Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Exoskeleton Glove Equipped with Variable Stiffness Joints, Abduction Capabilities, and a Telescopic Thumb

Robotic hand exoskeletons have become a popular and efficient technological solution for assisting people that suffer from neurological conditions and for enhancing the capabilities of healthy individuals. This class of devices ranges from rigid and complex structures to soft, lightweight, wearable gloves. In this work, we propose a hybrid (tendon-driven and pneumatic), lightweight, affordable, easy-to-operate exoskeleton glove equipped with variable stiffness, laminar jamming structures, abduction/adduction capabilities, and a pneumatic telescopic extra thumb that increases grasp stability. The efficiency of the proposed device is experimentally validated through five different types of experiments: i) abduction/adduction tests, ii) force exertion experiments that capture the forces that can be exerted by the proposed device under different conditions, iii) bending profile experiments that evaluate the effect of the laminar jamming structures on the way the fingers bend, iv) grasp quality assessment experiments that focus on the effect of the inflatable thumb on enhancing grasp stability, and v) grasping experiments involving everyday objects and seven subjects. The hybrid assistive, exoskeleton glove considerably improves the grasping capabilities of the user, being able to exert the forces required to execute a plethora of activities of daily living. All files that allow the replication of the device are distributed in an open-source manner. INDEX TERMS Robotic exoskeletons, assistive devices, robotic rehabilitation, human augmentation, soft robotics.
Myosin isoforms and regulation of tonic and phasic contraction in smooth muscle

MYOSIN ISOFORMS AND REGULATION OF TONIC AND PHASIC CONTRACTION IN SMOOTH MUSCLE Qian Huang, B.S. Marquette University, 2012 The contractile properties of smooth muscle (SM) are broadly classified as tonic and phasic. Among the hypothesized underlying regulatory mechanisms for this difference is the different actomyosin ATPase kinetic properties of SM SMA/SMB myosin heavy chain (MHC) isoforms and the preferential expression of SMA and SMB MHC isoforms in tonic and phasic SM respectively. Thus, we hypothesized that SM SMA/B MHC expression determines tonic and phasic contractile patterns in SM. To test the this hypothesis, the role of SMA and SMB MHC isoforms in tonic and phasic contractions was studied in phasic (longitudinal ileum and stomach circular antrum) and tonic (stomach circular fundus) smooth muscle tissues of SMB knockout mice. Knocking out the SMB MHC gene eliminated SMB MHC protein expression and resulted in up-regulation of the SMA MHC protein without altering the total MHC protein level. Switching from SMB to SMA MHC protein expression decreased the rate of the force transient and increased the sustained tonic force in SMB antrum with high potassium (KPSS) or Carbachol (CCh) stimulation. The sustained tonic force in SMB ileum was also significantly increased with KPSS stimulation but not with CCh. The increased tonic contraction under depolarized condition was not through changes in second messenger signaling pathways (PKC/CPI-17 or Rho/ROCK signaling pathway) or LC20 phosphorylation. Biochemical analyses showed that the expression of contractile regulatory proteins (MLCK, MLCP, PKCδ, and CPI-17) did not change significantly in tissues tested except for PKCα protein expression being significantly decreased in the SMB antrum. However, specifically activating PKCα with phorbol dibutyrate (PDBu) was not significantly different in knockout and wild type tissues, with total force being a fraction of the force generation with KPSS or CCh stimulation in SMB ileum and antrum. Taken together, these data show inhibiting SMB MHC protein expression results in a compensatory increase in the SMA MHC protein expression and enhanced sustained tonic contraction with a reduced rate of force generation in these phasic tissues. These results are consistent with SMA and SMB MHC regulating tonic and phasic contraction in SM.
Spectroscopie des transitions électroniques des cations hydrocarbures aromatiques polycycliques et de leurs agrégats

