Miracle-dz/UniFAR-FTUs huggingface dataset:
The structure of the prokaryotic communities of the initial stages of soil formation in Antarctic Peninsula

The study of living organisms in the Antarctic soils is one of the most priority areas of polar research. The essential role of microorganisms in soil-formation and biospheric processes implies the relevance of the study of the microbiome adaptations and its ecological patterns in a variety of Antarctic soils. The use of Illumina sequencing technique was applied for investigation of prokaryotic communities in soils not covered and covered by moss-lichen cover, and in soils in conditions of different degree of ornitogenic impact. The results obtained showed that Actinobacteriota were generally dominated on the places without moss-lichen cover, Bacteroidota – in the soils with well-developed organogenic horizon, including the group of ornithogenic soils. The studied microbiomes were also characterized by a significant proportion of Cyanobacteria – prokaryontic phylum, typical for the initial stages of soil formation. Biodiversity analysis showed that the main factor that formed the composition of prokaryotic community was the presence and amount of organic matter in the soil, while ornithogenic influence was of subordinate importance. The results obtained make a contribution to the study of the microbial ecology of the primary soils of Antarctica and the adaptive trends of the microbiome to the harsh conditions of ecosystems of polar regions.
Introduction of oblique sections into the curves web in modeling the framing of a vessel hull in KOMPAS-3D

The paper considers the issues of solving the applied problem of three-dimensional modeling of a vessel hull theoretical surface in KOMPAS-3D computer-aided design system. The methods to increase the smoothness of skeleton curves – frames – are shown. Various methods of modeling the vessel hull as a frame surface are considered: a surface in sections and a web of curves. Their comparative analysis was carried out from the point of view of surface accuracy and smoothness according to the set of proposed quantitative parameters to assess the modeling quality of a frame surface. The method to improve the modeling quality is proposed through the introduction of additional sections with oblique planes – ribbands – into the curves web. Various methods of 3D modeling of the vessel hull were put into practice, as well as the comparative results of quantitative study of the modeling quality of the vessel hull by various methods are given. It was shown that additional oblique sections introduced into the curves web may improve the quality of the frame surface. The results of the study may be of practical interest in modeling and evaluating the quality of a wide class of frame surfaces defined by more than one set of sections.
Improvement of multi-objective genetic algorithms using differential evolution

The following paper describes the manner to improve the efficiency of multi-objective genetic algorithms. It uses the differential evolution with the developed original scheme. One is exploited in the process of creating new individuals. By this scheme in recombination process the searching path is generated into two spaces – decisions and objectives. To increase the probability of creating individuals that dominate their parents various combinations of individuals selected for recombination may be examined. Ultimately it was found that such a strategy should allow to realize a higher replacement of the population dominated individuals. The proposed scheme of differential evolution is used as an extension (modification) for all evolutionary methods of multi-objective optimization. The experiments for the evaluation of the given scheme were conducted on the DTLZ benchmark problems. As a result, in many cases the algorithms which used the proposed scheme of differential evolution, allowed to improve the results (on some indicators) of algorithms SPEA2, NSGA-II and their modifications with the general scheme of differential evolution.
How Knowledge-Based Human Recourse Practices Affect Innovation & Organization Performance: The Mediatory Role of Human, Relational, and Structural Capital, Complemented by Individualism and Uncertainty Avoidance

Innovation depends on knowledge since any innovation implies the development of new knowledge as both an input (e.g. new ideas, concepts, prototypes, etc.) and an outcome (i.e. the novelty produced). This paper explored how the human resource management (HRM) system of explicitly knowledge-based HRM practices impacts a firm's intellectual capital, producing higher innovation and organizational performance. For this, we proposed a theoretical framework modifying the Kianto et. al. (2017) model to include organizational performance as an outcome. Furthermore, we also explored the complementarities of the national culture of collectivism and uncertainty avoidance with knowledge-based HRM on both performance outcomes. Five dimensions of knowledge-based HRM were included namely 1. Training & Development, 2. Collectivism, 3. Performance Assessment, 4. Recruitment & Selection, and 5. Compensation. We proposed that these factors affect the Human, Rational, and Structural Capital in the organizations, which in turn impact the performance outcomes. Empirical validity was established by conducting a survey using a close-ended questionnaire. Data was collected from 300 respondents and analyzed using confirmatory factor analysis and structured equation modeling. The results suggested that compensation, Performance Assessment, Recruitment & Selection seems to have a significant and positive effect on all three types of intellectual capital, while Training & Development seems to affect human and structural capital positively. Relational and structural capital also seems to significantly and positively affect organizational performance. Relational capital also seems to affect innovative performance. Performance Assessment also seems to directly affect innovative and organizational performance proving partial mediation. Whereas in other seems to have a full mediation. Collectivism also seems to be positively affecting both performances, whereas Uncertainty Avoidance seems to be affecting organizational performance positively. The moderation analysis suggested Uncertainty avoidance significantly moderates the relationship between training & development(HRM) and innovation performance. Uncertainty avoidance has an insignificant moderating effect and a positive relationship between performance assessment(KHRM) & innovation performance. The result implies that effective innovation management is a human and knowledge-related challenge. Knowledge-based HRM approaches encourage knowledge exchange, production, and application among employees and can help organizations significantly enhance their innovation and organizational performance.
Time Use and Happiness: Evidence across Three Decades

