Miracle-dz/UniFAR-FTUs huggingface dataset:
Real space observation of spin frustration in Cr on a triangular lattice

Using spin-polarized scanning tunneling microscopy (SP-STM), we observe spin frustration in one monolayer Cr on the triangular lattice of the Pd(111) surface. Our STM measurements demonstrate pseudomorphic growth of the first Cr layer on Pd(111) without intermixing. Using SP-STM in the constant current mode, we observe three different types of images depending on the tip magnetization indicative of a noncollinear $(\sqrt{3}\ifmmode\times\else\texttimes\fi{}\sqrt{3})$ magnetic superstructure with an angle of $120\ifmmode^\circ\else\textdegree\fi{}$ between moments of nearest-neighbor Cr atoms. The $120\ifmmode^\circ\else\textdegree\fi{}$ N\'eel ground state of Cr/Pd(111) and the SP-STM images are explained based on our first-principles calculations.
Study and Analysis on CNTFET Design and Process Parameters for Performance Optimization

Carbon Nanotube Field Effect Transistor (CNTFET) technology is one of the favorable devices to replace MOSFET technology. A lot of research has been done to further enhance the CNTFET performance. This paper studies the implementation of Taguchi method to further optimize CNTFET design parameters. In this research, the HSPICE simulator is used to simulate the Stanford University CNFET model. The factors involved in the design of experiment include the diameter of CNT, oxide thickness and the dielectric constant. By using Taguchi method, the optimum combination of the three-design parameter is 1.0nm of CNT, 1.0 nm of oxide layer and Zirconium Dioxide (25) as the dielectric material. The optimum CNTFET design manages to produce the highest current ratio (Ion/Ioff. The ANOVA analysis is used to evaluate the percentage of each design parameter that affects the current ratio of the CNTFET. The result shows that the diameter of CNT had a significant effect towards the current ratio of 87.24%. The optimized CNTFET is applied to an inverter circuit to study the circuit performance in terms of propagation delay, power consumption and Power-Delay Product (PDP). From the study, the optimized CNTFET inverter had a better performance in term of PDP with $4.1984 \text{x}10^{-18}\text{J}$ compared to unoptimized CNTFET inverter design.
An Improved Noise Reduction Technique for Enhancing the Intelligibility of Sinewave Vocoded Speech: Implication in Cochlear Implants

A cochlear implant (CI) is the most suitable option for individuals with severe profound hearing loss. CI restores the audibility to near perfection and offers good speech understanding in quiet. However, the speech perception in noise with CIs is less optimal as most speech coding strategies of CIs encode only the temporal envelope. Besides the current CI signal coding strategies lacks sophisticated pre-processing. In the current study, we proposed a novel pre-processing method to improve speech Intelligibility in noise and tested using the acoustic simulations of cochlear implants. The proposed noise reduction technique aims to minimize the mean square error (MSE) between the temporal envelopes of the enhanced speech and its clean speech. Therefore, the proposed method will be suitable for CI applications. This paper provides an analysis of the theoretical derivation of the noise suppression function and also the performance evaluation using objective and subjective tests. The effectiveness of the proposed method was objectively evaluated using the SRMR-CI and ESTOI. Additionally, speech recognition through the acoustic simulations of the cochlear implant was done for the subjective evaluation. Performance of the proposed method was compared with the Weiner filter (WF) and sigmoidal functions. The sinewave vocoder was used to simulate the cochlear implant perception. Both objective and subjective scores revealed that the performance of the proposed technique is superior to the WF and sigmoidal function.
Electronic Nose and Deep Learning Approach in Identifying Ripe Lycopersicum esculentum L. TomatoFruit

