mostly by hot electrons, without resorting to the implementation of a z-scan set-up.
Unsupervised Acoustic Unit Representation Learning for Voice Conversion using WaveNet Auto-encoders

Unsupervised representation learning of speech has been of keen interest in recent years, which is for example evident in the wide interest of the ZeroSpeech challenges. This work presents a new method for learning frame level representations based on WaveNet auto-encoders. Of particular interest in the ZeroSpeech Challenge 2019 were models with discrete latent variable such as the Vector Quantized Variational Auto-Encoder (VQVAE). However these models generate speech with relatively poor quality. In this work we aim to address this with two approaches: first WaveNet is used as the decoder and to generate waveform data directly from the latent representation; second, the low complexity of latent representations is improved with two alternative disentanglement learning methods, namely instance normalization and sliced vector quantization. The method was developed and tested in the context of the recent ZeroSpeech challenge 2020. The system output submitted to the challenge obtained the top position for naturalness (Mean Opinion Score 4.06), top position for intelligibility (Character Error Rate 0.15), and third position for the quality of the representation (ABX test score 12.5). These and further analysis in this paper illustrates that quality of the converted speech and the acoustic units representation can be well balanced.