Ce travail est une etude experimentale de la spectroscopie electronique des cations hydrocarbures aromatiques polycycliques (PAH) et de leurs agregats dans des conditions simulant celles regnant dans le milieu interstellaire, froids et totalement isoles. L’objectif principal est d’obtenir des spectres au laboratoire qui puissent etre compares aux spetres des Bandes Diffuses Interstellaires et d’explorer les proprietes des agregats de PAH.
The effects of supplementation of salts of organic acids on production performance in layers: A Review

The poultry industry is a well-organized and fast growing sector of Indian economy. Introduction of improved breeds, nutritional standards, knowledge of disease prevention and scientific management practices have increased meat and egg production significantly. Antibiotics have been widely used in poultry production for decades to improve growth rate and feed conversion efficiency, however, their use as growth promoters in the poultry industry has been intensively controversial because of the development of bacterial resistance and potential consequences on the human health (Ratcliff, 2000). In response to this apparent threat, the European Commission (EC) decided to phase out, and ultimately ban (January, 2006), the marketing and use of antibiotics as growth promoters in feed (EC Regulation No. 1831/2003). Now the poultry sector is continuously searching for new feed additives in order to improve the feed efficiency with minimum deleterious effects on animal health. A modernistic challenge in the poultry production is to exploit the use of specific dietary supplements to boost the intrinsic potential of poultry birds in order to perform better.
Occurrence of thermophilic Campylobacter in an organised cattle farm in kerala, India

Foodborne diseases comprise a broad spectrum of illnesses and an escalating public health problem worldwide. Foods of animal origin and drinking water are commonly considered as the key source of food borne infections. Campylobacter spp. are important zoonotic bacterial pathogens which mainly cause foodborne enteritis in humans. The organism is generally dispersed across a wide range of animals, including livestock, poultry, and wildlife, and is transmitted to human beings mainly by the consumption of contaminated food, water, and milk. In this study the occurrence of thermophilic campylobacter in an organised farm in Kerala was evaluated. The overall prevalence rate of thermophilic Campylobacter was found to be 16 per cent by multiplex polymerase chain reaction with dung (four per cent), milk (four per cent), water (four per cent) and soil samples were found to harbour Campylobacter spp. Two Campylobacter coli isolates were obtained from the faecal samples and the isolates were sensitive to amikacin, amoxycillin, chloramphenicol, ciprofloxacin, clindamycin, dorepenem, doxycycline, gentamicin, imipenem and meropenem and resistant to cefixime, cefotaxime, cefuroxime, ceftazidime, enrofloxacin and tetracycline.
Kinetic samplers for neural quantum states

Neural quantum states (NQS) are a novel class of variational many-body wave functions that are very flexible in approximating diverse quantum states. Optimization of an NQS ansatz requires sampling from the corresponding probability distribution defined by squared wave function amplitude. For this purpose we propose to use kinetic sampling protocols and demonstrate that in many important cases such methods lead to much smaller autocorrelation times than Metropolis-Hastings sampling algorithm while still allowing to easily implement lattice symmetries (unlike autoregressive models). We also use Uniform Manifold Approximation and Projection algorithm to construct two-dimensional isometric embedding of Markov chains and show that kinetic sampling helps attain a more homogeneous and ergodic coverage of the Hilbert space basis.
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping

Deep Neural Networks are known to be very demanding in terms of computing and memory requirements. Due to the ever increasing use of embedded systems and mobile devices with a limited resource budget, designing low-complexity models without sacrificing too much of their predictive performance gained great importance. In this work, we investigate and compare several well-known methods to reduce the number of parameters in neural networks. We further put these into the context of a recent study on the effect of the Receptive Field (RF) on a model's performance, and empirically show that we can achieve high-performing low-complexity models by applying specific restrictions on the RFs, in combination with parameter reduction methods. Additionally, we propose a filter-damping technique for regularizing the RF of models, without altering their architecture and changing their parameter counts. We will show that incorporating this technique improves the performance in various low-complexity settings such as pruning and decomposed convolution. Using our proposed filter damping, we achieved the 1st rank at the DCASE-2020 Challenge in the task of Low-Complexity Acoustic Scene Classification.
Blow-up analysis of hydrodynamic forces exerted on two adjacent $M$-convex particles

