Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Green and Social Finance on Firms, Markets , and the Economy

Firms with better access to green and social finance withstand the COVID-19 pandemic more successfully than other firms. This evidence is supported by data from 60 countries around the world, covering all the major economies. Green firms were able to issue new bonds to support corporate activities. Their stocks experienced less decline. At the macro level, green and social bonds issued in the first two quarters of 2020 led to better economic recovery in the third quarter of 2020. Although green and social finance is not intended to boost economic growth during normal times, it helps smooth economic growth and leads to lower growth volatility. We also find that country total carbon dioxide emissions decrease after the green bond inceptions. Overall, green and social finance has real beneficial effects during both the pandemic and normal times. bonds loans have become major financing instruments to combat environmental threats. (2020) show that bonds are beneficial to the issuing firms in terms of broadening investor base. Flammer (2020) reports real environmental benefits associated with green bond issuance. are also under pressure to change their traditional way of increasingly pressure major banks, including to curb to fossil fuel companies. and loans have gained since 2019. Therefore, may
Semantic Correspondence with Transformers - Supplementary Materials -

Network Architecture Details. Given resized input images Is, It ∈ R256×256×3, we conducted experiments using different feature backbone networks, including DeiT-B [22], DINO [2] and ResNet-101 [5]. For the ResNet-101multi in the paper, we use the best layer subset [15] of (0,8,20,21,26,28,29,30) for SPair-71k, and (2,17,21,22,25,26,28) for PF-PASCAL and PF-WILLOW. We resized the spatial resolution of extracted feature maps to 16× 16. The extracted features undergo l-2 normalization and the correlation maps are constructed using dot products. Contrary to original Transformer [23] with encoder-decoder architecture, CATs is an encoder-only architecture. Within our Transformer aggregator, as explained in the paper, we concatenate the embedded features with correlation maps. We feed the resized features into the projection networks to reduce the dimension from c to 128, where c is the channel dimension of the feature. We then feed the augmented correlation map into the transformer encoder, which we use 1 encoder layer and 6 heads in multi-head attention layers. We then use soft-argmax function [8] with temperature τ = 0.02 to infer a dense correspondence field.
RESIN: A Holistic Service for Dealing with Memory Leaks in Production Cloud Infrastructure

Memory leak is a notorious issue. Despite the extensive efforts, addressing memory leaks in large production cloud systems remains challenging. Existing solutions incur high overhead and/or suffer from high inaccuracies. This paper presents R ESIN , a solution designed to holistically address memory leaks in production cloud infrastructure. R ESIN takes a divide-and-conquer approach to tackle the challenges. It performs a low-overhead detection ﬁrst with a robust bucketization-based pivot scheme to identify suspicious leaking entities. It then takes live heap snapshots at appropriate time points in carefully sampled leak entities. R ESIN analyzes the collected snapshots for leak diagnosis. Finally, R ESIN automatically mitigates detected leaks. R ESIN has been running in production in Microsoft Azure for 3 years. It reports on average 24 leak tickets each month with high accuracy and low overhead, and provides effective diagnosis reports. Its results translate into a 41 × reduction of VM reboots caused by low memory.
Understanding the User Preferences in the Types of Video Censorship

Video on demand (VOD) platforms provide immersive, inspiring, and commercial-free binge watching experiences. Recently, the number of these platform users increased dramatically as users can enjoy various contents without physical and time constraints during COVID-19. However, such platforms do not provide sufficient video censorship services while there is a strong need. In this study, we investigated the users' desire for video censorship when choosing and watching movies on VOD platforms, and how video censorship can be applied to different types of scenes to increase the censoring effect without diminishing the enjoyment. We first conducted an online survey with 98 respondents to identify the types of discomfort while watching sexual, violent, or drug-related scenes. We then conducted an in-depth online interview with 18 participants to identify the effective video filtering types and regions for each of the three scenes. Based on the findings, we suggest implications for designing a censor application for videos that contain uncomfortable scenes.
Risk of hypertension and treatment on out-of-hospital cardiac arrest incidence