Identification techniques that determine the ripeness of fruits already have several approaches, but most of the methods use instrumental methods that require sophisticated equipment operated by trained technicians. Electronic nose systems have already been used to identify the freshness and ripeness of other fruits, including tomatoes. This is due to the own volatile organic compound profiles of fruits in various stages of maturity. In this study, an electronic nose system is made using a gas sensor array, Raspberry Pi 3B, and a deep learning model. The model is trained using 150 samples wherein data augmentation is implemented to produce 750 grayscale image patterns. The tomatoes are classified into three: ripe, not ripe, and unknown. During training, the model that undergoes data augmentation and uses 24×8 input data has the highest mean score, 0.96. This model is used in the electronic nose system, and 43 out of 50 samples are correctly identified, which is an 86% accuracy.
BARIUM HEXAFERRITE THICK FILMS BY HOT PRESS SINTERING

BaFe12O19 ferrite thick films were screen-printed and sintered at 1150°C under pressure (hot press sintering). The effects of the hot press sintering on the properties of barium hexaferrite thick films were investigated. The X-ray diffraction pattern shows improvement of the (00l) texture under pressure. The porosity size of the hot press sintering samples decreases obviously and the surface compactness is improved as the pressure is increased. The films are quite anisotropic with magnetic easy axis perpendicular to the film plane. The largest remanence ratio achieved is 0.75 under the highest pressure of 4 MPa.
LEP and LEPR are possibly a double‐edged sword for wound healing

Wound healing is a complex and error‐prone process. Wound healing in adults often leads to the formation of scars, a type of fibrotic tissue that lacks skin appendages. Hypertrophic scars and keloids can also form when the wound‐healing process goes wrong. Leptin (Lep) and leptin receptors (LepRs) have recently been shown to affect multiple stages of wound healing. This effect, however, is paradoxical for scarless wound healing. On the one hand, Lep exerts pro‐inflammatory and profibrotic effects; on the other hand, Lep can regulate hair follicle growth. This paper summarises the role of Lep and LepRs on cells in different stages of wound healing, briefly introduces the process of wound healing and Lep and LepRs, and examines the possibility of promoting scarless wound healing through spatiotemporal, systemic, and local regulation of Lep levels and the binding of Lep and LepRs.
Diagnostic value of odd‐integer half‐harmonic emission from laser‐produced plasmas

The diagostic value of odd‐integer half‐harmonic emission from laser‐produced plasmas is evaluated in the light of recent spectral measurements on UV irradiation experiments. It has been found that under certain conditions a sharp, slightly red‐shifted feature is observed in the ω/2 spectra. This feature has been identified with a particular mode of the 2ωp decay instability for which one of the plasmon wave vectors vanishes. This feature is eminently well suited for coronal electron temperature measurements. Another half‐harmonic (blue‐shifted) feature is more easily observed and may serve as a secondary—though less accurate—temperature diagnostic. In contrast, the spectral splitting of the (3)/(2) harmonic emission proved to be ill suited for temperature diagnostics because of its sensitivity to irradiation and observation geometry. Either ω/2 or 3ω/2 emission is, however, a good qualitative indicator for the presence of the 2ωp decay instability although quantitative inferences on the level of the 2ωp .
Avifaunal diversity in Indian Institute of Technology Guwahati Campus, Assam, India

Indian Institute of Technology - Guwahati (IITG), Assam, is an ecologically rich campus hosting different species of birds, butterflies and mammals. It accommodates several migratory and resident species of birds across different seasons. However, information is scanty on avian diversity with respect to the different habitats of the campus. Therefore, the present study attempts to gain insight into avian diversity with respect to habitat heterogeneity by considering the species presence-absence dataset collected for three years (2017–2020). A multivariate Beta (β) diversity analysis is carried out for the IITG campus constituted of five primary habitats, viz., secondary growth, eco–forest, water bodies, swampy-marshy area, and constructions. Of 152 bird species observed in the IITG campus, the highest number is reported from secondary growth, followed by eco-forest. The multivariate analysis shows that the average β–diversity for the IITG campus is approximately equal to 79%, which is in accordance with another published study. These observations are examined in light of hypotheses and phenomena documented in the literature, such as habitat heterogeneity hypothesis, niche-based hypothesis and anthropogenic impact on habitats. The study also establishes that the IITG is among the educational institutes and campuses that host many migratory bird species. Lastly, based on the outcomes of β–diversity analysis, it is suggested that the conservation effort for avian species in the campus should be directed towards individual habitats uniformly.
High-Order Neural Networks: Information Storage without Errors

