Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Resolution Investigation of the Multi-Phase ISM in a Galaxy during the First Two Billion Years

We have carried out the first spatially-resolved investigation of the multi-phase interstellar medium (ISM) at high redshift, using the z = 4.24 strongly-lensed sub-millimetre galaxy H-ATLASJ142413.9+022303 (ID141). We present high-resolution (down to ∼350 pc) ALMA observations in dust continuum emission and in the CO(7-6), $\rm H_2O (2_{1,1} - 2_{0,2})$, [ C i ] (1-0) and [ C i ] (2-1) lines, the latter two allowing us to spatially resolve the cool phase of the ISM for the first time. Our modelling of the kinematics reveals that the system appears to be dominated by a rotationally-supported gas disk with evidence of a nearby perturber. We find that the [ C i ] (1-0) line has a very different distribution to the other lines, showing the existence of a reservoir of cool gas that might have been missed in studies of other galaxies. We have estimated the mass of the ISM using four different tracers, always obtaining an estimate in the range $\rm 3.2-3.8 \times 10^{11}\ M_{\odot }$, significantly higher than our dynamical mass estimate of $\rm 0.8-1.3 \times 10^{11}\ M_{\odot }$. We suggest that this conflict and other similar conflicts reported in the literature is because the gas-to-tracer ratios are ≃4 times lower than the Galactic values used to calibrate the ISM in high-redshift galaxies. We demonstrate that this could result from a top-heavy initial mass function and strong chemical evolution. Using a variety of quantitative indicators, we show that, extreme though it is at z = 4.24, ID141 will likely join the population of quiescent galaxies that appears in the Universe at z ∼ 3.
Outbreak of COVID-19 among vaccinated and unvaccinated homeless shelter residents- Sonoma County, California, July 2021

On Saturday, July 3, 2021, the Sonoma County Health Department was alerted to three cases of COVID-19 among residents of a homeless shelter in Santa Rosa, California. Among 153 shelter residents, 83 (54%) were fully vaccinated; 71 (86%) vaccinated residents had received the Janssen COVID-19 vaccine and 12 (14%) received an mRNA (Pfizer BioNTech or Moderna) COVID-19 vaccine. Within 1 month, 116 shelter residents (76%) received positive SARS-CoV-2 test results, including 66 (80%) of 83 fully vaccinated residents and 50 (71%) of 70 residents who were not fully vaccinated. Nine (14%) of 66 fully vaccinated cases, compared with 1 of 50 (2%) of unvaccinated cases, were hospitalized with COVID-19. All hospitalized cases had at least one underlying medical condition. Two deaths occurred, one in a vaccinated resident and one in a resident who was not fully vaccinated. Specimens from 52 residents underwent whole genome sequencing; all were identified as SARS-CoV-2 (the virus that causes COVID-19) AY.13, a sublineage of the B.1. 617.2 (Delta) variant. These findings suggest that comprehensive measures including routine testing, individual quarantine, mask wearing, and physical distancing are needed in addition to vaccination in homeless shelters and other similar congregate settings, particularly those with residents who are at high risk for severe COVID-19.
Transport spin polarization of noncollinear antiferromagnetic antiperovskites

Spin-polarized currents play a key role in spintronics. Recently, it has been found that antiferromagnets with a non-spindegenerate band structure can efficiently spin-polarize electric currents, even though their net magnetization is zero. Among the antiferromagnetic metals with magnetic space group symmetry supporting this functionality, the noncollinear antiferromagnetic antiperovskites ANMn3 (A = Ga, Ni, Sn, and Pt) are especially promising. This is due to their high Néel temperatures and a good lattice match to perovskite oxide substrates, offering possibilities of high structural quality heterostructures based on these materials. Here, we investigate the spin polarization of antiferromagnetic ANMn3 metals using first-principles density functional theory calculations. We find that the spin polarization of the longitudinal currents in these materials is comparable to that in widely used ferromagnetic metals, and thus can be exploited in magnetic tunnel junctions and spin transfer torque devices. Moreover, for certain film growth directions, the out-of-plane transverse spin currents with a giant charge-to-spin conversion efficiency can be achieved, implying that the ANMn3 antiperovskites can be used as efficient spin sources. These properties make ANMn3 compounds promising for application in spintronics.
Detecting Underspecification with Local Ensembles

We present local ensembles, a method for detecting underspecification -- when many possible predictors are consistent with the training data and model class -- at test time in a pre-trained model. Our method uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class. We compute this approximation by estimating the norm of the component of a test point's gradient that aligns with the low-curvature directions of the Hessian, and provide a tractable method for estimating this quantity. Experimentally, we show that our method is capable of detecting when a pre-trained model is underspecified on test data, with applications to out-of-distribution detection, detecting spurious correlates, and active learning.
Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation

Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method for temporal action segmentation. Our method hinges on unsupervised representation learning, which, for temporal action segmentation, poses unique challenges. Actions in untrimmed videos vary in length and have unknown labels and start/end times. Ordering of actions across videos may also vary. We propose a novel way to learn frame-wise representations from temporal convolutional networks (TCNs) by clustering input features with added time-proximity conditions and multi-resolution similarity. By merging representation learning with conventional supervised learning, we develop an "Iterative Contrast-Classify (ICC)'' semi-supervised learning scheme. With more labelled data, ICC progressively improves in performance; ICC semi-supervised learning, with 40% labelled videos, performs similarly to fully-supervised counterparts. Our ICC improves MoF by {+1.8, +5.6, +2.5}% on Breakfast, 50Salads, and GTEA respectively for 100% labelled videos.
Harmonic model of power transformer

Appropriate harmonic models of all power system components including power transformers are the basis of power system harmonic analysis. Currently, several harmonic models of transformers have been proposed. This paper firstly summarizes all available models of transformers and then derives a general harmonic model for transformers. Consequentially, the paper details the computation and harmonic analysis of the magnetizing current of a single phase power transformer. Finally, based on the controlled source model of power transformers, the paper derives its practical harmonic equivalent circuit.
PhillyCarShare : First-year social and mobility impacts of carsharing in philadelphia, pennsylvania

One year into a carsharing program in Philadelphia, Pennsylvania, compelling evidence of reduced vehicle ownership, reduced vehicle miles traveled (VMT), and social change was uncovered through member surveys and detailed usage data. Each PhillyCarShare vehicle replaced an average of 23 private vehicles. Members giving up cars overwhelmingly reported driving less and replacing automobile trips with a variety of modes (transit, walking, taxi, and to a lesser extent biking). However, members who simply gained access to cars used PhillyCar-Share primarily to substitute for modes that most resemble driving (taxis, borrowing or renting, and to a lesser extent transit). The average monthly VMT increase of members gaining access to a car was limited to 29.9 mi, whereas the monthly VMT decrease of members who gave up a car appeared to be several hundred miles but not greater than 522 mi. After joining, PhillyCarShare members made travel decisions more judiciously, expressed greater awareness of transportation cos.
Satellite Based Live and Interactive Distance Learning Program in the Field of Geoinformatics - a Perspective of Indian Institute of Remote Sensing, India

Geoinformatics is a highly specialized discipline that deals with Remote Sensing, Geographical Information System (GIS), Global Positioning System (GPS) and field surveys for assessing, quantification, development and management of resources, planning and infrastructure development, utility services etc. Indian Institute of Remote Sensing (IIRS), a premier institute and one of its kinds has played a key role for capacity Building in this specialized area since its inception in 1966. Realizing the large demand, IIRS has started outreach program in basics of Remote Sensing, GIS and GPS for universities and institutions. EDUSAT (Educational Satellite) is the communication satellite built and launched by ISRO in 2004 exclusively for serving the educational sector to meet the demand for an interactive satellite based distance education system for the country. IIRS has used EDUSAT (shifted to INSAT 4 CR recently due to termination of services from EDUSAT) for its distance learning program to impart basic training in Remote Sensing, GIS and GPS, catering to the universities spread across India. The EDUSAT based training is following similar to e-learning method but has advantage of live interaction sessions between teacher and the students when the lecture is delivered using EDUSAT satellite communication. Because of its good quality reception the interactions are not constrained due to bandwidth problems of Internet. National Natural Resource Management System, Department of Space, Government of India, under Standing Committee in Training and Technology funded this unique program to conduct the basic training in Geoinformatics. IIRS conducts 6 weeks basic training course on "Remote Sensing, GIS and GPS" regularly since the year 2007. The course duration is spread over the period of 3 months beginning with the start of the academic year (1st semester) i.e., July to December every year, for university students. IIRS has utilized EDUSAT satellite for conducting 4 six weeks training course during 2007 till 2009 and INSAT 4CR for conducting the next 2 programs. Till March 2011, fifty four universities with the participation of over 4000 students have benefited from the program (Table 7 and Figure 8). IIRS also organized workshops on "EDUSAT based distance learning: experiences & future learning" in 2007, 09 and 2011. Feedbacks have been taken to address the issues on course structure, duration etc. and plan for improvement in future programs and wider participation. Majority of the participants expressed satisfaction and provided positive feedback and willing to participate in the future programs.
Experiences of Swedish Parents Seeking Social Services Support for Their Adult Children With Drug Addiction