Abstract Hypertension (HTN) is a high risk factor for major cardiovascular adverse events. This study aimed to investigate the effect of HTN risk on out-of-hospital cardiac arrest (OHCA) incidence and determine whether the effect of HTN on OHCA incidence differs according to antihypertensive medication. This case-control study used the Korean Cardiac Arrest Resuscitation Consortium and Korean Community Health Survey (CHS). Cases were defined as emergency medical service-treated adult OHCA patients presumed to have a cardiac etiology from 2015 to 2017. Patients without information on HTN diagnosis were excluded from the study. The Korean CHS database's controls were matched at a 1:2 ratio with strata, including age, gender, and county of residence. Multivariable conditional logistic regression analysis was conducted to estimate HTN risk and antihypertensive treatment on OHCA incidence, A total of 2633 OHCA patients and 5266 community-based controls were enrolled in this study. Among them, 1176 (44.7%) patients and 2049 (38.9%) controls were diagnosed with HTN. HTN was associated with an increased risk of OHCA (adjusted odds ratio [AOR]: 1.19 [1.07–1.32]). On comparing HTN with or without the antihypertensive treatment group with the non-HTN-diagnosed group (as a reference), the HTN without treatment group had the highest AOR (95% confidence interval) (3.41 [2.74–4.24]). The AOR in the HTN treatment group was reduced to that in the non-HTN-diagnosed group (0.96 [0.86–1.08]). HTN increased OHCA risk, and the HTN without treatment group had the highest OHCA risk. Conversely, OHCA risk decreased to the non-HTN-diagnosed group level with HTN treatment.
DAP-SDD: Distribution-Aware Pseudo Labeling for Small Defect Detection

: Detecting defects, especially when they are small in the early manufacturing stages, is critical to achieving a high yield in industrial applications. While numerous modern deep learning models can improve detection performance, they become less effective in detecting small defects in practical applications due to the scarcity of labeled data and signiﬁcant class imbalance in multiple dimensions. In this work, we propose a distribution-aware pseudo labeling method (DAP-SDD) to detect small defects accurately while using limited labeled data effectively. Speciﬁcally, we apply bootstrapping on limited labeled data and then utilize the approximated label distribution to guide pseudo label propagation. Moreover, we propose to use the t-distribution conﬁdence interval for threshold setting to generate more pseudo labels with high conﬁdence. DAP-SDD also incorporates data augmentation to enhance the model’s performance and robustness. We conduct extensive experiments on various datasets to validate the proposed method. Our evaluation results show that, overall, our proposed method requires less than 10% of labeled data to achieve comparable results of using a fully-labeled (100%) dataset and outperforms the state-of-the-art methods. For a dataset of wafer images, our proposed model can achieve above 0.93 of AP (average precision) with only four labeled images (i.e., 2% of labeled data).
Muscle, the motor of movement: properties in function, experiment and modelling

The purpose of this paper is to review exemplary aspects of different views of skeletal muscle characteristics. A classical view of muscle characteristics plays a very important role in modelling of muscles and movement. However, it often also pervades concepts on which our understanding of muscle function is based. In this view length effects, velocity effects and effects of degrees of activation and recruitment are distinguished and, often implicitly, assumed to be independent effects. It will be illustrated that using the classical approach many valuable things may be learned about muscle function and adaptation. At the same time we should realize that such a classical approach is too limited for use in generating knowledge about properties of muscles during daily use. The use of scaling of force to estimate muscular properties during submaximal activity on the basis of properties during maximal activation is shown to be very inadequate. An alternative view is described and particular examples are provided of changes in length–force characteristics as a consequence of submaximal activation, previous length change, as well as the effect of short-term histories of these variables. In addition, effects of inhomogeneities of muscle in morphology as well as physiological properties are considered. It is concluded that length–velocity–force characteristics are not unique properties of a muscle, and that these characteristics are not only strongly inﬂuenced by actual effects of recruitment, ﬁring frequency, shortening performed and actual velocity of shortening but also by the short time history of these factors. Therefore, length, velocity and activation cannot be considered as independent determinants of muscle functioning. It is also shown that we are confronted with many indications of physiological individuality regarding these phenomena. (cid:211)
Aadhar Based Electronic Voting Machine

Voting is the opportunity given to the people it is one of the political process, and the system is created to work best with everyone’s participation. Electronic Voting Machines (EVM) are being used by Indian election commission to conduct elections for the past decade. In Aadhar based electronic voting machine voters have to register their fingerprint. Based on the features of fingerprint it gets matched with the person who will cast the vote. Finger print matching will be done using the Aadhar card data base. The efficiency of a fingerprint is based on biometric system and is relatively high in comparison to other biometric based authentication systems like iris recognition, face readers, retina scanning, voice recognitions or hand geometry. With the help of keypad matrix voter can select the candidate to whom they wish to cast their vote and a message is displayed in LCD confirming the vote casted.
Modeling and Simulation for Multi-Robot Allocation and Execution of Tasks with Temporal and Precedence Constraints