In a viscous incompressible fluid, the hydrodynamic forces acting on two close-to-touch rigid particles in relative motion always become arbitrarily large, as the interparticle distance parameter $\varepsilon$ goes to zero. In this paper we obtain asymptotic formulas of the hydrodynamic forces and torque in $2\mathrm{D}$ model and establish the optimal upper and lower bound estimates in $3\mathrm{D}$, which sharply characterizes the singular behavior of hydrodynamic forces. These results reveal the effect of the relative convexity between particles, denoted by index $m$, on the blow-up rates of hydrodynamic forces. Further, when $m$ degenerates to infinity, we consider the particles with partially flat boundary and capture that the largest blow-up rate of the hydrodynamic forces is $\varepsilon^{-3}$ both in 2D and 3D. We also clarify the singularities arising from linear motion and rotational motion, and find that the largest blow-up rate induced by rotation appears in all directions of the forces.
Challenges and strategies for running controlled crowdsourcing experiments

This paper reports on the challenges and lessons we learned while running controlled experiments in crowdsourcing platforms. Crowdsourcing is becoming an attractive technique to engage a diverse and large pool of subjects in experimental research, allowing researchers to achieve levels of scale and completion times that would otherwise not be feasible in lab settings. However, the scale and flexibility comes at the cost of multiple and sometimes unknown sources of bias and confounding factors that arise from technical limitations of crowdsourcing platforms and from the challenges of running controlled experiments in the “wild”. In this paper, we take our experience in running systematic evaluations of task design as a motivating example to explore, describe, and quantify the potential impact of running uncontrolled crowdsourcing experiments and derive possible coping strategies. Among the challenges identified, we can mention sampling bias, controlling the assignment of subjects to experimental conditions, learning effects, and reliability of crowdsourcing results. According to our empirical studies, the impact of potential biases and confounding factors can amount to a 38% loss in the utility of the data collected in uncontrolled settings; and it can significantly change the outcome of experiments. These issues ultimately inspired us to implement CrowdHub, a system that sits on top of major crowdsourcing platforms and allows researchers and practitioners to run controlled crowdsourcing projects.
Asynchronous Deep Model Reference Adaptive Control

In this paper, we present Asynchronous implementation of Deep Neural Network-based Model Reference Adaptive Control (DMRAC). We evaluate this new neuro-adaptive control architecture through flight tests on a small quadcopter. We demonstrate that a single DMRAC controller can handle significant nonlinearities due to severe system faults and deliberate wind disturbances while executing high-bandwidth attitude control. We also show that the architecture has long-term learning abilities across different flight regimes, and can generalize to fly different flight trajectories than those on which it was trained. These results demonstrating the efficacy of this architecture for high bandwidth closed-loop attitude control of unstable and nonlinear robots operating in adverse situations. To achieve these results, we designed a software+communication architecture to ensure online real-time inference of the deep network on a high-bandwidth computation-limited platform. We expect that this architecture will benefit other deep learning in the closed-loop experiments on robots.
End-to-end-Architekturen zur Datenmonetarisierung im IIoT. Konzepte und Implementierungen

The value creation potential of the Internet of Things (IoT), that is the connection of arbitrary objects to the Internet, lies in the creation of business benefits through accessing and processing the circa 80 Zettabytes (1 ZB = 10^21 Bytes) of data produced by an estimated 40 billions of IoT endpoints (prognosis for 2025). This contribution derives and presents the information technology-related fundament and basis required to be able to reap this potential. Quantity and heterogeneity of the devices and machines especially encountered in the industry at large in the so-called industrial IoT (IIoT) require the use of a typically cloud-based IoT platform for logical concentration and more efficient management of the -- unavoidable in industry -- complexity. Stringent non-functional requirements especially regarding (low) latency, (high) bandwidth, access to large processing capacities, and security and privacy-related aspects necessitate the deployment of intermediary IoT gateways endowed with different capability sets in the edge continuum between IoT endpoints and the IoT platform in the cloud. This will be illustrated in the form of two use cases from corporate projects using a component architecture view point. Finally we argue that this classical concept of IoT projects needs to be strategically widened towards application integration (key word: IT/OT integration) and API management resulting in the coupling of a suitable integration and API management platforms to the IoT platform in order to use this end-to-end understanding of IoT/IIoT to fully leverage the innovation-stimulating and transformational character of IIoT and Industry 4.0.
Architecture Agnostic Neural Networks