We use large-scale diary data from a representative sample of Americans to proxy welfare at the level of individual activities. We make three contributions. First, we examine the association between individual activities and happiness, and show how this association has changed over time. Compared to 1985, domestic work and social care produce more happiness today. Watching TV produces less happiness today than it used to. Second, we combine activity-level data on happiness and time allocation to construct a measure of ‘time-weighted happiness’. We then analyse historical trends in this measure across population groups, particularly gender. We observe that, over the last 35 years, women’s time-weighted happiness has improved significantly relative to men’s. This trend is largely driven by gendered shifts in time allocation, rather than heterogenous trends in the enjoyability of individual activities. Our result is in stark contrast to previous work which showed a decline in women’s relative wellbeing. To explain this, our third contribution is to compare the determinants of life satisfaction – a global measure on which most previous work is built – with our measure of time-weighted happiness. Time-weighted happiness and life satisfaction turn out to only be weakly correlated. Moreover, although we obtain strong associations of income and employment status with life satisfaction, no such associations can be observed for time-weighted happiness. These findings highlight the importance of distinguishing between happiness as experienced in time and more global wellbeing measures. Finally, we verify the robustness our results by replicating them in data from the United Kingdom and show that our results are robust to alternative assumptions about how people use happiness scales.
The Influencing Factors of Reward Crowdfunding on Small and Micro Entrepreneurial Project Financing: A Moderating Perspective of Economic Embeddedness

Abstract: The emergence of crowdfunding financing platforms provides a new solution to the financing difficulties of small and micro enterprises. In order to improve the financing performance of small and micro enterprises, existing studies mostly start from the aspects of the project itself, promoter, investors, etc., and rarely consider the impact of the overall social environment. This study introduces social embeddedness theory, takes the moderating effect of economic embeddedness as research object, establishes a crowdfunding financing performance measurement model, and discuss the moderating effect of the RMB/US dollar exchange and the CPI on the crowdfunding performance. The research results show that participation willingness plays partly mediating role between product value and crowdfunding performance, and a complete mediating role between visualization value and crowdfunding performance; the RMB/US dollar exchange rate up-regulated the relationship between perceived value and financing performance. The CPI negatively regulates the relationship between perceived value and financing performance.
System norm regularization methods for Koopman operator approximation

Approximating the Koopman operator from data is numerically challenging when many lifting functions are considered. Even low-dimensional systems can yield unstable or ill-conditioned results in a high-dimensional lifted space. In this paper, Extended Dynamic Mode Decomposition (DMD) and DMD with control, two methods for approximating the Koopman operator, are reformulated as convex optimization problems with linear matrix inequality constraints. Asymptotic stability constraints and system norm regularizers are then incorporated as methods to improve the numerical conditioning of the Koopman operator. Specifically, the H∞ norm is used to penalize the input–output gain of the Koopman system. Weighting functions are then applied to penalize the system gain at specific frequencies. These constraints and regularizers introduce bilinear matrix inequality constraints to the regression problem, which are handled by solving a sequence of convex optimization problems. Experimental results using data from an aircraft fatigue structural test rig and a soft robot arm highlight the advantages of the proposed regression methods.
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information