A new learning rule is derived, which allows the perfect storage and the retrieval of information and sequences, in neural networks exhibiting high-order interactions between some or all neurons. Such interactions increase the storage capacity of the networks and allow to solve a class of problems which were intractable with standard networks. We show that it is possible to restrict the amount of high-order interactions while improving the attractivity of the stored patterns.
EMG-based Hybrid Impedance-Force Control for Human-Robot Collaboration on Ultrasound Imaging

Ultrasound (US) imaging is a common but physically demanding task in the medical field, and sonographers may need to put in considerable physical effort for producing high-quality US images. During physical human-robot interaction on US imaging, robot compliance is a critical feature that can ensure human user safety while automatic force regulation ability can help to improve task performance. However, higher robot compliance may mean lower force regulation accuracy, and vice versa. Especially, the contact/non-contact status transition can largely affect the control system stability. In this paper, a novel electromyography (EMG)-based hybrid impedance-force control system is developed for US imaging task. The proposed control system incorporates the robot compliance and force regulation ability via a hybrid controller while the EMG channel enables the user to online modulate the trade-off between the two features as necessary. Two experiments are conducted to examine the hybrid controller and show the necessity of involving an EMG-based modulator. A proof-of-concept study on US imaging is performed with implementing the proposed EMG-based control system, and the effectiveness is demonstrated. The proposed control system is promising to ensure robot's stability and patient's safety, thus obtain high-quality US images, while monitoring and reducing sonographer's fatigue. Furthermore, it can be easily adapted to other physically demanding tasks in the field of medicine.
Elements of spatial structure : a quantitative approach

1. Introduction Part I. Static Aspects of Regional Structure: 2. Regions as combinatorial structures 3. Regions as ordered series 4. Regions as surfaces Part II. Dynamic Aspects of Regional Structure: 5. Spatial comparison of time series: a framework 6. Spatial comparison of time series: I. Contagious processes 7. Spatial comparison of time series: II. Unemployment in South-West England Part III. Autocorrelation and Forecasting: 8. Spatial autocorrelation 9. The analysis of regional patterns by nearest-neighbour methods 10. Regional forecasting Appendices.
Acoustical properties of porous materials-Generalizations of empirical models-

Empirical models for the characteristic impedance and propagation constant of porous materials given by Delany and Bazley and by the author are generalized with respect to the porosity, the tortuosity and the pore shape factor ratio. The new models have these three parameters besides the flow resistivity. For a practical use, the impedance model can be described with two parameters, the effective flow resistivity and the ratio of tortuosity to porosity. An excellent agreement with Attenborough's four-parameter model shows that the new models are useful for the prediction of acoustical behavior of a wide variety of ground surfaces.
Improved Performance of CPG Parameter Inference for Path-following Control of Legged Robots

The difficulty associated with the coordinated locomotion of legged robots grows quickly as the number of joints increases. Although prior approaches have addressed this problem through sampling-based planners, learning-based techniques have recently been explored as a means to handle such complexity. Among these recent approaches are systems that utilize probabilistic graphical models in order to infer parameters for central pattern generators (CPGs) which enable the path-following locomotion of highly-articulated legged robots through unstructured terrain. This paper presents a novel formulation of a CPG parameter inference-based path-following controller. The new inference process and accompanying CPG formulation enforce oscillator convergence to the limit-cycle specified by the inferred parameters in addition to biasing towards parameters that quickly reach stable-state. This formulation is shown to improve the performance of CPG parameter inference-based path-following control for legged robots across a number of simulated and physical experiments.
OSMO-Microwave Drying of Pineapple (Ananas comosus) Slices: Mass Transfer Kinetics and Product Quality Characterization