In this paper, we explore an alternate method for synthesizing neural network architectures, inspired by the brain's stochastic synaptic pruning. During a person's lifetime, numerous distinct neuronal architectures are responsible for performing the same tasks. This indicates that biological neural networks are, to some degree, architecture agnostic. However, artificial networks rely on their fine-tuned weights and hand-crafted architectures for their remarkable performance. This contrast begs the question: Can we build artificial architecture agnostic neural networks? To ground this study we utilize sparse, binary neural networks that parallel the brain's circuits. Within this sparse, binary paradigm we sample many binary architectures to create families of architecture agnostic neural networks not trained via backpropagation. These high-performing network families share the same sparsity, distribution of binary weights, and succeed in both static and dynamic tasks. In summation, we create an architecture manifold search procedure to discover families or architecture agnostic neural networks.
Realization of a quantum Fredkin gate with hybrid degrees of freedom in a single photon

Optimizing the physical realization of quantum gates is important to build a quantum computer. Fredkin gate, also named controlled-swap gate, can be widely applicable in various quantum information processing schemes. In the present research, we propose and experimentally implement quantum Fredkin gate in a single-photon hybrid-degrees-of-freedom system. Polarization is used as the control qubit, and SWAP operation is achieved in a four-dimensional Hilbert space spanned by photonic orbital angular momentum. The effective conversion rate $\mathcal{P}$ of the quantum Fredkin gate in our experiment is $(95.4\pm 2.6)\%$. Besides, we find that a kind of Greenberger-Horne-Zeilinger states can also be prepared by using our quantum Fredkin gate, and the Mermin inequality test shows its quantum contextual characteristic. Our experimental design paves the way for quantum computing and quantum fundamental study in high-dimensional and hybrid coding quantum systems.
Universal Multi-Source Domain Adaptation

Unsupervised domain adaptation enables intelligent models to transfer knowledge from a labeled source domain to a similar but unlabeled target domain. Recent study reveals that knowledge can be transferred from one source domain to another unknown target domain, called Universal Domain Adaptation (UDA). However, in the real-world application, there are often more than one source domain to be exploited for domain adaptation. In this paper, we formally propose a more general domain adaptation setting, universal multi-source domain adaptation (UMDA), where the label sets of multiple source domains can be different and the label set of target domain is completely unknown. The main challenges in UMDA are to identify the common label set between each source domain and target domain, and to keep the model scalable as the number of source domains increases. To address these challenges, we propose a universal multi-source adaptation network (UMAN) to solve the domain adaptation problem without increasing the complexity of the model in various UMDA settings. In UMAN, we estimate the reliability of each known class in the common label set via the prediction margin, which helps adversarial training to better align the distributions of multiple source domains and target domain in the common label set. Moreover, the theoretical guarantee for UMAN is also provided. Massive experimental results show that existing UDA and multi-source DA (MDA) methods cannot be directly applied to UMDA and the proposed UMAN achieves the state-of-the-art performance in various UMDA settings.
Generation of gravitational waves and tidal disruptions in clumpy galaxies