Adversarial attacks against commercial black-box speech platforms, including cloud speech APIs and voice control devices, have received little attention until recent years. Constructing such attacks is difficult mainly due to the unique characteristics of time-domain speech signals and the much more complex architecture of acoustic systems. The current "black-box" attacks all heavily rely on the knowledge of prediction/confidence scores or other probability information to craft effective adversarial examples (AEs), which can be intuitively defended by service providers without returning these messages. In this paper, we take one more step forward and propose two novel adversarial attacks in more practical and rigorous scenarios. For commercial cloud speech APIs, we propose Occam, a decision-only black-box adversarial attack, where only final decisions are available to the adversary. In Occam, we formulate the decision-only AE generation as a discontinuous large-scale global optimization problem, and solve it by adaptively decomposing this complicated problem into a set of sub-problems and cooperatively optimizing each one. Our Occam is a one-size-fits-all approach, which achieves 100% success rates of attacks (SRoA) with an average SNR of 14.23dB, on a wide range of popular speech and speaker recognition APIs, including Google, Alibaba, Microsoft, Tencent, iFlytek, and Jingdong, outperforming the state-of-the-art black-box attacks. For commercial voice control devices, we propose NI-Occam, the first non-interactive physical adversarial attack, where the adversary does not need to query the oracle and has no access to its internal information and training data. We, for the first time, combine adversarial attacks with model inversion attacks, and thus generate the physically-effective audio AEs with high transferability without any interaction with target devices. Our experimental results show that NI-Occam can successfully fool Apple Siri, Microsoft Cortana, Google Assistant, iFlytek and Amazon Echo with an average SRoA of 52% and SNR of 9.65dB, shedding light on non-interactive physical attacks against voice control devices.
Faster Rates for the Frank-Wolfe Algorithm Using Jacobi Polynomials

The Frank Wolfe algorithm (FW) is a popular projection-free alternative for solving large-scale constrained optimization problems. However, the FW algorithm suffers from a sublinear convergence rate when minimizing a smooth convex function over a compact convex set. Thus, exploring techniques that yield a faster convergence rate becomes crucial. A classic approach to obtain faster rates is to combine previous iterates to obtain the next iterate. In this work, we extend this approach to the FW setting and show that the optimal way to combine the past iterates is using a set of orthogonal Jacobi polynomials. We also a polynomial-based acceleration technique, referred to as Jacobi polynomial accelerated FW, which combines the current iterate with the past iterate using combing weights related to the Jacobi recursion. By carefully choosing parameters of the Jacobi polynomials, we obtain a faster sublinear convergence rate. We provide numerical experiments on real datasets to demonstrate the efficacy of the proposed algorithm.
Development of a Cesium Fountain Clock at HUST: Preliminary Results

A cesium atomic fountain clock is under development at Huazhong University of Science and Technology (HUST) in China. In this paper, we describe the construction of the entire fountain clock system and report the preliminary results. A frequency stability of 2.5×10τ has been achieved by intercomparison with a hydrogen maser, and the factors limiting the frequency stability are also discussed.
A Novel Recurrent Adaptive Backstepping Optimal Control Strategy for a Single Inverted Pendulum System

In this paper, a novel recurrent adaptive backstepping optimal control strategy for a single inverted pendulum system is studied. By this method, an inverted pendulum is stabilized using projection recurrent neural network-based adaptive backstepping control (PRNN-ABC). The inverted pendulum is a popular nonlinear system that is used in both industry and academic and is applied various control approaches since it has many applications. Here, first of all, the backstepping control laws are investigated based on the nonlinear dynamic model of the system. Second, by considering control constrains and performance index, the constrained optimization problem is formulated. Later, the optimization problem will be converted to a constrained quadratic problem (QP). To study the recurrent neural network (RNN) according to the Karush- Kuhn-Tucker (KKT) optimization conditions and the variational inequality, the dynamic model of the RNN will be derived. At last, the stability analysis of the system is studied using Lyapunov function.
Interpolating between sampling and variational inference with infinite stochastic mixtures

Sampling and Variational Inference (VI) are two large families of methods for approximate inference that have complementary strengths. Sampling methods excel at approximating arbitrary probability distributions, but can be inefficient. VI methods are efficient, but may misrepresent the true distribution. Here, we develop a general framework where approximations are stochastic mixtures of simple component distributions. Both sampling and VI can be seen as special cases: in sampling, each mixture component is a delta-function and is chosen stochastically, while in standard VI a single component is chosen to minimize divergence. We derive a practical method that interpolates between sampling and VI by solving an optimization problem over a mixing distribution. Intermediate inference methods then arise by varying a single parameter. Our method provably improves on sampling (reducing variance) and on VI (reducing bias+variance despite increasing variance). We demonstrate our method's bias/variance trade-off in practice on reference problems, and we compare outcomes to commonly used sampling and VI methods. This work takes a step towards a highly flexible yet simple family of inference methods that combines the complementary strengths of sampling and VI.
Speech Enhancement Based on CycleGAN with Noise-informed Training