—This work leverages the power of simulation as a tool to analyze the robustness of schedules for robots in a multi-robot system, where the robots cooperate to accomplish a set of tasks. Data collected during simulations are used to compute well-known indexes that measure the risk of delay and failure in the robots’ schedules. We also build a Bayesian probabilistic model from which we can compute the probability of missing tasks’ deadlines. In this model, start times of tasks are treated as random variables; the relations imposed by precedence constraints deﬁne a graphical model that captures the dependencies between tasks’ random variables. We run ROS-Stage simulations for up to tens of robots and tasks on an already computed schedule to generate data to perform robustness analysis.
Unoccupied aerial video (UAV) surveys as alternatives to BRUV surveys for monitoring elasmobranch species in coastal waters

Effective conservation strategies are founded by baseline information on abundance and diversity estimates. Method choice can influence the success of baseline surveys as method performance is variable and needs to be selected based on habitat and taxa. Here, we assess the suitability of unoccupied aerial vehicle (UAV) surveys, specifically multi-rotor “drones”, and baited remote underwater video (BRUV) surveys in shallow-water habitats to quantify elasmobranch abundance and diversity in the Saudi Arabian central Red Sea. Our results show that the number of elasmobranchs h−1 observed using UAV surveys exceeded that of BRUV surveys by two orders of magnitude, indicating that the increased spatial coverage of UAV surveys is beneficial for long-term monitoring projects. BRUV surveys detected a greater number of species within reef habitats, whereas UAV surveys detected a greater number of species within sandflat habitats, indicating the value of multi-method approaches for regional biodiversity studies. Here, we provide the first insight into elasmobranchs associated with sandflat habitats in Saudi Arabia, emphasising the importance of these habitats to stingrays and the need for further information on elasmobranch habitat use to better inform management and conservation efforts in the face of rapid coastal developments across the Red Sea.
Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.
Sentiment analysis framework organization based on twitter corpus data

Since its inception in 2006, Twitter has gathered millions of users. They post daily tweets about news, events or conversations. These tweets express their opinion about the topic they are discussing. Twitter is a large database of content that can be semantically exploited to extract opinions and based on these opinions to classify the users. This paper presents the organization of a sentiment analysis framework based on Twitter corpus data, including crawling tweets and opinion mining of the tweets, making it easy for its users to create portfolios of trustful Twitter accounts.
Impact of balance retraining and electrotherapy modality on unilateral osteoarthritis of knee - Case report

Osteoarthritis of knee is a condition that leads to painful joints if left untreated. Its development in the early stages of life due to excessive weight bearing, sedentary life style and early articular cartilage destruction has made it one of the major disabling arthritic conditions. Reporting a case of elderly female with medial side knee pain since a month hampering her everyday activities, for which she visited the clinical setting. Physiotherapy rehabilitation included ultrasound therapy for the pain and a modified Otago exercises targeting strength, range of motion and balance. Improving these aspects lead to an overall improvement in the functioning of everyday activities, reduction in pain and an improved patient reported quality of life concluded from various outcome measures.
Analyzing the Seasonal Deformation of the Sichuan–Yunnan Region Using GNSS, GRACE, and Precipitation Data

The Global Navigation Satellite System (GNSS) time series non-constructive deformation shows significant seasonal variations, and the study of its periodic term components and possible physical mechanisms has important theoretical significance and application value for the accurate use of the data and more in-depth analysis. In this paper, wavelet transform (WT) is used to extract the seasonal terms of 24 GNSS continuous station time series, Gravity Recovery and Climate Experiment (GRACE) displacement time series and precipitation data in the Sichuan–Yunnan area, and the three data sets are compared and analyzed in terms of amplitude, phase and cross-correlation coefficient (CC), and the results show that the seasonal deformation in the area is strongly related to the precipitation variation. GNSS and GRACE have good consistency in the vertical component, and the seasonal variation is mainly related to the hydrological load; the difference in the horizontal component is obvious, and the amplitude of the seasonal term of GNSS is larger than that of GRACE, indicating that the resolution of GRACE in the horizontal component is lower than that of the vertical component, and the overall estimation accuracy is lower than that of GNSS. There are significant seasonal terms of annual and semi-annual cycles for the three GNSS components, and the vertical component mainly shows the seasonal deformation of the annual cycle with the strongest seasonality; the horizontal component mainly shows the deformation of the semi-annual cycle, and the seasonality of the N component is stronger than that of the E component, and they are negatively correlated with a coefficient of −0.90.
Constructing Labor Market Transitions Recall Weights in Retrospective Data: An Application to Egypt and Jordan