Obtaining a better understanding of intermediate-mass black holes (IMBHs) is crucial, as their properties could shed light on the origin and growth of their supermassive counterparts. Massive star-forming clumps, which are present in a large fraction of massive galaxies at z ∼ 1–3, are among the venues wherein IMBHs could reside. We perform a series of Fokker–Planck simulations to explore the occurrence of tidal disruption (TD) and gravitational wave (GW) events about an IMBH in a massive star-forming clump, modelling the latter so that its mass ($10^8 \, {\rm M}_{\odot}$) and effective radius (100 pc) are consistent with the properties of both observed and simulated clumps. We find that the TD and GW event rates are in the ranges of 10−6 to 10−5 and 10−8 to 10−7 yr−1, respectively, depending on the assumptions for the initial inner density profile of the system (ρ ∝ r−2 or ∝ r−1) and the initial mass of the central IMBH (105 or $10^3\, {\rm M}_{\odot}$). By integrating the GW event rate over z = 1–3, we expect that the Laser Interferometer Space Antenna will be able to detect ∼2 GW events per year coming from these massive clumps; the intrinsic rate of TD events from these systems amounts instead to a few 103 per year, a fraction of which will be observable by e.g. the Square Kilometre Array and the Advanced Telescope for High Energy Astrophysics. In conclusion, our results support the idea that the forthcoming GW and electromagnetic facilities may have the unprecedented opportunity of unveiling the lurking population of IMBHs.
High eccentricities and high masses characterize gravitational-wave captures in galactic nuclei as seen by Earth-based detectors

The emission of gravitational waves (GWs) during single–single close encounters in galactic nuclei (GNs) leads to the formation and rapid merger of highly eccentric stellar-mass black hole (BH) binaries. The distinct distribution of physical parameters makes it possible to statistically distinguish this source population from others. Previous studies determined the expected binary parameter distribution for this source population in single GNs. Here, we take into account the effects of dynamical friction, post-Newtonian corrections, and observational bias to determine the detected sources’ parameter distributions from all GNs in the Universe. We find that the total binary mass distribution of detected mergers is strongly tilted towards higher masses. The distribution of initial peak GW frequency is remarkably high between 1 and 70 Hz, $\sim \! 50{{\ \rm per\ cent}}$ of GW capture sources form above 10 Hz with e ≳ 0.95. The eccentricity when first entering the LIGO/Virgo/KAGRA band satisfies $e_{\rm 10\, Hz}\gt 0.1$ for over $92{{\ \rm per\ cent}}$ of sources and $e_{\rm 10\, Hz}\gt 0.8$ for more than half of the sources. At the point when the pericentre reaches 10GM/c2 the eccentricity satisfies e10M > 0.1 for over $\sim \! 70{{\ \rm per\ cent}}$ of the sources, making single–single GW capture events in GNs the most eccentric source population among the currently known stellar-mass binary BH merger channels in our Universe. We identify correlations between total mass, mass ratio, source detection distance, and eccentricities $e_{\rm 10\, Hz}$ and e10M. The recently measured source parameters of GW190521 lie close to the peak of the theoretical distributions and the estimated escape speed of the host environment is $\sim \! 7.5\times 10^3\! -\! 1.2\times 10^4\, \rm km\, s^{-1}$, making this source a candidate for this astrophysical merger channel.
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples

Unsupervised anomalous sound detection is concerned with identifying sounds that deviate from what is defined as 'normal', without explicitly specifying the types of anomalies. A significant obstacle is the diversity and rareness of outliers, which typically prevent us from collecting a representative set of anomalous sounds. As a consequence, most anomaly detection methods use unsupervised rather than supervised machine learning methods. Nevertheless, we will show that anomalous sound detection can be effectively framed as a supervised classification problem if the set of anomalous samples is carefully substituted with what we call proxy outliers. Candidates for proxy outliers are available in abundance as they potentially include all recordings that are neither normal nor abnormal sounds. We experiment with the machine condition monitoring data set of the 2020's DCASE Challenge and find proxy outliers with matching recording conditions and high similarity to the target sounds particularly informative. If no data with similar sounds and matching recording conditions is available, data sets with a larger diversity in these two dimensions are preferable. Our models based on supervised training with proxy outliers achieved rank three in Task 2 of the DCASE2020 Challenge.