Cycle-consistent generative adversarial networks (CycleGAN) were successfully applied to speech enhancement (SE) tasks with unpaired noisy-clean training data. The CycleGAN SE system adopted two generators and two discriminators trained with losses from noisy-t0-clean and cleant0-noisy conversions. CycleGAN showed promising results for numerous SE tasks. Herein, we investigate a potential limitation of the clean-to-noisy conversion part and propose a novel noise-informed training (NIT) approach to improve the performance of the original CycleGAN SE system. The main idea of the NIT approach is to incorporate target domain information for clean-t0-noisy conversion to facilitate a better training procedure. The experimental results confirmed that the proposed NIT approach improved the generalization capability of the original CycleGAN SE system with a notable margin.
A Lightweight, High-Extension, Planar 3-Degree-of-Freedom Manipulator Using Pinched Bistable Tapes

To facilitate sensing and physical interaction in remote and/or constrained environments, high-extension, lightweight robot manipulators are easier to transport and reach substantially further than traditional serial chain manipulators. We propose a novel planar 3-degree-of-freedom manipulator that achieves low weight and high extension through the use of a pair of spooling bistable tapes, commonly used in self-retracting tape measures, which are pinched together to form a reconfigurable revolute joint. The pinching action flattens the tapes to produce a localized bending region, resulting in a revolute joint that can change its orientation by cable tension and its location on the tapes though friction-driven movement of the pinching mechanism. We present the design, implementation, kinematic modeling, stiffness behavior of the revolute joint, and quasi-static performance of this manipulator. In particular, we demonstrate the ability of the manipulator to reach specified targets in free space, reach a 2D target with various orientations, and maintain an end-effector angle or stationary bending point while changing the other. The long-term goal of this work is to integrate the manipulator with an aerial robot to enable more capable aerial manipulation.
Model Predictive Control for Automotive Climate Control Systems via Value Function Approximation

Among the auxiliary loads in light-duty vehicles, the air conditioning system is the single largest energy consumer. For electrified vehicles, the impact of heating and cooling loads becomes even more significant, as they compete with the powertrain for battery energy use and can significantly reduce the range or performance. While considerable work has been made in the field of optimal energy management for electrified vehicles and optimization of vehicle velocity for eco-driving, few contributions have addressed the application of energy-optimal control for heating and cooling loads. This letter proposes an energy management strategy for the thermal management system of an electrified powertrain, based on Model Predictive Control. Starting from a nonlinear model of the vapor compression refrigeration system that captures the dynamics of the refrigerant in the heat exchangers and the power consumption of the system, a constrained multi-objective optimal control problem is formulated to reduce energy consumption while tracking a desired thermal set point. An efficient implementation of MPC is proposed for real-time applications by introducing a terminal cost obtained from the approximation of the global optimal solution.
Microscopic treatment of instantaneous spectral diffusion and its effect on quantum gate fidelities in rare-earth-ion-doped crystals

The eﬀect of instantaneous spectral diﬀusion (ISD) on gate operations in rare-earth-ion-doped crystals is an important question to answer for the future of rare-earth quantum computing. Here we present a microscopic modeling that highlights the stochastic nature of the phenomenon, and use it to investigate ISD errors on single-qubit gate operations. Furthermore, we present a method to estimate the total error from many diﬀerent error sources by only studying subsystems containing one error source at a time. This allows us to estimate the total ISD error from all non-qubit dopants in the vicinity of a qubit. We conclude that optical pumping techniques must be used to empty the frequency regions around the qubit transitions from absorption (transmission windows) in order to suppress the ISD errors. Despite using such windows, there remains a roughly 0 . 3% risk that a qubit has an ISD error larger than the error from other sources. In those cases, the qubit can be discarded and its frequency channel can be reused by another qubit. However, in most cases the ISD errors are signiﬁcantly smaller than other errors, thus opening up the possibility to perform noisy intermediate-scale quantum (NISQ) algorithms despite ISD being present.
Chunked Autoregressive GAN for Conditional Waveform Synthesis

Conditional waveform synthesis models learn a distribution of audio waveforms given conditioning such as text, mel-spectrograms, or MIDI. These systems employ deep generative models that model the waveform via either sequential (autoregressive) or parallel (non-autoregressive) sampling. Generative adversarial networks (GANs) have become a common choice for non-autoregressive waveform synthesis. However, state-of-the-art GAN-based models produce artifacts when performing mel-spectrogram inversion. In this paper, we demonstrate that these artifacts correspond with an inability for the generator to learn accurate pitch and periodicity. We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression. We discuss the inductive bias that autoregression provides for learning the relationship between instantaneous frequency and phase, and show that this inductive bias holds even when autoregressively sampling large chunks of the waveform during each forward pass. Relative to prior state-of-the-art GAN-based models, our proposed model, Chunked Autoregressive GAN (CARGAN) reduces pitch error by 40-60%, reduces training time by 58%, maintains a fast generation speed suitable for real-time or interactive applications, and maintains or improves subjective quality.