Objective: Family members of a person with drug addiction often experience negative impacts on their lives and face barriers to seeking professional support. More knowledge is needed about such barriers so they can be reduced. This article examines the help-seeking experiences of parents of adult children with drug addiction, a group that has received little attention in research. Method: We conducted in-depth interviews with 32 parents of adult children with drug addiction in Sweden. Results: Parents described problems in encounters with social services and barriers to adequate support for their children. On a psychological level, they described feelings of shame and guilt, negative views of social services, and fear of stigma and loss of control that were barriers to seeking professional support. On an interpersonal level, barriers to help were connected to problems in the interaction among parents, children, and social services. On a structural level, barriers pertain to deficiencies in the availability and quality of support measures, inadequate cooperation between authorities, and a shift in responsibility from the state to the individual and the family. Conclusions: Parents of children with drug addiction are a vulnerable group that often experience problems in their contacts with authorities. A more collaborative approach by social services may abate parents’ self-blame and concern and may strengthen their role in their children’s treatment process.
Classification of plant diseases using machine and deep learning

Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.
Recommended Practices for Helicopter Bonding Procedures for Live-Line Work

This document proposes guidelines and definitions for approach distances for contacting conductors, parts, and struc- tures, involving the safe use of a helicopter to position a worker to work on energized and de-energized overhead lines using live-line work methods. This paper will serve as a basis to establish a guide for the use of a helicopter when performing work on electric overhead lines. Two known support systems are covered by this paper. Other support systems must provide equivalent protection. As methods, other than those included in this paper may be developed, they should be thoroughly reviewed, before implementation, by quali- fied professionals knowledgeable in the industries involved. Statement of Purpose: To review and present, using sound engi- neering practices, methods of safely bringing a worker into contact with components of overhead lines while performing live-line work from a helicopter. To utilize safe approach distances, using air as an insulation, es- tablished by ANSI/IEEE standard 516-1995 or applicable govern- mental regulations and develop any essential factors to be consid- ered when applying these distances to the helicopter live-line work methods. To compile information into one document on what is proven and safe live-line helicopter techniques. Comments are solicited for this purpose into one document. Background: Energized line work using a helicopter has been successfully performed throughout the world on voltage up to and including 800 kV. Proven efficiencies of utilizing helicopters for live-line work can be enhanced by developing uniform safety re- quirements.
Design and Analysis of Rib Waveguide Bragg Grating for Multiplexed Biosensing Application

Over the past decade, waveguide Bragg grating (WBG) sensors have assumed significance in biosensing applications, due to the rapid development in fabrication technology of silicon photonic devices. In this paper, we propose and theoretically analyze a multiplexed biosensor based on surface corrugated Bragg gratings on a Rib waveguide structure using Transfer Matrix method. The Rib waveguide was designed with single mode operating condition at the wavelength of 1550 nm. The effective index is calculated numerically using the Effective Index method and has been verified using Mode Analysis in COMSOL Multiphysics. Highest sensitivity of 44 nm/RIU has been recorded for the design with the smallest dimensions. This design has been further used in the multiplexed biosensing structure by utilizing a cascaded configuration of gratings with two different grating pitches to achieve two distinct Bragg reflection peaks. The sensitivity for the two gratings is calculated to be 44 nm/RIU and 48 nm/RIU. The proposed sensor provides a good sensitivity along with multiplexed sensing and is a viable option when fabrication of smaller dimension sensors is not feasible.
Vast Portfolio Selection With Submodular Norm Regularizations

Portfolio selection (or portfolio optimization) has been a fundamental problem in the financial investment world since the modern portfolio theory (a.k.a. mean-variance analysis) was introduced by Harry Markowitz in 1952. The goal of portfolio selection is to assign different portions of dollars to the underlying assets according to a certain investment target. In practice, to overcome the high sensitivity to inevitable estimation errors of the input parameters, regularization techniques have been introduced for portfolio stabilization. Besides, regularization techniques have been shown to be capable of achieving some other goals like selecting sparse portfolios and grouping assets whose returns exhibit colinearity. While achieving such merits, acclaimed regularizers like the l1-norm may bring an adverse effect on the original portfolio design target. In this paper, a novel framework for vast portfolio selection is proposed via the lens of submodular set functions, which can select a sparse portfolio according to the influence that each asset exerts on the overall portfolio risk. An efficient and convergent algorithm based on the alternating direction method of multipliers is developed for problem resolution. The superiority of the proposed portfolio selection framework is demonstrated with numerical simulations on real market data.
PeakRNN and StatsRNN: Dynamic Pruning in Recurrent Neural Networks