This present research aimed to investigate the effect of slice thickness and concentration of the osmotic solution on mass transfer kinetics, the color profile of osmotically dehydrated pineapple slices, and product quality characteristics of osmotically dehydrated microwave-dried (ODMWD) products. Three slice thicknesses (0.5, 1, and 1.5 cm) and three concentrations of osmotic solution (40, 50, and 60 °Brix) were used. The mass transfer kinetics (moisture reduction behaviour, weight loss, solid gain), physicochemical properties (color, TSS, pH, titratable acidity, vitamin C, and total sugar), and total phenolic content of pineapple slices were analyzed. During osmotic dehydration, the moisture reduction behaviour of 0.5 cm slices was faster in all osmotic solutions, whereas water loss and solid gain were higher for all slices treated with 60 °Brix. Both slice thickness and concentration of the solution significantly affected the color of OD pineapple slices. For ODMWD products, total soluble solids (TSS), pH, ascorbic acid content, total sugar, and total phenolic content increased for all slice thicknesses with an increase in osmotic solution concentration, whereas titratable acidity exhibited the opposite result. The rehydration ratio was higher in 0.5 cm slices for all solution concentrations. According to the finding, pineapple fruits can be dehydrated by using 60 °Brix solution concentration with 0.5 cm slices for making dehydrated pineapple fruit, and osmotic dehydration followed by microwave drying of pineapple fruit could be used for value-added processing products.
Attention-guided RGB-D Fusion Network for Category-level 6D Object Pose Estimation

This work focuses on estimating 6D poses and sizes of category-level objects from a single RGB-D image. How to exploit the complementary RGB and depth features plays an important role in this task yet remains an open question. Due to the large intra-category texture and shape variations, an object instance in test may have different RGB and depth features from those of the object instances in training, which poses challenges to previous RGB-D fusion methods. To deal with such problem, an Attention-guided RGB-D Fusion Network (ARF-Net) is proposed in this work. Our key design is an ARF module that learns to adaptively fuse RGB and depth features with guidance from both structure-aware attention and relation-aware attention. Specifically, the structure-aware attention captures spatial relationship among object parts and the relation-aware attention captures the RGB-to-depth correlations between the appearance and geometric features. Our ARF -Net directly establishes canonical correspondences with a compact decoder based on the multi-modal features from our ARF module. Extensive experiments show that our method can effectively fuse RGB features to various popular point cloud encoders and provide consistent performance improvement. In particular, without reconstructing instance 3D models, our method with its relatively compact architecture outperforms all state-of-the-art models on CAMERA25 and REAL275 benchmarks by a large margin.
Safe and Ergonomic Human-Drone Interaction in Warehouses

This paper presents an application of human-drone interaction (HDI) for inventory management in a ware-house 4.0 that aims at improving the operators' safety and well-being together with increasing efficiency and reducing production costs. In our work, the speed and separation monitoring (SSM) methodology is applied for the first time to HDI, in analogy to the human-robot interaction (HRI) ISO safety requirements as well as the rapid upper limb assessment (RULA), for evaluating the operator's ergonomic posture during the interaction with the drone. With the aim of validating the proposed approach in a realistic scenario, a quadrotor is controlled to perform a pick and place task along a desired trajectory, from the picking bay to the palletizing area where the operator is located, avoiding collisions with the warehouse shelves by implementing the artificial potential field technique (APF) for planning and the linear quadratic regulator (LQR) and iterative LQR (iLQR) algorithms for tracking. The obtained results of the HDI architecture simulations are presented and discussed in detail proving the effectiveness of the proposed method for a safe and ergonomic HDI.
Confined Water Body Coverage under Resource Constraints