To be able to redress retrospective panels into random samples and correct for any recall and/or design bias the data might suffer from, this paper builds on the methodology proposed by Langot and Yassin (2015) and extends it to correct the data on the individual transaction level (i.e. micro level). It creates user-friendly weights that can be readily used by researchers relying on retrospective panels extracted from the Egypt and Jordan Labor Market Panel Surveys(ELMPS and JLMPS respectively). The technique suggested shows that it is sufficient to have population moments - stocks and/or transitions (for at least one point in time) to correct overor under-reporting biases in the retrospective data. The paper proposes two types of microdata weights: (1) naive proportional weights and (2) differentiated predicted weights. Both transaction-level weights i.e. for each transition at a certain point in time, as well as panel weights i.e. for an entire job or non-employment spell, are built. In order to highlight the importance of these weights, the paper also offers an application using these weights. The determinants of labor market transitions in Egypt and Jordan are analyzed via a multinomial regression analysis with and without the weights. The impact of these weights on the regressions estimations and coefficients is therefore examined and shown significant among the different types of labor market transitions, especially separations.
Implementation of Object Detection Method for Intelligent Surveillance Systems at the Faculty of Engineering, Universitas Sebelas Maret (UNS) Surakarta

The number of positive Covid-19 cases in Indonesia continue to increase. This increase influenced by the behavior of Indonesian citizens in dealing with the pandemic, one of which is rarely wearing masks. In this study, we implemented an object detection method for intelligent surveillance systems (ISS) at the Faculty of Engineering, Universitas Sebelas Maret (UNS), Surakarta. By implementing face detection and mask detection, the surveillance system can recognize whether a person in a CCTV video frame is wearing a mask or not. In addition, deep metric learning and histogram of gradient (HOG) are applied to recognize faces of unmasked people in images. The test results show that the surveillance system can recognize the use of masks with 75%-87% accuracy rate. Furthermore, the accuracy rate for facial recognition on images ranges from 69% -100% for each person
Define, Measure, Analyze, Improve, Control (DMAIC) Methodology as a Roadmap in Quality Improvement

The term DMAIC, which stands for ‘‘define, measure, analyze, improve, and control,’’ represents a method for process analysis used in Lean Six Sigma. To understand DMAIC methodology, one must first know the history of Lean and Six Sigma. ‘‘Lean’’ production originates from the Toyota Production System introduced in Japan by engineer Taiichi Ohno in the 1950s through 1980s. Lean principles (aspects of which are shaped by Japanese culture and Japanese Zen Buddhism) are designed to permeate every aspect of an organization from company culture and philosophy down to leadership, technology, teamwork, and task standardization. Ultimately, the goal of Lean is to improve efficiency of a process and deliver the best product by eliminating waste. Six Sigma is a Western concept that originated from statistical modeling of manufacturing processes themselves. In the 1920s, Walter Shewhart created the basis for statistical process control with the concept that observed variation in a manufacturing process leads to deviation which results in an unideal product. Furthermore, changing a manufacturing process without proper analysis can worsen variation and result in more defects. Shewhart went on to work with physicist Edwards Deming to develop the ‘‘Plan Do Study Act’’ (PDSA) cycle in the 1930s. These concepts were applied by engineer Bill Smith in the 1980s to reduce process variation at Motorola and were coined, ‘‘Six Sigma. ’’ [2] ‘‘Lean Six Sigma’’ combines the principles of both Lean and Six Sigma with the overarching goal to reduce both waste and variation within a system using data and continuous quality improvement. To reduce variation in a process, Six Sigma utilizes statistical analysis through a 5-step approach called the DMAIC method.
Printed and Flexible ECG Electrodes Attached to the Steering Wheel for Continuous Health Monitoring during Driving

Continuous health monitoring in a vehicle enables the earlier detection of symptoms of cardiovascular diseases. In this work, we designed flexible and thin electrodes made of polyurethane for long-term electrocardiogram (ECG) monitoring while driving. We determined the time for reliable ECG recording to evaluate the effectiveness of the electrodes. We recorded data from 19 subjects under four scenarios: rest, city, highway, and rural. The recording time was five min for rest and 15 min for the other scenarios. The total recording (950 min) is publicly available under a CC BY-ND 4.0 license. We used the simultaneous truth and performance level estimation (STAPLE) algorithm to detect the position of R-waves. Then, we derived the RR intervals to compare the estimated heart rate with the ground truth, which we obtained from ECG electrodes on the chest. We calculated the signal-to-noise ratio (SNR) and averaged it for the different scenarios. Highway had the lowest SNR (−6.69 dB) and rural had the highest (−6.80 dB). The usable time of the steering wheel was 42.46% (city), 46.67% (highway), and 47.72% (rural). This indicates that steering-wheel-based ECG recording is feasible and delivers reliable recordings from about 45.62% of the driving time. In summary, the developed electrodes allow continuous in-vehicle heart rate monitoring, and our publicly available recordings provide the opportunity to apply more sophisticated data analytics.