This paper introduces two dynamic real-time pruning techniques PeakRNN and StatsRNN for reducing costly multiplications and memory accesses in recurrent neural networks. The methods are demonstrated on a gated recurrent unit in a multi-layer network, solving a single-channel speech enhancement task with a wide variety of real-world acoustic environments and speakers. The performance is compared against the baseline gated recurrent unit and the DeltaRNN method. Compared to the unprocessed speech, the SNR and Perceptual Evaluation of Speech Quality were on average improved by 8.11 dB and 0.43 MOS-LQO, respectively. Additionally, the two proposed methods outperformed DeltaRNN by 0.7 dB and 0.11 MOS-LQO in the two objective measures, while using the same computational budget per timestep and reducing the original operations by 88 %. Furthermore, PeakRNN is fully deterministic, i.e. it is always known in advance how many computations will be executed. Such worst-case guarantees are crucial for real-time acoustics applications.
Bibliometric Analysis of the Use of the Internet of Things in Precision Agriculture

The application of precision agriculture is presented with the objective of spatially and temporally optimizing the life cycle of different crops through technologies, to increase the productivity of agricultural enterprises. In this article, a bibliometric analysis was performed, where a trend study of publications was carried out, extracting the main characteristics of the topic. We also made use of the statistical tool Rstudio together with the Scopus database, where we analyzed about the most relevant sources published from 2010 to 2021 and the exact number of sources published on precision agriculture and IoT. In addition, the most cited articles worldwide on the topic and the production profile by authors and areas (Thematic) indicating the total number of articles published for precision agriculture, agriculture, IoT, etc. are analyzed. This shows that precision agriculture has had a greater impact in Asia and India.
Evaluation of Modern Cotton Harvest Systems on Irrigated Cotton: Harvester Performance

Picker and stripper harvest systems were evaluated on production-scale irrigated cotton on the High Plains of Texas over three harvest seasons. Observations on harvester performance, including time-in-motion, harvest loss, seed cotton composition, and turnout, were conducted at seven locations with multiple cultivars. In systems where sufficient support equipment was available, strippers had higher productivity (i.e. acres per hour) than pickers. In higher yielding cotton, pickers had a higher productivity rate than strippers. The picker harvest had higher harvest losses but also resulted in lower levels of foreign matter and, therefore, higher turnout. The results of this research have elsewhere been incorporated into an economic model comparing harvest systems under various yield conditions.
Deep Learning-based Secure Transmission for SWIPT System with Power-Splitting Scheme

Recently, simultaneous wireless information and power transfer (SWIPT) has emerged as a potential solution for improving energy efficiency. However, it is also unavoidable to be eavesdropped on by security threats. In this paper, we investigate the secure transmission of the SWIPT system where a user is equipped with power-splitting (PS) scheme while an eavesdropper (Eve) tries to listen to the private information of the transmitter-user communication link. Our objective is to optimize the system secrecy rate while satisfying the requirements on energy harvesting (EH) at the user and transmit power at the transmitter. For solving the optimization problem, we utilize the feasible point pursuit (FPP) technique and the successive convex approximation (SCA) iterative-based algorithm. Although this algorithm can effectively solve our non-convex problem, it is often quite complex and time-consuming due to the many mathematical transformations and the need for the number of iterations to be converged to the optimal solutions. Therefore, we also consider the deep learning (DL)-based approach which helps the optimization problem to achieve an efficient solution in terms of the computation time. The simulation results validate the proposed scheme significantly improve the system secrecy rate. Moreover, the DL scheme achieved similar performance to the optimization algorithm, but with low computation time.
End-to-End Training for Acoustic Scene Analysis with Distributed Sound-to-Light Conversion Devices

We propose an end-to-end acoustic scene analysis framework with distributed sound-to-light conversion devices called Blinkies. Blinkies transmit sound information as the intensity of an on-board light-emitting diode (LED). A video camera can then easily collect acoustic information by capturing the LED intensities from multiple Blinkies distributed over a large area. However, the transmitted signal is band-limited owing to a video camera's frame rate, typically 30 frames per second. We aim to optimize the sound-to-light conversion process for acoustic scene analysis under this bandwidth constraint. In light-signal propagation in air, signal degradation due to physical constraints such as light attenuation and noise will also occur. We model the physical constraints as differentiable physical layers, which enable us to train two deep neural networks (DNNs) for sound-to-light conversion and acoustic scene analysis in an end-to-end manner. Simulation experiments of acoustic scene analysis using a DCASE 2018 dataset show that the proposed framework can produce a higher accuracy than the previous framework with Blinkies. This result suggests the suitability of Blinkies for acoustic scene analysis.