This paper presents a novel algorithm for monitoring marine environments utilizing a resource-constrained robot. Collecting water quality data from large bodies of water is paramount for monitoring the ecosystem's health, particularly for predicting harmful cyanobacteria blooms. The large spatial dimensions of such bodies of water and the slow varying of water quality parameters make exhaustive, complete coverage impractical and unnecessary. This work explores a new strategy for efficiently measuring water quality quantities with an autonomous surface vehicle (ASV). The method utilizes the medial axis of the water body producing a guideline for the ASV trajectory that visits representative areas of the environment. The proposed method ensures data collection in the narrower parts of the lake, where researchers have historically observed harmful blooms while also visiting open water areas. It also presents an analysis of the Spatio-temporal sensitivity of the target sensor. A comparison with the traditional lawnmower algorithm demonstrates that the conventional BCD-based complete coverage method cannot sample the small coves of a lake. As such, we show that the proposed method captures more diverse regions of the area with a partial coverage technique. Offline analysis of several lakes and reservoirs and results from field deployments at Lake Murray, SC, USA, demonstrate the proposed method's effectiveness.
Vertical Bend and T-branch Travels of an Articulated Wheeled In-pipe Inspection Robot by Combining Its Joint Angle and Torque Controls

The paper reports the performance verification of vertical bend and T-branch travels of an articulated wheeled in-pipe inspection robot. The robot is composed of only a single active compliant middle joint, two passive compliant joints, three drive wheels, and two roll wheels. The passage of the bend pipe is achieved only by the joint torque control, while the T-branch travel is achieved by controlling both joint angle and torque. Instead of using a torque sensor, a polyurethane-based series elastic actuator (SEA) is installed in the middle joint. In this paper, the travel performances of our developed in-pipe robot were tested on bend pipes and 10 types of T-branch with different gravity directions. From the experiments, in all cases, the effectiveness of the bend and T-branch travels performance was confirmed.
Cache Antagonists Identification: A Practice from Alibaba Colocation Datacenter

Colocating latency-critical (LC) jobs and best-effort (BE) jobs on a host effectively improve resource efficiency in modern datacenters. But it increases resource contention between jobs, which seriously affects job performance. In Alibaba's real- world LC- BE colocation datacenters, we observed that cache is one of the most contended resources in the CPU. When cache contention occurs, identifying the antagonists that caused cache resource contention is the first step to mitigate cache contention, called cache antagonists identification (CAl). However, it is chal-lenging to identify cache antagonists because cache contention is difficult to observe and quantify. In this paper, we first propose cache usage graph (CUG) to finely characterize cache usage of jobs in the multiple CPU microarchitectural hierarchies and locations, and we provide a monitoring tool to collect “per-container-per-logic CPU” Ll/2/3 cache misses and build CUG. Then we propose a CUG-based CAl approach, $\mu$ Tactic. $\mu$ Tactic leverages machine learning models to quantify the cache contention on every cache hierarchy, then reasons out the cache antagonists with CUG. Experiments in production datacenters show that $\mu$ Tactic has a high precision (85+%) and low cost (32 ms), which are better than state-of-the-art approaches.
Modeling and analyzing multiple station baggage screening security system performance

In the aftermath of the tragic events of 11 September 2001, numerous changes have been made to aviation security policy and operations throughout the nation's airports. The allocation and utilization of checked baggage screening devices is a critical component in aviation security systems. This paper formulates problems that model multiple sets of flights originating from multiple stations (e.g., airports, terminals), where the objective is to optimize a baggage screening performance measure subject to a finite amount of resources. These measures include uncovered flight segments (UFS) and uncovered passenger segments (UPS). Three types of multiple station security problems are identified and their computational complexity is established. The problems are illustrated on two examples that use data extracted from the Official Airline Guide. The examples indicate that the problems can provide widely varying solutions based on the type of performance measure used and the restrictions imposed by the security device allocations. Moreover, the examples suggest that the allocations based on the UFS measure also provide reasonable solutions with respect to the UPS measure; however, the reverse may not be the case. This suggests that the UFS measure may provide more robust screening device allocations. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005.